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The Impact of COVID-19 Lockdowns on the Progression of Macular Degeneration

June 22, 2023

By Jessie Lei, Neurobiology, Physiology, & Behavior and minor in Human Development, ’24

Author’s Note: Every person and every facet of life was uniquely impacted by the effects of the COVID-19 lockdowns, yet how deep this influence runs can be unclear. Through the eyes of my grandfather, I witnessed first-hand just how detrimental the pandemic was on the progression of his chronic retinal eye disease. In an effort to learn more about current findings on the collateral damage from lockdowns on macular degeneration patients, this review seeks to synthesize relevant research on this growing topic. The hope is that readers come to recognize the real-time irreversible effects that are occurring in patients because of a temporary event and make connections with this information in any way to bring overall awareness.

ABSTRACT 

Purpose of Review: Neovascular age-related macular degeneration (nAMD) is an unrelenting disease that leads to complete loss of vision in the global elderly population. Though there have been recent advances in treatment, namely anti-VEGF (vascular endothelial growth factor) injections that slow the progression, its success is contingent on repeated administration. These injections slow the secretion of VEGF by retinal cells to prevent the growth of abnormal blood vessels in the retina. When COVID-19 lockdowns were enforced in 2020, nAMD patients were forced to stop their essential ophthalmic visits. The purpose of this review is to synthesize the results from 5 retrospective studies, which explored the long-term effects nAMD patients were subjected to due to the termination of their eye care during the COVID pandemic.

Main Findings: Severe declines in visual acuity were observed from 2020 to 2021, implying that the visual ability for nAMD patients was declining. This was observed even after treatment visits were resumed after lockdown. An additional set of parameters was used to measure the effects on the structural anatomy of the eye, but results were inconclusive due to contradicting data presented from different studies. For example, the damage inflicted on the macula from abnormal fluid accumulation was reversible after anti-VEGF injections resumed, while in other studies, it was determined that these structural parameters only continued to worsen over time. 

Keywords: macular degeneration, neovascular, injections, COVID-19, delay of care 

INTRODUCTION 

Globally, one of the primary causes of irreversible blindness in older generations is age-related macular degeneration (AMD) [1].  This progressive disease is characterized by a gradual loss of one’s central vision due to the degradation of the macula, an area located in the lining of the back of the eye. It is categorized into two clinical forms, dry and wet, with the latter being responsible for severe central vision loss in 90% of AMD cases [2]. Although the pathogenesis of neovascular, or wet, AMD (nAMD) is still unknown, it is characterized by the proliferation of abnormal blood vessels in the retina. These blood vessels are recruited due to the secretion of the vascular endothelial growth factor (VEGF) by retinal cells. Consequently, the atypical vessels will leak fluid and damage the dense population of color photoreceptors within the macula, causing distorted vision. To track the progression of nAMD, current research supports utilizing best-corrected visual acuity (BCVA) measurements and the analysis of optical coherence tomography (OCT) images [3]. BCVA helps assess the functional health of one’s vision and is measured by having patients read letters of decreasing size from a set distance. OCT scans are best simplified as cross-sectional images of the layers in the retina that help visualize a number of parameters indicative of the eye’s structural integrity. 

Treatment Options 

Despite nAMD’s prevalence in the aging population, only recently has an effective treatment been developed that helps stabilize vision by preventing damage from blood vessel growth [4].  Prior to this discovery, photodynamic, or light therapy was the main modality of care where light-activated medicine was stimulated by lasers to seal off abnormal blood vessels. But, it had many complications and only focused on reducing the likelihood of further vision loss. Currently, anti-VEGF drugs are the treatment of choice by ophthalmologists because the intravitreal injections into the eye have fewer adverse effects and outperform previous treatments by partially restoring vision [2]. The mechanism of action is through the inhibition of the VEGF pathway expression, which produces the main glycoprotein that causes leaky blood vessels [4]. However, the caveat is that the drug must be continuously administered every few weeks for a variable amount of time (usually 12 weeks) to take effect. This is the only treatment regimen that has seen significant improvements in patients’ control over nAMD progression [5]. 

COVID-19 and Macular Degeneration 

When the COVID-19 pandemic brought global lockdowns in March of 2020, this treatment plan grew difficult to follow because hospitals were placed under strict public health measures. nAMD patients experienced an unplanned discontinuation of injections since virtual ophthalmic visits were unfeasible for this type of care. Adherence to regular treatment visits dropped to as low as 46% during lockdowns [6],  and a recent systematic review by Im et al. illustrates how compared to other common retinal eye disorders, nAMD patients were the most susceptible to deterioration of the eye after treatment suspension [7]. Therefore, this literature review seeks to understand how the delay of injection care impacted long-term disease progression after COVID-19 lockdowns in 2020 for patients diagnosed with nAMD that were already receiving treatment. 

RESULTS 

Decline of Visual Acuity (VA) 

​​As one of the key determinants in nAMD patients’ overall functional capabilities, BCVA is heavily prioritized by researchers. It provides an accurate representation of how sharp their vision is and tracks changes that occur over time. The study conducted by Stattin et al. assessed how VA was compromised in 98 nAMD patients one year after a 9-week treatment deferral due to COVID-19 restrictions [8]. All patients had ophthalmic exams and injections every few weeks after restrictions were lifted where additional data on BCVA changes was collected. By using 95% confidence intervals with p-values of <0.0001 and linear regression models, the analysis revealed a loss of 4.1 ± 8.1 letters in 1 year after lockdown, which was compared against a pre-lockdown average of 2.9 ± 12.7 letters loss in 3 years [8]. Given the context of using small p-values in the analysis, the results yield large ranges but the significant difference in letter loss still illustrates a profound decline in visual acuity. In essence, the typical BCVA decline that would have been observed in nAMD patients over the course of 5 years was witnessed within 1 year due to unintended treatment breaks. Furthermore, because the delay in care expedites the rate of neovascular AMD in patients, anti-VEGF injections offer limited help in slowing that progression down. 

Along a similar line, Rego Lorca et al. also demonstrated that 12 months of routine visits were unable to mitigate the immediate VA consequences that stemmed from suspension of treatment in 242 nAMD patients from Spain [9]. Additionally, a statistical model was developed for the sample population to demonstrate what visual loss as part of natural disease progression looked like. This serves as a point of comparison to help identify what percentage of the results could be attributed to injection delay. It was found that an average of 7.2 letters were lost in patients’ BCVA over the duration of COVID-19 lockdowns and the 12 months of resumed follow-up care [9]. Based on the approximated natural rate of vision loss calculation, a 2.5 letter deficit can be credited, thus leaving an average of 4.7 letters lost over the aforementioned time period [9]. This means that capacity wise, nAMD patients experienced a functional decrease in visual clarity by 4.7 letters that can not be explained as part of inevitable nAMD progression. Another way that Rego Lorca et al. presented their findings is that pre-pandemic, 1.6 letters lost/year was expected; but post-pandemic, even with treatments restarted, an average of 3.1 letters lost/year was observed [9]. All of these figures point to a continuing theme that sudden drops in BCVA scores is the direct product of treatment discontinuation from the pandemic. Sevik et al. further supported this pattern in a paper that directly compared the outcomes of two different groups – those that delayed their appointments by an average of 3 months during the pandemic (group 1) and those that remained consistent (group 2) [10]. Each group consisted of 30 to 50 nAMD patients from Istanbul and were evaluated periodically for 6 months after lockdowns. It was discovered that with normal injections and follow-up post-lockdown, BCVA remained stable in group 1 while group 2 continued to suffer from statistically significant decreases that were not regained within 6 months [10]. With other possible confounding variables accounted for during the inclusion and exclusion of patient selection in this retrospective study, the only variable that can be attributed for the difference in functional outcomes is the postponement in treatment care. 

Variable Impacts on Structural Eye Health 

In contrast to the prior parametric analysis, there is not one single measurement that can appropriately represent the complexities of the eye as a whole and determine its structural health. Instead, the majority of researchers will examine a variety of anatomical characteristics that can be seen on OCT images. For Kim et al., they chose to study the maximum subretinal fluid (SRF) height, which conveys how much abnormal fluid has accumulated under the retina, as the main indicator of disease activity in 57 Korean nAMD patients [11]. They found that SRF height initially improved but by the 6-month check-up, had significantly worsened compared to baseline. At the end of the study, SRF height increased by 37 μm, which was about 18.9% higher than the recorded baseline value prior to COVID-19 lockdowns [11]. Because of the unique methodology employed where follow-ups occurred in 2-month intervals, researchers were able to catch nuanced fluctuations in progress results, like the temporary morphological improvements in SRF height following the return to treatment regimen. But that quickly deteriorated by the 6-month mark, emphasizing the rapid and unpredictable effects that delayed injections could have on disease activity. 

Interestingly, the article by Rego Lorca et al. completely contradicts previous conclusions from Kim et al. [11] by exhibiting that their sample population was able to anatomically recover to pre-pandemic state with 12 months of continuous treatment [9]. Active disease was defined as showing evidence of SRF and macular neovascularization (MNV), which is the proliferation of atypical blood vessels on the retina, on OCT images. Prior to the lockdown, SRF was present in 25.7% of the sample population, which increased to 28.2% during lockdowns, and decreased to 14.7% after 12 months of routine check-ups. Similarly, the percentage of MNV within nAMD participants before lockdown was 65.3%, increased to 79.6% during lockdowns, and decreased to 51% after [9]. Thus, the structural deterioration of the eye was demonstrated to be fully reversed and improved across two separate parameters in this specific paper. 

Rozon et al. brings in a plausible explanation for the previous discrepancies in their research which assesses the influence that delayed follow-ups had on 351 nAMD patients in Canada [12]. SRF and central foveal thickness (CFT) parameters on OCT images were used to define the structural integrity of the eye. CFT refers to how far off the thickness of the retinal center is from the normal range, because the thicker it is, the higher the likelihood of unwanted fluid accumulation within the retina. After 6 months of rigorous injections, the delay in follow-ups was linked to significant worsening of SRF parameters, while CFT measurements were normalized within the same time frame. SRF increased from 12.4% to 19.1% by 6 months; CFT levels began at 256.1 μm and fluctuated throughout the 6 months, but eventually ended up at 255.7 μm [12]. This provides further clarification on anatomical impacts because multiple different characteristics on OCT images were considered. It illustrated how some features may

improve over the 6-month period while others remain at a deficit, therefore depending on the parameter chosen by researchers, it will yield different data. 

DISCUSSION 

Analysis of Findings 

Amongst these reviewed articles, the combination of statistics highlight the persistent decline in visual loss that appears to be irreversible since pre-pandemic functional capabilities were not recovered in any nAMD patients [8-10]. Even after undergoing intense care following the interruption of visits, visual acuity continues to decline at alarming rates that current treatments cannot seem to dampen. On the other hand, no definite structural outcome could be confidently concluded because the data of each paper do not fully corroborate with one another [9, 11-12].  Some depict the possibility of restoring any anatomical damage sustained during the delay [10], while others express the worsening of structural parameters in the long-term [11-12]. Therefore, it is evident that additional research which explores a multitude of OCT features is needed before a conclusion on the reversibility of damage on the structural integrity of the eye can be made. However, a contradicting paradigm starts to form as there is a high possibility that the functional and structural impacts are complete opposites, which may imply a more complex issue. 

Strengths and Limitations 

Limitations of this review and its sources may help explain why this contradicting paradigm emerged from the findings. One of the biggest weaknesses is that the majority of the studies employed similar experimental designs that were retrospective in nature. Since it would be unethical to create a scenario in which treatment is purposefully delayed, this limitation is inevitable but should still be addressed because retrospective papers tend to supply a degree of selective bias during patient selection. Furthermore, in the article by Rozon et al.,  additional focus on risk factors and minimal acknowledgement of the COVID-19 pandemic’s contribution to the delayed follow-ups act as notable limitations to the relevancy of this source [12]. However, it still provided invaluable insight into why inconsistencies associated with using OCT measurements as a dependent variable occur that other researchers did not identify. Despite these disadvantages, there are still many notable strengths that this review has to offer. Researchers were able to eliminate possible confounding variables and establish a cause-effect relationship by including control groups [10], and statistical models of natural nAMD history [8-9]. Additionally, there is a wide diversity of patients from different countries represented, ensuring that the results of this review can be extrapolated and applied to nAMD patients on a global scale. 

Application of Findings 

As for implications, it is clear that nAMD injection treatments should be given priority in the event of another lockdown because consistency is necessary for positive effects. Patients and health professionals should maintain strict care regimens to ensure that further regression is not seen. It is recommended that research still be conducted in this topic, especially because there is potential to examine what effects from this lockdown period remain 5 or 10 years into the future and provide more evidence on anatomical impacts.

CONCLUSION 

When thoroughly considering all five core sources, there is a distinguishable response to the primary research question about the long-term deleterious effects on functional and structural eye health that were felt by nAMD patients after COVID-19 lockdowns caused unexpected gaps in treatment. Specific themes about the profound negative impacts had on visual acuity and the uncertainty regarding ramifications on the morphological health of the eye were uncovered. To limit the collateral effect of this pandemic on patients’ ophthalmic health, healthcare providers should proactively check in on nAMD patients. They should also place emphasis on performing additional research to better understand the long-term effects of lockdowns and if morphological damage sustained in the eye can be fully restored. 

The author declares no conflict of interest.

REFERENCES (FULL)

  1.   Learn about age-related macular degeneration. Centers for Disease Control and Prevention. Published November 23, 2020. Accessed March 14, 2023. https://www.cdc.gov/visionhealth/resources/features/macular-degeneration.html
  2. Flores R, Carneiro Â, Vieira M, Tenreiro S, Seabra MC. Age-Related Macular Degeneration: Pathophysiology, Management, and Future Perspectives. Ophthalmologica. 2021;244(6):495-511. doi:10.1159/000517520
  3. Ho AC, Albini TA, Brown DM, Boyer DS, Regillo CD, Heier JS. The Potential Importance of Detection of Neovascular Age-Related Macular Degeneration When Visual Acuity Is Relatively Good. JAMA Ophthalmol. 2017;135(3):268–273. doi:10.1001/jamaophthalmol.2016.5314
  4. Song D, Liu P, Shang K, Ma YB. Application and mechanism of anti-VEGF drugs in age-related macular degeneration. Front Bioeng Biotechnol. 2022;10. doi:10.3389/fbioe.2022.943915
  5. Augsburger M, Sarra G-M, Imesch P. Treat and extend versus pro re nata regimens of ranibizumab and aflibercept in neovascular age-related macular degeneration: A comparative study. Graefes Arch ClinExp Ophthalmol. 2019;257(9):1889-1895. doi:10.1007/s00417-019-04404-0
  6. Rothaus K, Kintzinger K, Heimes-Bussmann B, Faatz H, Lommatzsch AP. Impact of the COVID 19 Pandemic on Treatment of nAMD via a Portal-Based Collaboration. Klin Monbl Augenheilkd. 2022;10.1055/a-1806-2474. doi:10.1055/a-1806-2474
  7. Im JHB, Jin Y-P, Chow R, Dharia RS, Yan P. Delayed anti-VEGF injections during the COVID-19 pandemic and changes in visual acuity in patients with three common retinal diseases: A systematic review and meta-analysis. Surv Ophthalmol. 2022;67(6):1593-1602. doi:10.1016/j.survophthal.2022.08.002
  8. Stattin, M., Ahmed, D., Graf, A. et al. The Effect of Treatment Discontinuation During the COVID-19 Pandemic on Visual Acuity in Exudative Neovascular Age-Related Macular Degeneration: 1-Year Results. Ophthalmol Ther. 2021;10, 935–945. doi:10.1007/s40123-021-00381-y
  9. Rego-Lorca D, Valverde-Megías A, Fernández-Vigo JI, et al. Long-term consequences of covid-19 lockdown in neovascular AMD patients in Spain: Structural and functional outcomes after 1 year of standard follow-up and treatment. J Clin Med. 2022;11(17):5063. doi:10.3390/jcm11175063
  10. Sevik, M.O., Aykut, A., Özkan, G. et al. The effect of COVID-19 pandemic restrictions on neovascular AMD patients treated with treat-and-extend protocol. Int Ophthalmol. 2021;41, 2951–296. doi:10.1007/s10792-021-01854-6
  11. Kim J-G, Kim YC, Kang KT. Impact of delayed intravitreal anti-vascular endothelial growth factor (VEGF) therapy due to the coronavirus disease pandemic on the prognosis of patients with neovascular age-related macular degeneration. J Clin Med. 2022;11(9):2321. doi:10.3390/jcm1109232
  12. Rozon J, Hébert M, Laverdière C, et al.. Delayed Follow-Up in Patients with Neovascular Age-Related Macular Degeneration Treated Under Universal Health Coverage. Retina. 2022; 42 (9): 1693-1701. doi:10.1097/IAE.0000000000003512

Could Training the Nose Be the Solution to Strange Post COVID-19 Odors?

May 28, 2023

By Bethiel Dirar, Human Biology ’24

Author’s Note: I wrote this article as a New York Times-inspired piece in my UWP102B course, Writing in the Disciplines: Biological Sciences. Having chosen the topic of parosmia treatments as a writing focus for the class, I specifically discuss olfactory training in this article. In the midst of the pandemic, this condition caught my attention once I found out about it through social media. It had me wondering what it would be like to struggle to enjoy food post-COVID infection. I simply hope that readers learn something new from this article!

Ask someone who has had COVID-19 if they’ve had issues with their sense of smell, and they may very well say yes. According to the CDC, one of the most prevalent symptoms of the respiratory disease is loss of smell [1]. However, there is a lesser understood nasal problem unfolding due to COVID-19: parosmia. Parosmia, as described by the University of Utah, is a condition in which typically pleasant or at least neutral smelling foods become displeasing or repulsive to smell and taste [2].

As a result of this condition, the comforts and pleasures of having meals, snacks, and drinks disappear. Those who suffer from this condition have shared their experiences through TikTok. In one video that has amassed 9.1 million views, user @hannahbaked describes how parosmia has severely impacted her physical and mental health. She tearfully explains how water became disgusting to her, and discloses hair loss and a reliance on protein shakes as meal replacements. 

The good news, however, is that researchers have now identified a potential solution to this smelly situation that does not involve drugs or invasive procedures: this solution is olfactory training.
A new study shows that rehabilitation through olfactory training could allow patients with parosmia induced by COVID-19 to return to enjoying their food and drink. Olfactory training is a therapy in which pleasant scents are administered nasally [3].

Modified olfactory training was explored in a 2022 study as a possible treatment for COVID-19-induced parosmia. Aytug Altundag, MD and the other researchers of the study recruited 75 COVID-19 patients with parosmia from the Acibadem Taksim Hospital in Turkey and sorted them into two different groups. One group received modified olfactory training and another group served as a control and received no olfactory training [3]. Modified olfactory training differs from classical olfactory training (COT) in that it expands the number of scents used beyond COT’s four scents: rose, eucalyptus, lemon, and cloves [4 ]. These four scents were popularized in olfactory training use as they represent different categories of odor (floral, resinous, fruity, and spicy, respectively) [5].
For 36 weeks, the treatment group was exposed to a total of 12 scents twice a day that are far from foul. In each 12-week period, four scents were administered. For the first 12 weeks, they started with smelling eucalyptus, clove, lemon, and rose. During the next 12 weeks, the next set of scents were administered: menthol, thyme, tangerine, and jasmine. To round it off for the last 12 weeks, they smelled green tea, bergamot, rosemary, and gardenia scents. Throughout the study, the subjects would smell a scent for 10 seconds, then wait 10 seconds before smelling the next scent. The subjects completed the five minute training sessions around breakfast time and bedtime. [3]. 

To evaluate the results of the study, the researchers implemented a method known as the Sniffin’ Sticks test. This test combines an odor threshold test, odor discrimination test, and odor identification test, to form a TDI (threshold, discrimination, identification) score. According to the test, the higher the score is, the more normal the state of an individual’s olfactory perception is. A composite score between 30.3 and the maximum score of 48 indicates normal olfactory function while scores below 30.3 point to olfactory dysfunction [3].

The results of this research are promising. By the ninth month of the study, a statistically significant difference in average TDI scores had been found between the group that received modified olfactory training and the control group (27.9 versus 14) [3]. This has led the researchers to believe that with prolonged periods of the therapy, olfactory training could soon become a proven treatment for COVID-19-induced parosmia. 

With this conclusion, there is greater hope now for those living with this smell distortion. Fifth Sense, a UK charity focusing on smell and taste disorders, has spotlighted stories emphasizing the need for effective treatments for parosmia. One member of the Fifth Sense community and sufferer of parosmia, 24-year-old Abbie, discussed the struggles of dealing with displeasing odors. “I ended up losing over a stone in weight very quickly because I was skipping meals, as trying to find food that I could eat became increasingly challenging,” she recounted to Fifth Sense [6].

If olfactory training becomes an effective treatment option, eating and drinking might no longer be a battle for those with parosmia. Countless people suffering from the condition will finally experience an improvement in their quality of life so desperately needed, especially with COVID becoming endemic.

REFERENCES:

  1. Centers for Disease Control and Prevention. Symptoms of COVID-19. Accessed November 20, 2022. Available from: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html
  2. University of Utah Office of Public Affairs. Parosmia after COVID-19: What Is It and How Long Will It Last? Accessed November 20, 2022. Available from: https://healthcare.utah.edu/healthfeed/postings/2021/09/parosmia.php
  3. Altundag Aytug, Yilmaz Eren, Caner Kesimli, Mustafa. 2022. Modified Olfactory Training Is an Effective Treatment Method for COVID-19 Induced Parosmia. The Laryngoscope [Internet]. 132(7):1433-1438. doi:10.1002/lary.30101
  4. Yaylacı Atılay, Azak Emel, Önal Alperen, Ruhi Aktürk Doğukaan, and Karadenizli Aynur. 2022. Effects of classical olfactory training in patients with COVID-19-related persistent loss of smell. EUFOS [Internet]. 280(2): 757–763. doi:10.1007/s00405-022-07570-w
  5. AbScent. Rose, lemon, clove and eucalyptus. Accessed February 5, 2023. Available from: https://abscent.org/insights-blog/blog/rose-lemon-clove-and-eucalyptus 
  6. Fifth Sense. Abbie’s Story: Parosmia Following COVID-19 and Tips to Manage It. Accessed November 23, 2022. Available from: https://www.fifthsense.org.uk/stories/abbies-story-covid-19-induced-parosmia-and-tips-to-manage-it/

Inference on the Dynamics of COVID-19 in Kerala, India

October 25, 2022

By Darya Petrov

Author’s Note:  I worked on this research project at the peak of the COVID-19 pandemic, while we were fully remote and on lockdown. I chose this topic because it was extremely relevant given the circumstances. I hope this report conveys the importance and value of the union of statistical modeling and public health in pandemic response efforts.

 

1 Introduction:

The coronavirus pandemic has been ongoing since the beginning of 2020. As of April 11, 2022, there have been 497 million confirmed cases of which 6 million resulted in death worldwide [1]; the recent World Health Organization (WHO) report on the pandemic indicates that this massive number itself is probably a significant underestimate [2]. Our understanding of the evolution of the coronavirus has dictated many large-scale social distancing measures including mask mandates, lock-downs, and travel restrictions that have had major impacts on society, economy, and public health. Conventional epidemiological models of infectious diseases, such as the SIR (Susceptible, Infected, Recovered) model which measures the spread of a disease through the change of the population in each of the three compartments listed, do not readily apply to COVID-19 dynamics; they do not utilize information on the count of asymptomatic individuals, an unobservable variable. It is well-known that asymptomatic but infected individuals have been the major spreaders of the COVID-19 pandemic, and therefore, it is imperative to obtain an estimate of such individuals in the population from available data. India is an excellent candidate for the analysis of disease dynamics because at one point during the pandemic, it had the worst COVID-19 crisis in the world. On May 6th, 2021, India had the largest worldwide single-day spike of over 400,000 new infections with shortages of hospital beds and ventilators [3]. We analyze publically available data from the state of Kerala in India to gain a better understanding of COVID-19 dynamics using a previously proposed methodology. The model is expressed through a system of difference equations, and incorporates information on social distancing measures and diagnostic testing rates to characterize the dynamics of the pandemic. The model’s key feature is its ability to estimate the unobservable count of asymptomatic individuals mentioned previously. This methodology has already been used to analyze COVID-19 dynamics in the United States [4]. 

2 Methods:

2.1 The Model 

A graphical representation of the disease propagation model is depicted in Figure 1. The color of each box represents the observability of the compartment: red indicates unobserved, blue indicates observed, and purple indicates partially observed, meaning the compartments are observed together. Suppose at time t, Ct , Dt , Tt respectively represent the number of confirmed COVID-19 cases, number of deaths due to the disease, and number of tests performed up to time t. Let At denote the number of asymptomatic individuals, of which Ht denotes the number of persons hospitalized and Qt denotes those quarantined due to COVID-19 at time t. St denotes the number of susceptible individuals in the population at time t. Rt denotes the number of recovered individuals, of which RtQ, RtH, RtA respectively are those recovered from quarantine, hospitalization, or from being asymptomatic but never quarantined or hospitalized. 

Figure 1: Graphical representation of the disease propagation model

2.2 Data Preprocessing

We consider the dynamics of the spread of COVID-19 in Kerala, India for a time window of April 1, 2020 to December 31, 2020. The proposed model is based on the observed state-wise daily counts of confirmed infections, deaths, hospitalizations and reported recoveries. Daily counts of the confirmed COVID-19 cases, recoveries, deaths, tests, and hospitalizations were obtained from an application programming interface (API) [5]. Daily hospitalizations were obtained from a state dashboard owned by the Kerala government [6]. Unfortunately, obtaining hospitalization data for India was arduous. We extracted the data manually for each district in Kerala for each day, and then combined the data into one data frame. The social mobility data was obtained from Google [7]. The data was preprocessed and cleaned, removing any irregularities present such as abnormally large counts for a given day that are clearly due to a mistake in the data reporting. Getting rid of such outliers is crucial because they can have a significant impact on the model and distort the actual relationships and patterns in the data. These irregularities or any missing observations were replaced using K-Nearest Neighbor (KNN) imputation with k=6 nearest neighbors, i.e. a missing observation for a given day was replaced by the average of the six closest observations by date. Inherent noise present in the daily counts, visually represented by frequent vertical spikes on a graph,   were removed by pre-smoothing the trajectories using the Locally Estimated Weighted Scatterplot Smoothing (LOWESS) method with bandwidth 1/16. This fits smooth curves to the data points to capture general patterns in the data, with the bandwidth indicating how much of the data to use when smoothing at each point. The smaller the bandwidth, the rougher the smoothed curve will be, i.e. the graph will have more bumps.

3 Results:

3.1 Case Study for Kerala, India

We present our analysis based on the data from Kerala for the time window between April 1st, 2020 to December 31st, 2020. Figure 2 plots the daily number of people in hospitals. Note that no vaccine was available during this time period, and so any immunity from the virus could only be obtained through exposure. An important assumption our model makes is that once somebody is infected (either showing symptoms or otherwise), the person remains immune to reinfection. The black curve plots the observed values, and the red curve plots the fitted values from the model. It can be seen that the fitted values obtained from the model closely follow the observed values. This validates our proposed model and the estimation procedure. From the data and the fit, it is visible that a wave started early July 2020. The number of hospitalizations peaked early October 2020 and started decreasing afterwards.

Figure 2: Daily hospitalizations, fitted by the model (red ⎯) and observed (black ⎯)

 

3.2 Estimation of Latent Compartment

The estimated number of infected asymptomatic individuals (Figure 3) shows a similar pattern with a high point around the beginning of October, and dipping afterwards. There is also a local peak around the end of August. Estimation of this latent, i.e. unobservable, compartment across time is a key feature of our proposed methodology, since this information cannot naturally be obtained from the conventional epi-models. 

Figure 3: Estimated number of asymptomatic individuals

 

3.3 Analogue of Basic Reproduction Rate

One large wave, i.e. a surge in new infections, can be observed from the plot of the proposed analogue of the basic reproduction rate (Figure 4). It measures the transmissibility of COVID-19 at time t and is influenced by spread mitigation efforts. The basic reproduction rate less than 1 indicates a decrease, while greater than 1 indicates a growth in the number of asymptomatic-infected individuals. Its estimate was mostly larger than 1 in the sub-interval, namely from the end of April to the beginning of October, indicating the singular large wave.

Figure 4: The basic reproduction rate ( R0 ) is the rate of growth of asymptomatic-infected individuals.  

 

The plot of the number of daily new and daily reported infections (Figure 5) shows a maximum near October. The black curve plots Ct, the number of observed confirmed cases at time t+1. The red curve plots NI(t), the daily number of new infections at time t, which is calculated as the estimated number of susceptible individuals that become asymptomatic-infected at time t.

Figure 5: Daily new infections observed by the change in confirmed cases, Ct (black ⎯), versus the estimated number of new infections, NI(red ⎯) . 

 

3.4 Transmission Rates

Figure 6 shows the plots of the crude infection rate (CIR) and net infection rate (NIR) . The red curve represents the CIR(t), the ratio of the daily change in the number of confirmed cases relative to the number of confirmed cases at time t+1. The CIR under-represents the infection rate, so the model estimates the infection rate with the NIR. This explains why the black curve represented by NIR(t) tends to be larger than the CIR(t) curve. The NIR(t) is the ratio of the daily change in the number of  asymptomatic-infected individuals relative to the number of asymptomatic-infected individuals at time t. 

Figure 6:  The observed crude infection rate , CIR (red ⎯),  and the estimated net infection rate, NIR (black ⎯)

 

The observed doubling rate obtained from the observed number of confirmed cases (Ct) and its estimate from the cumulative number of new infections (CNI) appear to be very close after mid July (see Figure 7). This implies reporting kept pace with the spread of the disease starting mid July. The doubling rate obtained from Ct is represented as the black curve, and the estimate obtained from CNI is represented by the red curve. It is the inverse of the doubling time at time t. The doubling time is the amount of time it takes to double the amount of infected individuals at time t. The higher the doubling rate, the faster the spread of the infection. The doubling rate reflects the effect of spread mitigation efforts, including social distancing campaigns, improved hygiene, and case tracking. 

Figure 7: Doubling rate, Ct (black ⎯) and CNI(red ⎯) 

 

Figure 8 shows the crude and net case fatality rates, CFR and NFR respectively. The black curve represents the CFR(t) and the red curve represents the NFR(t). CFR(t) is given by the percent of total deaths to the total confirmed cases up to time t. NFR(t) is given by the percent of total deaths to the cumulative number of infections up to time t estimated by the model. It is important to note that the formulas of the CFR and NFR are the same, except the denominator of the NFR is the CNI(t) while the denominator of the CFR is Ct . The observed number of confirmed cases Ct will be strictly less than or equal to the estimated cumulative number of new infections CNI(t), and likely much less, therefore the CFR is naturally much larger than the NFR. 

Figure 8: The crude case fatality rate, CFR (black ⎯), and the net case fatality rate, NFR (red ⎯). 

 

3.5 Testing and Hospitalization

The daily number of tests and its effect in quarantining asymptomatic but infected people can be judged from Figures 9 and 10. Figure 9 plots the number of tests performed per hospitalization. Tt represents the number of COVID-19 tests at time t+1. Ht represents the number of hospitalized persons for COVID-19 up to time t. This measure is an approximation of the contact tracing intensity. Figure 10 plots the RCCF, the relative change in confirmed fraction. The RCCF measures the change in the rate of currently asymptotic-infected individuals with COVID-19 that are detected through testing and quarantined relative to the rate of detection of currently infected individuals. This measure shows the dynamics of the effectiveness of detecting and isolating asymptomatic-infected individuals from the population through testing. Empirical comparison of Figures 2 and 9 reveals that although the number of daily tests could keep pace with daily number of hospitalized patients up to early July, the growing number of hospitalized people from July to October ultimately outpaced the number of daily tests. The daily number of hospitalizations beginning to decrease in early October was accompanied by the daily testing beginning to increase. 

Figure 9: Tt / Ht

Figure 10:  The relative change in confirmed fraction, RCCF 

 

Discussion:

In comparison to conventional SIR models which model disease dynamics from the number of susceptible, infected, and recovered individuals, the proposed model also incorporates information about testing and quarantine. It is important to note the following assumptions the proposed model is based on:

  1. Only an asymptomatic individual who is not either in quarantine or in hospital can transmit the disease to a susceptible individual.
  2. People who recover from the disease are immune from subsequent infection. 
  3. False positive rate for the test is negligible, so that if somebody is confirmed to be positive, then he/she is assumed to be infected. 
  4. Anybody who shows significant symptoms, whether being in quarantine or not, is immediately hospitalized, and is tested to be positive. 
  5. There is no effective treatment regime for the asymptomatic individuals, and so they recover or turn symptomatic at the same rate regardless of whether they are tested positive (and hence quarantined) or not. 

These assumptions are quite general, however, the model could be modified if necessary to adjust for assumptions not met. For example, assumption 2 and 3 can be generalized by adding a fraction of recovered individuals to the susceptible population. Additionally, violations of some assumptions, such as assumption 1, are unlikely to have a significant impact on the disease dynamics. However, the current model does not incorporate impact of vaccination on the disease dynamics, which renders it applicable to the data being studied. Clearly, analyzing more recent data would require using a more enhanced version of the model that includes vaccination effects.

Smoothing was a crucial technique in this model because counts are rough. It was used in the data preprocessing to reduce the impact of anomalies, such as abnormally high counts likely due to incorrect data reporting. Additionally, it was used in the estimation of time varying parameters, which is intrinsic because of the locally weighted time window. 

The goal of this study was to analyze how well the proposed model, which has already been used to model data from the United States, models disease dynamics of COVID-19 in Kerala, India. The performance of the model is validated by its ability to capture the large wave Kerala experienced between August and December of 2020, which is visible in the number of hospitalizations, estimated number of asymptomatic individuals, and the basic reproduction rate. The number of new infections estimated by the model appears reliable compared to the reported number of new confirmed cases. This reported number is an underestimate of the number of new infections since not all infections are reported. For example, an individual may be infected with COVID-19, but not reported as a confirmed case of COVID-19 if they are asymptomatic and did not get tested. This underestimate of the number of new infections worsens as the number of asymptomatic cases increases. The plots of testing per hospitalization and RCCF give us an idea of contact tracing intensity in Kerala, and how well it was coping with the pandemic. This model can help evaluate the effectiveness of measures used in hopes of reducing disease spread, such as social distancing, curfews, and mask mandate. The proper response to a pandemic is a controversial topic, and this model can help make informed actions in future pandemics. Just as this model was originally used on data from the United States and applied here to data from India, this model can also be applied to other regions, as long as the necessary data is available, preprocessed, and cleaned. 

It is important to note that variants can have a strong influence on disease dynamics. For example, the omicron variant of the original SARS-CoV-2 strain is more infectious and spreads faster [8]. Additionally, the current state of this model is most applicable to a pandemic in which a vaccine has not yet been developed, which can be a big chunk of a pandemic since vaccine development takes time. The first cases of COVID-19 were detected in December of 2019, and a vaccine wasn’t approved until a year later in December of 2020 [9, 10], but even then the vaccine supply was limited and was distributed in phases, prioritizing those most at risk [11]. Because vaccinations impact disease dynamics, a potential next step is incorporating data about vaccinations into the proposed model [12]. 

 

Acknowledgements:

Thank you to Sruthi Rayasam for scraping the data from online, Satarupa Bhattacharjee for helping with the R code, and Dr. Debashis Paul for supervising this project.

 

References:

  1. World Health Organization. WHO coronavirus (COVID-19) dashboard. Accessed April 11, 2022. Available from: https://covid19.who.int/
  2. World Health Organization. 2022. Global excess deaths associated with covid-19, January 2020 – December 2021. World Health Organization. Accessed May 13, 2022. Available from: https://www.who.int/data/stories/global-excess-deaths-associated-with-covid-19-january-2020-december-2021 
  3. Runwal, P. 2021. How India’s covid-19 crisis became the worst in the world. Science News. Accessed April 16, 2022. Available from: https://www.sciencenews.org/article/coronavirus-covid-india-crisis-social-distancing-masks-variant
  4. Bhattacharjee, S., Liao, S., Paul, D. Chaudhuri, S. 2022. Inference on the dynamics of COVID-19 in the United States. Sci Rep. 12(1): 2253. https://doi.org/10.1038/s41598-021-04494-z
  5. Babu, J., Shukla, A., & Bharath. Covid19-India API. Accessed April 21, 2021. Available from: https://data.covid19india.org/ 
  6. C-DIT. Kerala : COVID-19 Battle. GoK Dashboard. Accessed June 4, 2021. Available from: https://dashboard.kerala.gov.in/covid/index.php 
  7. Google. Covid-19 Community Mobility Reports. Accessed August 9, 2021. Available from: https://www.google.com/covid19/mobility/
  8. Centers for Disease Control and Prevention. 2022. What you need to know about variants. COVID-19. Accessed April 16, 2022. Available from: https://www.cdc.gov/coronavirus/2019-ncov/variants/about-variants.html 
  9. U.S. Food and Drug Administration.. FDA Approves First COVID-19 Vaccine. Accessed June 16, 2022. Available from: https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine 
  10. Centers for Disease Control and Prevention. 2022. CDC Museum Covid-19 Timeline. Accessed June 16, 2022. Available from: https://www.cdc.gov/museum/timeline/covid19.html 
  11. California Department of Public Health. Covid-19 vaccine prioritization recommendations for moving through vaccine phases and tiers. Accessed June 16, 2022. Available from: https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/Vaccine-Prioritization.aspx?TSPD_101_R0=087ed344cfab2000b4d56b612d8caafb7743db82d9ff978793d54888b3c213b891734b5b4e6868b0087aa92767143000f5f590c2e053b6fe510cb7cedbfa962f4b18412db1954061d6f01003ae226244940e863d42ef53db7a5f6abb6d403af1 
  12. Chen, X., Huang, H., Ju, J. Sun, R., Zhang, J. 2022. Impact of vaccination on the COVID-19 pandemic in U.S. states. Sci Rep. 12(1): 1554. https://doi.org/10.1038/s41598-022-05498-z

mRNA Vaccines: A Safe and Effective Technology

March 18, 2022

By Elexia Butler, Human Biology, ’23

Author’s Note: This article was written to reveal how the COVID-19 vaccines are produced and how they are a safe technology used to help reduce the number of sick individuals. Throughout the article, I will discuss the safety and efficacy of mRNA vaccines as well as the limitations that scientists overcome. I chose this topic because mRNA vaccines are a “new” technology that many of us don’t understand and has led to a larger social debate. The controversy surrounding mRNA vaccines stems from people’s questions regarding the vaccines’ safety and necessity. After reading this article, I hope the reader is able to take away the fact that the mRNA vaccines are safe and effective. 

 

Abstract:

As the world begins to settle after the past year and a half of operating with the COVID-19 pandemic, we look to mRNA vaccines to help return to a sense of normalcy. With both Moderna and Pfizer leading the market of mRNA vaccines since April 2021, we have seen a large decline in cases [27]. However, many people across the country are still skeptical of this “new” mRNA vaccine technology [8] and remain hesitant about getting the vaccine. Additionally, the COVID-19 vaccine controversy has left many individuals wondering if the vaccine is truly a safe way to fight the spread of COVID-19 or not. Currently, 54.7-59% of Americans have been fully vaccinated, but based on a PBS poll 24% have chosen to not receive any dose of the vaccine [40-41]. The goal of this article is to demonstrate the safety of mRNA vaccines, their development, limitations, and potential for treating future diseases. 

Introduction:

Messenger RNA (mRNA) vaccines are not a new technology, in fact they have been researched for years. mRNA is a small genetic molecule that encodes specific proteins [33]. The discovery of mRNA in 1961 sparked an entire field of research related to gene regulation [1-5]. 

Traditional vaccines work by introducing an antigen (a foreign substance that is recognized by the immune system) to elicit an immune response and cause the body to produce antibodies against that antigen [13]. For nucleic acid vaccines (DNA and RNA vaccines), rather than directly injecting the antigen, the instructions for producing the antigen are introduced into the cell [14]. The cell can then use these instructions to “make a protein—or even just a piece of a protein—that triggers an immune response inside our bodies” [16]. In the case of COVID-19, Pfizer and Moderna mRNA vaccines encode the instructions to make a viral spike protein from SARS-COV-2 (the virus that causes COVID-19). The spike protein won’t cause sickness on its own, it trains the immune system to defend against the real SARS-COV-2 virus [38]. While research has been conducted on both DNA and RNA based nucleic acid vaccines, it has been shown that RNA vaccines are able to elicit a stronger immune response and are likely safer [15]. The technology of mRNA vaccines became increasingly promising as scientists used the speed of production of the technology to develop a safe and effective mRNA vaccine to their advantage [50-51]. One of the many reasons the Moderna and Pfizer vaccines work is the way they modify the stability of the mRNA and establish a method for efficient delivery, allowing for a strong immune response when administered [17, 45-47]. Though hesitancy remains surrounding the COVID-19 vaccine, the Moderna and Pfizer vaccines are both effective and have significantly reduced the infection rate of COVID-19 [27]. This hesitancy has been fueled by reports of conspiracies as well as possible health effects, which all have been proven false and will be discussed later in larger detail.

Figure 1. This diagram demonstrates how the SARS-COV-19 vaccine was produced and how it elicits an immune response. Through the mRNA being introduced into the body, the cells gain instructions on how to produce the spike protein and forms antibodies. 

Proof of Principle:

The COVID-19 mRNA vaccine has brought hope to the medical field because they are effective and can continue to develop. With this technological advancement, it is important to maintain a certain standard of success to build confidence in the vaccines.  The Food and Drug Administration (FDA) has set a standard for success of “at least 50%” efficacy, or the prevention of the spread of infection due to the vaccine [18, 53]. The Moderna and Pfizer mRNA vaccine clinical trials exceeded this standard, granting them Emergency Use Authorization (EUA). The application of the mRNA vaccine demonstrated an effectiveness of “90% for full immunization and 80% for partial immunization” [10]. A study, conducted by the CDC in March of 2021, was used to assess the real world application and effectiveness of the vaccine in a potentially infectious setting. As reported by the CDC, the group of vaccinated first responders and essential health care workers were prevented from infection. This study showed that the Moderna and Pfizer vaccines are highly effective in the real world.

Along with this, there have been observational studies that show the vaccines have reduced the amount of transmission and need for hospitalization [9, 23]. Through a recent study by the Center of Disease Control and Prevention (CDC), it was concluded that the “SARS-CoV-2 vaccines significantly reduce the risk for COVID-19–associated hospitalization in older adults and, in turn, might lead to commensurate reductions in post-COVID conditions and deaths.” [9] 

The vaccines have created an opportunity for the world to return to a somewhat normal reality through the concept of herd immunity. Herd immunity is the idea that a “large portion of a community becomes immune to a disease … As a result, the whole community becomes protected—not just those who are immune” [30]. In other words, as more people get vaccinated, the transmissibility of SARS-COV-2 will be significantly reduced. Proof of this comes from the CDC as they discovered that in 1000 working days, infections among unvaccinated individuals (1.38 infections) were significantly higher than both fully vaccinated (0.04 infections) and partially vaccinated individuals (0.19 infections) [10]. To put it simply, the COVID-19 vaccine works. The vaccine has protected individuals throughout the past 6 months, and now that it is readily available we are seeing a massive decline in cases [27]. 

Figure 2. This diagram demonstrates how herd immunity functions in our society. As shown, the more people that are vaccinated, they are less likely to become infected. 

Versatility:

         Researchers have started studying possible applications of mRNA vaccines to diseases such as AIDS and other incurable diseases. It has been difficult to make regular vaccines due to the fact that there are so many mutations and strains, however the mRNA vaccine has been able to sidestep that by teaching the body to make antibodies and proteins. Before the modern advancements of mRNA vaccines that the COVID-19 vaccine brought forward, there was no efficient and effective way to deliver mRNA into the cell [31-32]. According to Mu et al. until these recent developments, there were major bottlenecks that hindered such research because mRNA is very unstable and can easily denature [31-32]. With new research, Moderna has begun trials on various mRNA vaccines, including one for HIV and AIDS [29]. 

Along with HIV, there has been research into using mRNA vaccines to treat cancer. Two types of vaccines have been proposed for cancer: preventative vaccines and treatment vaccines. Preventative vaccines attempt to protect the body from viruses that can potentially lead to cancer.  HPV and Hepatitis B are two infections where vaccines have been made in an effort to prevent these infections and stop the development of cancer [43]. In this method, the body “mount[s] an attack against cancer cells … Instead of preventing disease, they are meant to get the immune system to attack a disease that already exists” [43]. Treatment based vaccines, meanwhile, are more personalized to an individual’s genome [49]. To implement this, there must be an understanding of the individual’s specific cancer genome [49]. Scientists identify the mutated genes that are responsible for the tumor growth in the individual. They then encode and inject the mutant mRNA into the body, providing the individual’s immune system with instructions to create the mutated protein. This mRNA enables the body to identify and attack the cells with markers for the mutated gene, which are not present in non-cancerous cells. Moderna implemented a similar approach and found that the method reduced tumor size in 30% of human participants when combined with checkpoint inhibitors, a drug which activates proteins to regulate the immune system when attacking cancer cells  [49, 54]. Through the use of an mRNA vaccine, this allows the body to fight the tumors on its own rather than using harsh chemical mixtures, like chemotherapy, to stop the growth of the cancer. 

In regards to the multitude of other infectious diseases, much of the research around mRNA vaccines has already started and will continue. With the full approval of the Pfizer vaccine and current EUA of Moderna, the opportunity for future mRNA vaccines seems promising. As noted in previous research for mRNA vaccines targeting Zika and other diseases, there was a lack of knowledge regarding mRNA vaccines that impacted the ability to create a successful vaccine [19]. Due to the recent advancements, the opportunity to revisit these vaccines is possible.  

Limitations:

Several major hurdles continue to limit the broad application of mRNA vaccines which include cost, safety concerns, and instability of mRNA affecting storage.

Cost: 

Due to the severity of COVID-19, funding was readily available in an effort to mitigate the spread of this deadly virus. The federal government was one of the major financial suppliers as they “pledged to give nearly $500 million to Moderna alone for its COVID-19 vaccine”, and this was able to support one of the first COVID-19 vaccines brought forward [24]. Dr. Nathaniel Wang, chief executive of Replicate Bioscience developing RNA-based treatments for cancer, said “it’s pretty hard to talk people into taking bets on this type of technology for vaccines in infectious diseases” because it is seen as “new” technology [19]. This has been gravely apparent regarding RNA vaccines for diseases like Zika [19]. These financial constraints delayed progress and it made mRNA vaccines a nonviable strategy of treatment for Zika, COVID, and other diseases previously discussed. 

Safety: 

The safety concerns regarding the COVID-19 vaccine have been particularly contentious in the U.S. This fear is fueled by misinformation such as rumors of infertility caused by the vaccine and other false claims that have been reported in opinion pieces online. Many of the conspiracy theories and stories that damaged the image of the vaccine originated from social media[21]. A study polled that a majority of Americans believe there was “rushed approval for the COVID-19 vaccine without the assurances of safety and efficacy” causing people to believe that the vaccine bypassed all the regulatory steps [22]. The FDA defines that “for an EUA to be issued for a vaccine… FDA must determine that the known and potential benefits outweigh the known and potential risks of the vaccine” [39]. Through years of advanced research, the trials and production of the vaccine were able to run in parallel without compromising the safety of the vaccine [50]. While there are some valid concerns specific to the COVID-19 mRNA vaccines, including myocarditis, blood clots, and potential allergic reactions, the COVID-19 mRNA vaccines have been deemed as safe and effective by the CDC [26].

Side effects:

It is possible that individuals will experience certain side effects ranging from pain, swelling in the arm, nausea and fever, along with some more serious side effects, for example myocarditis and blood clots, reported by the CDC. It is important to note that if these less serious side effects even occur they are generally present for less than a week. A small price to pay for a vaccine that has been effective in preventing the spread of COVID-19 [23]. This was shown through mouse and hamster trials, as they noted that they had full immune system responses that protected against COVID-19, similar to that of humans [57]. In another study done with rats, they focused on the vaccine’s potential impact on pregnant rats to simulate that of a pregnant woman and found that there are potential side effects on that impact fetal development, female fertility, and early offspring development, but none were observed [58]. 

Through a variety of trials, scientists have determined that the body has been able to perform a timely immune response to the vaccine. A measurement of this has been the body’s reaction in the form of specific side effects [52]. Only a small number of cases include more serious reactions, such as anaphylaxis (2.5 per 1 million Moderna vaccines). Most cases will only have small reactions and no long-term side effects have been recorded [34, 35]. Though the majority of people only have minor reactions, these side effects show that the vaccine has gotten into the cell and the body has identified the viral mRNA [52]. 

Through the immense amount of data showing the vaccine’s efficacy, Pfizer has received FDA approval while Moderna has begun the FDA approval process [36, 37]. This milestone highlights the safety and efficacy of both mRNA vaccines. 

Storage: 

Due to the fact that both Moderna and Pfizer need lower temperatures for stability, they require the vaccines be kept below freezing around -20 to -80 degrees C for long term storage [25]. RNA needs to be stored at lower temperature as it will degrade due to alkaline hydrolysis, (breakdowns on its own in basic conditions) and RNAse activity (a nuclease that cleaves RNA). There have been cases of COVID-19 vaccines being discarded due to improper storage [55]. This limits packaging, shipment, and regions of the world allowed to have access to these vaccines because their storage will require specialized equipment and refrigeration. 

Conclusion:

The COVID-19 vaccines have paved a way for more mRNA vaccines to be brought to the medical field. If there is a steady increase in funding, researchers can begin to establish these kinds of vaccines for a variety of different diseases. By working through setbacks and finding a way to deliver vaccinations to the masses as well as bringing money to research, many of the limitations of mRNA vaccines can be mitigated in the future. The COVID-19 vaccine has proven to be quite efficacious and the recent FDA approvals are evident of this. These vaccines have been able to set a precedent of how mRNA vaccines can be used throughout health care as a protective measure.  mRNA vaccines are still considered a “new” technology and will continue to be researched and applied to a wide variety of fields in the future.

 

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Computational Strategies in the Treatment and Analysis of COVID-19

March 11, 2022

By Surya Vishnubhatt, Biomedical Engineering, ’22

Author’s Note: The devastating COVID-19 pandemic, having resulted in the death of millions of people worldwide, has spurred innovation in countless sectors of academia, namely in the field of bioinformatics and computational biology. By using computer science techniques, researchers have been able to rapidly identify treatments and further analyze the SARS-CoV-2 virus; the following review synthesizes computational advancements in COVID-19 research through immunoinformatics, docking servers, machine learning, and microRNA analysis. This review also incorporates current computational approaches in the treatment and analysis of COVID-19 viral variants. The use of bioinformatics and computational biology, in pursuit of analyzing and treating all forms of COVID-19, has yielded fast and effective therapeutic treatments in conjunction with crucial analytical findings. With much of the United States now opening up, and the virus likely to become a global endemic, the need for fast, computational analysis of the disease, regarding its progression and spread, is invaluable in ensuring public safety.

 

Introduction

The COVID-19 pandemic is a global public health emergency, with the fast spreading virus having engulfed the world within a few months. The respiratory disease, as of February 2022, has resulted in the death of 5.9 million people worldwide [1]. The viral pathogen behind COVID-19 is SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). This relentless virus has a mutation rate of 9.8 x 10-4  substitutions per site per year, which refers to the replacement of a specific DNA base pair with another by means of nucleotide substitution. Given the scale of the genome (the human genome is 3.2 billion base pairs long), the mutation rate of SARS-CoV-2 allows for substantial changes to the virus’ spike protein, allowing it to evade its host’s defenses and causing new viral variants to crop up around the world [2]. As the prevalence of these variants increases worldwide, the importance of effective computational analysis of COVID-19 protein and antibody dynamics cannot be overstated.

Bioinformatics and computational biology are highly similar, interdisciplinary areas of study that use the core tenets of computer science to analyze biological data. In general, bioinformatics and computational biology are crucial in understanding and analyzing protein dynamics, primarily in regards to sequence, structure, and evolution based analysis (which tracks changes in protein composition over time) [3].

A variety of computational approaches have been and are being investigated in the hopes of better understanding how COVID-19 operates in order to develop effective therapeutic treatments. One such approach concerns the field of immunoinformatics, a subset of bioinformatics and computational biology, which uses protein structures and genome sequences to develop vaccines [4]. Other areas of interest involve the use of docking servers (which predict and model protein-ligand binding interactions) and machine learning to identify and develop COVID-19 therapeutics [5]. Further research is also being conducted in analyzing micro RNA to better understand and exploit the cellular dynamics of COVID-19 [6]. 

This review will investigate established computational approaches and will also explore novel research regarding COVID-19 in the hopes of stimulating further research in COVID-19 variant analysis.

A Brief History of COVID-19 

The SARS-CoV-2 virus emerged in December of 2019, first reported in Wuhan, China. The unique virology of SARS-CoV-2 allowed for its rapid progression and spread. The virus itself is covered with spike proteins on its surface; each spike protein consists of three monomeric units which bind to human ACE2 receptor cells [7]. ACE2 receptors are present on the surface of human muscle cells, primarily in the lungs, and act to mediate vascular constriction and inflammation. During COVID-19 pathogenesis, the SARS-CoV-2 spike protein can bind to the human ACE2 receptor, invade the cell, and proliferate, leading to lung damage. The spike protein has an incredibly high affinity for the ACE2 receptor due to contact interactions which occur at the interface between the receptor binding domain of the spike protein and the ACE2 receptor, contributing to the widespread nature of the disease [8].

Figure 1: The SARS-CoV-2 infection mechanism. 

Currently, in the United States, vaccines have been created for the original viral strain, namely the double dose Pfizer and Moderna (mRNA based) vaccines and the single dose Johnson and Johnson (adenovirus based) vaccine [9]. These vaccines stimulate the host to synthesize a non-pathogenic version of the spike protein, which triggers an immune response and is then targeted by host-specific antibodies (generated in response to the host-mediated spike protein), rendering immunity to the major COVID-19 strain. However, with the rise of new variants, most notably, the omicron B.1.1.529 strain, the effectiveness of mRNA (e.g. Pfizer and Moderna) vaccines are diminishing from 95% efficacy to 35% efficacy, with booster shots required to increase efficacy to 75% [10, 11]. Similarly, the Johnson & Johnson adenovirus based vaccine declined from 94% to 85% efficacy in adenovirus based vaccine therapies in individuals who received booster shots [12, 13].

Applications of Bioinformatics and Computational Biology in COVID-19 Research

The field of bioinformatics and computational biology deals in the collection and analysis of biological data, namely genomic and proteomic data, in the hopes of better understanding disease pathogenesis [3]. 

3.1 Immunoinformatics and COVID-19 Vaccine Development

The field of immunoinformatics is a subset of the field of bioinformatics and computational biology. Specifically, it uses computational, analytical, and mathematical data tied in with computer science processing techniques, to formulate predictions about immunity and vaccine development [14]. In the immunoinformatics-based approach to COVID-19 vaccine development and drug discovery, it is important to note that only the receptor binding domain (RBD) of the SARS-CoV-2 spike protein is in contact with the human ACE2 receptor, making the RBD the major functional region of the virus. Accordingly, the major immunoinformatics-based approaches to COVID-19 antibody development target the RBD of the spike protein, preventing its attachment to the ACE2 receptor [15]. 

There are two major methodologies of vaccine discovery through immunoinformatics: reverse vaccinology and structural vaccinology [4]. Reverse vaccinology analyzes expressed genomic sequences in order to identify various antigens as potential vaccine targets, as these identified antigens are, ideally, to be synthesized and subsequently targeted by the host immune system. Meanwhile, structural vaccinology uses 3D protein models to engineer immunogenic conformations of antigens in the hopes of eliciting antibody responses against pathogenic attack [16, 17]. Structural vaccinology is not explicitly used in COVID-19 drug discovery, but is incorporated within modern reverse vaccinology techniques [4]. 

A new study used reverse vaccinology programs as well as novel computation techniques such as the Molecular Mechanics Poisson-Boltzmann Surface Area calculation approach, to design a COVID-19 antibody protein that can provoke a wide array of host immune responses [18]. This immunological approach, in its emphasis on reverse vaccinology, has also been successfully implemented in the design of a multi-epitope subunit vaccine, triggering immunity in both humoral and cell-mediated contexts [19]. Using DNA/PCR visualization software, researchers observed that the multi-epitope vaccine has highly specified, targeted responses to pathogenic invasion via host-mediated immune response [19, 20]. 

3.2 COVID-19 Docking Analysis

The SARS-CoV-2 docking procedure binds the pathogen to the host’s ACE2 receptors. It is a key point of interest for many researchers who aim to disrupt this binding configuration to prevent COVID-19 infection [21]. Free energy simulators can be used to visualize the stability of various binding configurations of proteins to ligands with a lower binding free energy value indicating a more stable protein-ligand configuration. Using these computational free-energy simulators that bind ligands to the spike protein, potential antibodies can be developed to block or destabilize host-virus interactions [22]. A variety of preliminary studies have been able to identify potential therapeutic compounds from which drug development can progress.

Furthermore, COVID-19 binding can be simulated by docking servers which model how small molecules, peptides, and antibodies bind to potential targets on SARS-CoV-2. In 2020, a team from China created a free meta-server to predict COVID-19 target-ligand interactions to promote drug discovery [23]. This server has been used in a variety of studies. One study used the server to test docking scores of a variety of potential antiviral agents and found that scalarane-based sesterterpenes (a biochemical) showed promise in developing COVID-19 vaccines [24]. Another study, using the same server, identified teicoplanin, an antibiotic, as a potential source of drug design in combating SARS-CoV-2 infection [25]. 

Several other studies have used docking servers to analyze potential plant-based therapeutic targets; including the compounds of the Boerhavia diffusa, the phytochemicals of the Phyllanthus amarus and Andrographis paniculata, and hesperidin [26-28]. These compounds were initially chosen due to their therapeutic properties and have been previously used to treat a wide array of diseases. Upon further analysis, resulting simulations show that these chemicals can destabilize the ACE2-spike protein complex, thus rendering host immunity [28]. In addition, in silico molecular docking techniques were used in identifying the antiviral drugs Remdesivir and Mycophenolic acid acyl glucuronide as potential candidates to be repurposed towards COVID-19 treatment, due to their preferential binding to the main protease of SARS-CoV-2. This preferential binding can then be used to disrupt the binding of SARS-CoV-2 to human ACE2 receptor cells [29]. 

3.3 Machine Learning and COVID-19

The field of machine learning is a branch of computer science which trains an algorithm to “learn” through feeding it enough data such that it can make logical predictions about a variety of different sets of conditions [30]. 

Reverse vaccinology can be combined with machine learning practices to design COVID-19 vaccines. The machine learning tool, known as Vaxign-ML, incorporates biochemical and physicochemical characteristics into its reverse-vaccinology analysis [31]. This platform can then be incorporated with machine learning algorithms, to identify “cocktail” vaccines, consisting of structural and non structural proteins (a protein that is encoded but not part of the viral body), which stimulate an immune response to COVID-19 [32]. 

Another aspect of machine learning in COVID-19 research involves a more external approach to attacking the problem. Researchers from the Sri Ramaswamy Memorial Institute of Science and Technology were able to train a machine learning algorithm to analyze abnormal chest x-rays and CT scan data in patients exhibiting signs of COVID-19. The study yielded a 93% recall score of CT scan images and 88% precision in analyzing chest x-ray images [33]. Similar machine learning algorithms were used in a different study to analyze abnormal features in the CT scan data of patient lungs. This study yielded an accuracy of 91.94% in diagnosing COVID-19 infection [34]. 

3.4 MicroRNA (miRNA) and COVID-19 Analysis

MicroRNAs or miRNAs are pieces of RNA which act to regulate gene expression post-transcriptionally [35]. Researchers from Italy and Singapore found that various miRNAs are regulated by the spike protein of SARS-CoV-2 and the human ACE2 receptor in conjunction with the enzyme histone deacetylase (HDAC). Through computational analysis, using in silico methods and the query miRNet analytics platform, these researchers were able to identify that HDAC inhibitors limited interactions between the spike protein and the ACE2 receptor [36]. Further studies confirmed the effectiveness of HDAC inhibitors as a preventative drug to restrict SARS-CoV-2 entrance into the host, using a wide array of laboratory tests and culture analyses [37]. Using similar methodologies, another study constructed its own computational meta-analysis framework to identify how host miRNAs bind to SARS-CoV-2 RNA and suggests the repurposing of anti hepatitis C, RNA based, drugs in the treatment of COVID-19, due to its substantial binding affinity [38]. 

Current Computational Efforts in COVID-19 Variant Analysis 

COVID-19 variants are formed when the virus’ spike protein mutates, making it harder for the established antibodies in vaccinated people to recognize and bind to the pathogen. In some cases, the established antibodies may be able to bind well enough against the variant molecule, while in other cases, a breakthrough infection may occur and the virus is able to override the host’s defense systems [39, 40]. Currently the four major variants of COVID-19 in the United States are: Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529) [41]. 

Figure 2: A phylogenetic tree depicting the dominant COVID-19 variants as of December 2021.

By using machine learning algorithms, and comparing genome sequences, researchers were able to obtain an overall picture of the spread of variants throughout all continents [42]. Similarly, other researchers were able to track the global progression of coronavirus variants by aggregating data, concerning the worldwide evolution of COVID-19 nucleotide-substitutions, and building an open source web application known as COVID CG to reflect their findings [43]. Other population-orientated studies investigate the genetic, topological, and evolutionary progression of SARS-CoV-2 in order to understand its emergence on the global scale and how to homogeneously apply vaccines to heterogeneous populations, in the hopes of preventing the spread of COVID-19 and its variants in the future [44-46].

Figure 3: The COVID CG tool, from the end user perspective.

Other studies use computational modeling mechanisms to determine how variants interact with ACE2 receptor cells. One such study modeled a wide array of mutations to the spike protein in order to determine variant transmissibility, which can aid in establishing future safety precautions [47]. Another study was able to model the transmission dynamics of COVID-19 by computationally comparing it with dengue infection (as dengue fever and COVID-19 have similar symptoms in the earlier stages of infection) to obtain alternate insights into COVID-19 disease progression [48].

Conclusion

The field of bioinformatics and computational biology is expansive in its coverage; it can be narrowed down to analyze specific protein-to-protein interactions on the molecular scale or expanded to examine the global progression of disease. With much of the United States reopening its borders, and students returning to in-person classes, the rapid computational analysis of COVID-19 disease progression on both a micro and macro scale is invaluable in ensuring public safety. 

As of January 2022, actions to curb the spread of variants have been taken in the form of booster shots and the Pfizer pill. Booster shots reintroduce the same material as the previously mentioned vaccinations to “boost” or reinforce host immunity by increasing the count of memory B and T cells [49]. Additionally, the FDA approved Pfizer COVID-19 Oral Antiviral Treatment, or Paxlovid, consists of nirmatrelvir and ritonavir, with nirmatrelivr acting to prevent viral replication while ritonavir reduces the breakdown of nirmatrelvir. Furthermore, Paxlovid has been proven to be effective against COVID-19 variants in in vitro studies [50-52]. Ultimately, due to the virus’ rapid evolution, most experts have reached a common consensus, that COVID-19 is likely to continually circulate as an endemic, with yearly vaccines needing to be developed and administered, much like the flu [53]. Like the flu, we must continually stay “ahead” of the virus and its variants. Thus, the need for fast, effective computational analysis of the disease and its mutations is essential in mitigating its potentially detrimental effects. 

 

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  36. Teodori, Laura, Piero Sestili, Valeria Madiai, Sofia Coppari, Daniele Fraternale, Marco Bruno Rocchi, Seeram Ramakrishna, and Maria Cristina Albertini. “MicroRNAs Bioinformatics Analyses Identifying HDAC Pathway as a Putative Target for Existing Anti‐Covid‐19 Therapeutics.” Frontiers in Pharmacology 11 (2020). https://doi.org/10.3389/fphar.2020.582003. 
  37. Liu, Ke, Rongfeng Zou, Wenqiang Cui, Meiqing Li, Xueying Wang, Junlin Dong, Hongchun Li, et al. “Clinical HDAC Inhibitors Are Effective Drugs to Prevent the Entry of SARS-COV2.” ACS Pharmacology & Translational Science 3, no. 6 (2020): 1361–70. https://doi.org/10.1021/acsptsci.0c00163. 
  38. Alam, Tanvir, and Leonard Lipovich. “Mircovid-19: Potential Targets of Human Mirnas in SARS-COV-2 for RNA-Based Drug Discovery.” Non-Coding RNA 7, no. 1 (2021): 18. https://doi.org/10.3390/ncrna7010018. 
  39. Hacisuleyman, Ezgi, Caryn Hale, Yuhki Saito, Nathalie E. Blachere, Marissa Bergh, Erin G. Conlon, Dennis J. Schaefer-Babajew, et al. “Vaccine Breakthrough Infections with SARS-COV-2 Variants.” New England Journal of Medicine 384, no. 23 (2021): 2212–18. https://doi.org/10.1056/nejmoa2105000. 
  40. Planas, Delphine, David Veyer, Artem Baidaliuk, Isabelle Staropoli, Florence Guivel-Benhassine, Maaran Michael Rajah, Cyril Planchais, et al. “Reduced Sensitivity of SARS-COV-2 Variant Delta to Antibody Neutralization.” Nature 596, no. 7871 (2021): 276–80. https://doi.org/10.1038/s41586-021-03777-9. 
  41. Aleem, Abdul, Amy K. Slenker, and Abdul Bari Akbar Samad. “Emerging Variants of SARS-COV-2 and Novel Therapeutics against Coronavirus (COVID-19).” National Center for Biotechnology Information. U.S. National Library of Medicine. Accessed January 4, 2022. https://pubmed.ncbi.nlm.nih.gov/34033342/. 
  42. Ekpenyong, Moses Effiong, Mercy Ernest Edoho, Udoinyang Godwin Inyang, Faith-Michael Uzoka, Itemobong Samuel Ekaidem, Anietie Effiong Moses, Martins Ochubiojo Emeje, et al. “A Hybrid Computational Framework for Intelligent Inter-Continent SARS-COV-2 Sub-Strains Characterization and Prediction.” Scientific Reports 11, no. 1 (2021). https://doi.org/10.1038/s41598-021-93757-w. 
  43. Chen, Albert Tian, Kevin Altschuler, Shing Hei Zhan, Yujia Alina Chan, and Benjamin E Deverman. “COVID-19 CG Enables SARS-COV-2 Mutation and Lineage Tracking by Locations and Dates of Interest.” eLife 10 (2021). https://doi.org/10.7554/elife.63409. 
  44. Hahn, Georg, Sanghun Lee, Scott T. Weiss, and Christoph Lange. “Unsupervised Cluster Analysis of Sars‐Cov‐2 Genomes Reflects Its Geographic Progression and Identifies Distinct Genetic Subgroups of SARS‐COV‐2 Virus.” Genetic Epidemiology 45, no. 3 (2021): 316–23. https://doi.org/10.1002/gepi.22373. 
  45. Sarkar, Jnanendra Prasad, Indrajit Saha, Arijit Seal, Debasree Maity, and Ujjwal Maulik. “Topological Analysis for Sequence Variability: Case Study on More than 2K Sars-COV-2 Sequences of COVID-19 Infected 54 Countries in Comparison with SARS-COV-1 and MERS-COV.” Infection, Genetics and Evolution 88 (2021): 104708. https://doi.org/10.1016/j.meegid.2021.104708. 
  46. Tabibzadeh, Alireza, Maryam Esghaei, Saber Soltani, Parastoo Yousefi, Mahsa Taherizadeh, Fahimeh Safarnezhad Tameshkel, Mahsa Golahdooz, et al. “Evolutionary Study of Covid‐19, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐COV‐2) as an Emerging Coronavirus: Phylogenetic Analysis and Literature Review.” Veterinary Medicine and Science 7, no. 2 (2020): 559–71. https://doi.org/10.1002/vms3.394. 
  47. Hark Gan, Hin, Alan Twaddle, Benoit Marchand, and Kristin C. Gunsalus. “Structural Modeling of the SARS-COV-2 Spike/Human ACE2 Complex Interface Can Identify High-Affinity Variants Associated with Increased Transmissibility,” 2021. https://doi.org/10.1101/2021.03.22.436454. 
  48. Rehman, Attiq ul, Ram Singh, and Praveen Agarwal. “Modeling, Analysis and Prediction of New Variants of Covid-19 and Dengue Co-Infection on Complex Network.” Chaos, Solitons & Fractals 150 (2021): 111008. https://doi.org/10.1016/j.chaos.2021.111008. 
  49. “Covid-19 Vaccine Booster Shots.” Centers for Disease Control and Prevention. Centers for Disease Control and Prevention. Accessed January 4, 2022. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/booster-shot.html. 
  50. “Pfizer’s Novel Covid-19 Oral Antiviral Treatment Candidate Reduced Risk of Hospitalization or Death by 89% in Interim Analysis of Phase 2/3 Epic-HR Study.” Pfizer. Accessed January 4, 2022. https://www.pfizer.com/news/press-release/press-release-detail/pfizers-novel-covid-19-oral-antiviral-treatment-candidate. 
  51. “Pfizer Receives U.S. FDA Emergency Use Authorization for Novel COVID-19 Oral Antiviral Treatment.” Pfizer. Accessed January 4, 2022. https://www.pfizer.com/news/press-release/press-release-detail/pfizer-receives-us-fda-emergency-use-authorization-novel. 
  52. Commissioner, Office of the. “Coronavirus (COVID-19) Update: FDA Authorizes First Oral Antiviral for Treatment of Covid-19.” U.S. Food and Drug Administration. FDA. Accessed January 4, 2022. https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-authorizes-first-oral-antiviral-treatment-covid-19. 
  53. Torjesen, Ingrid. “Covid-19 Will Become Endemic but with Decreased Potency over Time, Scientists Believe.” BMJ, 2021. https://doi.org/10.1136/bmj.n494.

How Poop is Fighting COVID-19

November 5, 2021

By Laura Gardner, Biochemistry and Molecular Biology ‘22

Author’s Note: With so much information in the media and online about COVID-19, I find many people get lost in, and fall victim to, false information. I want to reassure the Davis community with factual information on how Davis is fighting COVID-19. With UC Davis’ strong scientific community, I was curious what tools were being used to mitigate the spread of  COVID-19. In January 2021, I attended a virtual COVID-19 symposium called Questions about Tests and Vaccines led by Walter S Leal, distinguished Professor of the Department of Molecular and Cellular Biology at University of California-Davis (UC Davis). In this symposium, I learned about Dr. Heather Bischel’s work testing the sewer system. This testing is another source for early detection of COVID-19. In combination with biweekly testing, I have no doubt that UC Davis is being proactive in their precautions throughout the pandemic, which made me personally feel more safe. I hope that this article will shed light on wastewater epidemiology as a tool that can be implemented elsewhere.

 

Dr. Heather Bischel is an assistant professor in the Department of Civil and Environmental Engineering at the University of California, Davis. Bischel has teamed up with the city of Davis through the Healthy Davis Together initiative to use wastewater epidemiology, a technique for measuring chemicals in wastewater, to monitor the presence of SARS-CoV-2, the virus that causes COVID-19 [6]. When a person defecates, their waste travels through the pipes and is collected in the sewer system. In both pre-symptomatic and asymptomatic individuals, their feces will carry the genetic material that indicates the virus is present. This is because SARS-CoV-2 uses angiotensin-converting enzyme 2, also known as ACE2, as a cellular receptor, which is abundantly expressed in the small intestine allowing viral replication in the gastrointestinal tract [1]. This serves as an early indicator of a possible COVID-19 outbreak and leads to quick treatment and isolation, which are important to stop the spread of the disease.  

Samples are taken periodically from manholes around campus using a mechanical device called an autosampler. These autosamplers are lowered into manholes to collect wastewater flow samples every 15 minutes for 24 hours. Next, the samples are taken to the lab where they are able to extract genetic material and use Polymerase Chain Reaction (PCR) to detect the virus. Chemical markers that attach to the specific genetic sequence of the virus are added to the sample, which reacts to the COVID-19 virus by fluorescing visible light. This light is the signal that indicates positive test results. 

The samples are collected throughout campus, with a focus on residential halls. An infected person will excrete the virus through their bowel movements before showing symptoms. The samples are so sensitive that if even just one person among thousands is sick, they are still able to detect the presence of COVID-19 genetic material.  When a PCR test provides a positive signal, the program works closely with the UC Davis campus to identify if there has been someone who has reported a positive COVID-19 test. If no one from the building is known to be positive, they send out a communication email asking all the students of the building to get tested as soon as possible. That way the infected person can be identified and isolated as soon as possible, eliminating exposure from unidentified cases [4].

In collaboration with the UC Davis campus as well as the city of Davis, Dr. Bische has implemented wastewater epidemiology throughout the community. Since summer 2020, Dr. Bische’s team of researchers have collected data which is available online through the Healthy Davis Together initiative [4].  

In addition to being an early indicator, this data has also been used to determine trends, which can indicate if existing efforts to combat the virus are working or not [2]. Existing efforts include vaccinations, mask wearing, washing hands, maintaining proper social distancing, and staying home when one feels ill. UC Davis has implemented protocols including biweekly testing and a daily symptom survey that must be completed and approved in order to be on campus.

Wastewater epidemiology has been implemented all over the world, at more than 233 Universities and in 50 different countries, according to monitoring efforts from UC Merced [3]. This testing has been used in the past to detect polio, but has never before been implemented on the scale of a global pandemic. Lacking infrastructure, such as ineffective waste disposal systems, open defecation, and poor sanitation pose global challenges, especially in developing countries [2].  Without tools for early detection, these communities are in danger of having an exponential rise in cases.

“Our work enables data-driven decision-making using wastewater infrastructure at city, neighborhood, and building scales,” Dr. Bische stated proudly in her latest blog post [2]. These decisions are crucial in confining COVID-19 as we continue to push through the pandemic.

Summary of how wastewater epidemiology is used to fight COVID-19

 

References:

  1. Aguiar-Oliveira, Maria de Lourdes et al. “Wastewater-Based Epidemiology (WBE) and Viral Detection in Polluted Surface Water: A Valuable Tool for COVID-19 Surveillance-A Brief Review.” International journal of environmental research and public health vol. 17,24 9251. 10 Dec. 2020, doi:10.3390/ijerph17249251
  2. Bischel, Heather. Catching up with our public-facing COVID-19 wastewater research. Accessed August 15, 2021.Available from H.Bischel.faculty.ucdavis
  3. Deepshikha Pandey, Shelly Verma, Priyanka Verma,et al. SARS-CoV-2 in wastewater: Challenges for developing countries, International Journal of Hygiene and Environmental Health,Volume 231,2021,113634, ISSN 1438-4639, https://doi.org/10.1016/j.ijheh.2020.113634.
  4. Healthy Davis Together. Accessed  February 2, 2021. Available from Healthy Davis Together – Working to prevent COVID-19 in Davis
  5. UCMerced Researchers. Covid Poops Summary of Global SARS-CoV-2 Wastewater Monitoring Efforts. Accessed February 2, 2021. Available from COVIDPoops19 (arcgis.com) 
  6. Walter S Leal. January 13, 2021. COVID symposium Questions about Tests and Vaccines. Live stream online on zoom.

COVID-19 Cover Art Gallery

September 24, 2021

This year, for the first time, The Aggie Transcript accepted submissions for our journal’s cover from the wider undergraduate community at UC Davis. To celebrate the release of our fifth annual print edition, we present three of the submissions that we received in this art gallery. The winning submission appears first, followed by our honorable mentions. We sincerely thank the authors and artists who submitted to our journal this year for sharing their work with us.

 

Unprecedented: The Science of COVID-19

By Mario Rodriguez, Wildlife, Fish & Conservation Biology, Design ‘22

This work was created with the intention of highlighting those in the medical profession and the timeline of the COVID-19 pandemic, from bottom to top: the death of loved ones, the medication and hospitalization of patients, and then the development of vaccines to combat the virus. The digital artwork was created on an iPad in the digital painting app Procreate.

 

Working Together to Catch Covid

By Daria Beniakoff, Biochemistry & Molecular Biology ‘21

Last year, the COVID-19 pandemic affected everyone. The hands of so many people from every direction were put to work figuring out what the virus was and how to mitigate and fight its effects, from the doctors treating patients to the scientists trying to develop treatments and vaccines to the everyday people who had to work around the circumstances. I wanted this digital medium piece to reflect the collaborative effort to contain and stop the pandemic.

 

Light of Hope

By Bianca Law, Design ‘23

COVID-19 survivors can retrain their smell to enjoy food and wine again

September 17, 2021

By Daniel Erenstein, Neurobiology, Physiology & Behavior ‘21

Author’s Note: Last spring, I enrolled in the inaugural offering of the University Writing Program’s wine writing course. Our instructor, Dr. Alison Bright, encouraged us to report on topics of personal interest through our news stories on the wine industry, viticulture, enology, and more. In this article, which was prepared for an audience of general science enthusiasts, I examine how biologists are making sense of a puzzling COVID-19 symptom — anosmia, or loss of smell — and what COVID-19 patients with this condition can do to overcome it. Eighteen months into this pandemic, scientists continue to study cases of COVID-19-related anosmia with dreams of a treatment on the horizon. I hope that readers feel inspired by this article to follow this in-progress scientific story. I extend my appreciation to Dr. Bright, who throughout the quarter shared approaches to rhetorical awareness that elevated my grasp of effective writing.

 

 

Image caption: Anton Ego, the “Grim Eater” from PIXAR’s Ratatouille, is reminded of his childhood by Remy’s rendition of ratatouille, a Provençal dish of stewed vegetables.

 

With a single bite of Remy’s latest culinary creation, the eyes of Anton Ego, a notoriously harsh food critic, dilate, and Ratatouille’s viewers are transported back in time with Monsieur Ego. The meal — a simple yet elegant serving of ratatouille, accompanied by a glass of 1947 Château Cheval Blanc — has triggered a flashback to one singular moment, a home-cooked meal during his childhood. The universal charm of this enduring scene resonates; in Ego’s eyes, many recognize how our senses of smell and taste can impact a culinary experience. 

Imagine how a real-life version of this scene might change for the millions of COVID-19 patients who have lost their sense of smell [1]. Anosmia, the phenomenon of smell loss, has become one of the more perplexing COVID-19 symptoms since first observed in patients during the earliest months of the pandemic [2].

What happens when we lose our sense of smell? During the pandemic, scientists have studied smell loss, which affects more than 85 percent of COVID-19 patients according to research published this year in the Journal of Internal Medicine [3]. In fact, anosmia is so common in COVID-19 patients that physicians were offered guidance for testing olfactory function as an indicator of infection last year [4]. 

To simplify studies of these complicated senses, taste and smell are often examined independently of one another, even though these senses are usually experienced simultaneously.

“Smell is just — it’s so crucial to taste, which means it’s really crucial to everything that I do,” said Tejal Rao, a New York Times food critic, in a March episode of The Daily [5]. “And it’s really difficult to cook without a sense of smell if you’re not used to it. You don’t know what’s going on. It’s almost like wearing a blindfold.”

Rao, who lost her sense of smell in mid-January after contracting COVID-19, began to search for answers to this mystery from scientists. Rao’s journey started with TikTok “miracle cures” and other aromatherapies — unfortunately, they were too good to be true — but she eventually discovered the work of Dr. Pamela Dalton, a scientist at the Monell Chemical Senses Center in Philadelphia [6]. At the center, Dalton studies the emotions that are triggered by our sense of smell [7].

During simple colds or viral infections, smell is normally affected when the molecules in food and other aromas are physically blocked off from chemoreceptors in our nose by congestion. Scientists have also cited Alzheimer’s and Parkinson’s diseases, head trauma, and chemotherapy as triggers for anosmia [8]. But a separate phenomenon was occurring in the case of COVID-19.

“COVID is different in that way, because most people who lost their sense of smell did so without having any nasal congestion whatsoever,” Dalton told Rao during an interview.

One study published in October of last year by Dr. Nicolas Meunier, a French neuroscientist, aimed to investigate how the SARS-CoV-2 virus, which causes COVID-19, may disrupt sustentacular cells [9]. These structural cells express the ACE2 receptor, which the virus hijacks to gain entry into our cells, at higher levels [10]. Sustentacular cells support the specialized neurons that transmit signals from the nose to the brain.

When Meunier and his team at Paris-Saclay University in France infected hamsters with the virus, tiny hair-like projections known as cilia on the surfaces of olfactory neurons began to peel back from sustentacular cells. This disruption is a possible explanation for the difficulties with smell that COVID-19 patients experience. 

If it is true that damage to sustentacular cells causes anosmia, loss of smell is not an irreversible brain condition. In this case, the poor connection between incoming odors and brain networks that typically process these stimuli is at fault, not direct damage to the brain itself. The sudden onset of smell loss in COVID-19 patients supports this thinking.

“It was just like a light bulb got turned off or a switch got flicked to off,” Dalton said. “And one moment they could smell. And the next moment, nothing smelled.”

But because olfactory support cells regularly regenerate, this loss of smell is only temporary, which allows for retraining of our senses. Two months of smell training, also known as olfactory training, allowed Rao to regain her sense of smell.

Olfactory training gradually exposes patients to particularly strong smells. Spices such as cinnamon or cumin, for example, were perfect for Rao’s first smell training session [5], and AbScent, a British charity offering support to patients with anosmia, sells kits with rose, lemon, and eucalyptus [8]. Scientists have found that recurring exposure to these strong scents gives the brain time to recalibrate its networks, a feature known as neuroplasticity [11].

But “you don’t just go from hurt to healed overnight,” Rao said. “It’s more like adjusting and learning how to live in a new space. That’s really just the beginning.”

Our chemical senses have the power to satisfy, to inspire, even to cause our memory to reveal itself, as 20th-century French author Marcel Proust observed in his seven-volume novel À la recherche du temps perdu, or In Search of Lost Time. Researchers have even speculated that our sense of smell can facilitate learning in other sensory domains, including vision [12].

While scientists further investigate how coronavirus causes loss of smell, olfactory training can provide an avenue in the meantime for COVID-19 patients to recover this crucial sense. Indeed, many patients are “in search of lost time,” and smell training can help them to once again experience food and wine in its sensory entirety.

 

References:

  1. Allen J, Almukhtar S, Aufrichtig A, Barnard A, Bloch M, Cahalan S, Cai W, Calderone J, Collins K, Conlen M, et al. 2021. Coronavirus in the U.S.: Latest Map and Case Count. New York (NY): New York Times; [accessed 28 July 2021]. https://www.nytimes.com/interactive/2021/us/covid-cases.html.
  2. Symptoms of COVID-19. 2021. Atlanta (GA): Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, Division of Viral Diseases; [accessed 28 July 2021]. https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
  3. Lechien JR, Chiesa-Estomba CM, Beckers E, Mustin V, Ducarme M, Journe F, Marchant A, Jouffe L, Barillari MR, Cammaroto G, et al. 2021. Prevalence and 6-month recovery of olfactory dysfunction: a multicentre study of 1363 COVID-19 patients. J Intern Med. 290(2):451–461. https://doi.org/10.1111/joim.13209.
  4. Whitcroft KL, Hummel T. 2020. Olfactory Dysfunction in COVID-19: Diagnosis and Management. JAMA. 323(24):2512–2514. https://doi.org/10.1001/jama.2020.8391.
  5. Antolini T, Dorr W, Powell D, Schreppel C. 2021. A Food Critic Loses Her Sense of Smell. New York (NY): New York Times; [accessed 28 July 2021]. https://www.nytimes.com/2021/03/23/podcasts/the-daily/coronavirus-smell-food.html.
  6. Rao T. 2021. Will Fish Sauce and Charred Oranges Return the World Covid Took From Me? New York (NY): New York Times; [accessed 28 July 2021]. https://www.nytimes.com/2021/03/02/dining/covid-loss-of-smell.html.
  7. What COVID 19 is teaching us about the importance of smell, with Pamela Dalton, PhD. 17 Mar 2021, 33:31 minutes. American Psychological Association; [accessed 28 July 2021]. https://youtu.be/0pG_U13XDog.
  8. Schoch D. 2021. Distorted, Bizarre Food Smells Haunt Covid Survivors. New York (NY): New York Times; [accessed 28 July 2021]. https://www.nytimes.com/2021/06/15/health/covid-smells-food.html
  9. Bryche B, St Albin A, Murri S, Lacôte S, Pulido C, Ar Gouilh M, Lesellier S, Servat A, Wasniewski M, Picard-Meyer E, et al. 2020. Massive transient damage of the olfactory epithelium associated with infection of sustentacular cells by SARS-CoV-2 in golden Syrian hamsters. Brain Behav Immun. 89(2):579–586. https://doi.org/10.1016/j.bbi.2020.06.032.
  10. Brann DH, Tsukahara T, Weinreb C, Lipovsek M, Van den Berge K, Gong B, Chance R, Macaulay IC, Chou HJ, Fletcher RB, et al. 2020. Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia. Sci Adv. 6(31): eabc5801.
  11. Kollndorfer K, Kowalczyk K, Hoche E, Mueller CA, Pollak M, Trattnig S, Schöpf V. 2014. Recovery of Olfactory Function Induces Neuroplasticity Effects in Patients with Smell Loss. Neural Plast. 1–7. https://doi.org/10.1155/2014/140419.
  12. Olofsson JK, Ekström I, Lindström J, Syrjänen E, Stigsdotter-Neely A, Nyberg L, Jonsson S, Larsson M. 2020. Smell-Based Memory Training: Evidence of Olfactory Learning and Transfer to the Visual Domain. Chem Senses. 45(7):593–600. https://doi.org/10.1093/chemse/bjaa049.

Surviving COVID-19: Variables of Immune Response

September 12, 2021

By La Rissa Vasquez, Neurobiology, Physiology & Behavior ‘23

Author’s Note: In this paper, I analyze autopsy reports conducted on deceased COVID-19 patients and supply a breakdown of the body’s immune response. The purpose of this paper is to provide a more generalized synopsis of how the body is affected by the virus from the onset of infection to the escalating factors that contribute to cause of death. COVID-19 and SARS-CoV-2 are referenced countless times throughout this paper, but they should not be used interchangeably. The name of the pathogenic virus is “Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2), and the name of the illness is called COVID-19 and is the common usage in forms of discussion. This paper only scratches the surface of the virus’s complexity and its effects upon the body and societies around the world.

 

Introduction
On December 31, 2019, the first case of the novel coronavirus was reported in Wuhan, China [1]. The first case of the virus reported in the United States was on January 22, 2020 [2]. Within 22 days, the Coronavirus had traveled across the Pacific to wreak havoc upon countries woefully unprepared. Within a year, COVID-19 has managed to bring some of the most powerful countries in the world to heel. Economies and healthcare systems across the world continue to be devastated by an adversary only 60 to 140 nanometers in diameter [3]. On February 11, 2020, the International Committee on Taxonomy of Viruses (ICTV) formally identified the virus as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). On March 11, 2020, the World Health Organization classified COVID-19 as a worldwide pandemic and global health crisis [4]. As of May 2021, the CDC has confirmed that the U.S. has over 32 million cases. Healthcare systems across the nation and around the world are overwhelmed by the number of infected patients. Many of them perish due to either a lack of resources or accurate and efficient testing.

SARS-CoV-2 Viral Pathogenesis
Humans have two levels of immunity. Innate immunity is the body’s first line of contact and defense against invading pathogens. Adaptive immunity learns and remembers how to effectively target and eliminate these pathogens.

Innate Immunity
Our innate immune system is composed of barrier tissues and cells specialized for defense against pathogens [5]. Barrier tissues are the first line of defense, and inside barrier tissues reside sentinel cells, which are capable of consistently recognizing repeated exposure to pathogen associated molecular patterns (PAMPs). The sentinel cells release proinflammatory mediators like cytokines, chemokines, or histamines and circulate within the blood vessels inviting more immune cells from the surrounding tissue into the bloodstream [5]. Cells such as neutrophils or monocytes differentiate into macrophages and migrate from the bloodstream and phagocytose (eat) the pathogens. Neutrophils will undergo programmed cell death, referred to as apoptosis. Macrophages will continue to phagocytose the rest of the pathogens and restore homeostasis by consuming the dead neutrophils [5].

Infection occurs when these viral pathogens in respiratory droplets from a sneeze or a cough enter a person’s mouth, nose, or eyes and attach to the ACE-2 receptors in the nose, throat, and especially the lungs. Like any virus, SARS-CoV-2 cannot replicate on its own and instead hijacks the body’s own cellular machinery. The virus inserts its own genetic information into the host cell to produce more copies of itself until the cell bursts and dies, spreading more of the virus around the body to infect more cells [6]. Infection of the host cell consists of the following five steps: attachment, penetration, biosynthesis, maturation, and release. Once a virus binds to host receptors (attachment), it enters host cells via endocytosis or membrane fusion (penetration). Once the viral contents are released inside the host cells, viral RNA are transported by protein molecules in the host cell’s cytoplasm and travel into the nucleus for replication via the nuclear pore complex (NPC). Viral mRNA then makes viral proteins (biosynthesis). Lastly, novel viral particles are made (maturation) and released [7]. This innate immune response is not as effective against SARS-CoV-2 due to the strength of the various proteins displayed in Figure 1, an ultrastructural morphology rendering, provided by the Centers for Disease Control and Prevention (CDC) Image Library on February 10 [8].

Figure 1

The SARS-CoV-2 virus contains “M (membrane), S (spike), E (envelope), and N (nucleocapsid)” proteins, which envelop the virion and act as a defensive shield [9]. The S or Spike viral surface protein, which consists of two subunits, S1 and S2, binds to the angiotensin converting enzyme 2 (ACE2) receptors of the host cells [7]. The primary role of ACE2 is the breakdown of the angiotensin II (ANG II) protein into molecules that neutralize its harmful effects. ANG II is responsible for increased inflammation and death of alveolar cells in the lungs, which reduces oxygen uptake. When the S (spike) protein of SARS-CoV-2 binds to the ACE2 receptors, they inhibit ACE2 from doing its job of regulating ANG II, allowing ANG II to freely damage tissue in the lungs. These ACE2 receptors are naturally present on the surface of the lung’s epithelial cells and other organs throughout the body, but the virus’ S protein uses these receptors to penetrate the cell membrane and replicate inside host cells. The N (nucleocapsid) protein is another viral surface protein of SARS-CoV-2, which inhibits interferons (IFN1 and IFN-β) responsible for cytokine production [10]. But if the signals for regulating proinflammatory response are disrupted by the pathogen’s surface proteins, the innate immune response becomes hyperactive and self-destructive. A malfunctioning innate immune response also compromises an adequate adaptive immune response [9].

Adaptive Immunity
Adaptive immunity consists of B-cell and T-cell responses. B-cells produce antibodies to trigger an immune response, while T-cells actively target and eliminate infected cells.

B-Cell Response
The innate immune response is not particularly equipped to combat pathogens that are especially complex and vicious because the innate immune response is non-specific and will attack anything it identifies as an invader. The adaptive immune response can target pathogens more precisely and powerfully by using proteins called antibodies, which are produced by B-cell lymphocytes that bind to antigens on the surface of pathogens [5]. Adaptive immunity can more efficiently handle foreign pathogens, like a virus, because antibodies can see through the debris of proteins and dead cells left by the cytokine storm. Antibodies uniquely bind to antigens, acting as a beacon for the adaptive immune response to converge on the invading pathogen [5]. More importantly, adaptive immunity has memory and learns how to become more effective by retaining its response to pathogens so that it can be even quicker at eliminating them after repeated exposure [5]. Widespread pandemics like COVID-19 occur because of a lack of protective antibodies in populations that have never been exposed to or vaccinated against the specificity of SARS-CoV-2 [5]. Figure 2 depicts the four ways in which antibodies attack pathogens: neutralization, complement fixation, opsonization, and antibody dependent cellular cytotoxicity.

Figure 2

Figure 2 – “Immunopathogenesis of Coronavirus Disease 2019 (COVID-19)” [3].

Neutralization is the process by which antibodies immediately bind to the surface antigens of a pathogen and block their S protein from attaching to the receptors of healthy cells, thereby neutralizing the virus’ ability to attach and insert its genetic information. Complement fixation occurs when antibodies are responsible for inviting complement proteins to bind to the antigens of the pathogen. This process coats the pathogen in attack proteins that can either initiate the complement cascade leading to cell lysis, the breakdown of the cell, or it can induce the third stage, opsonization. During opsonization, proteins called opsonins bind to the invading pathogen, acting as markers for phagocytotic cells like macrophages to identify and consume the pathogen. Lastly, antibody dependent cellular cytotoxicity (ADCC) is the process by which antibodies recognize the antigen of a pathogen and signal for natural-killer cells (NK cells) to release cytotoxic molecules which kill off the virally infected cell [5].

T-Cell Response
T-cell lymphocytes are produced by the bone marrow and mature in the thymus. They form the basis of cellular immunity because they directly attack foreign pathogens. Consequently, they are more effective than innate immune or B-cell responses at targeting intracellular pathogens like viruses [5]. Antibodies can get distracted by viral particles and proteins, so they rely on the blind T-cell lymphocytes to ignore the surrounding virus particles and eliminate the infected host cell at the source. As naive T-cells circulate the lymph nodes and spleen, they express T-cell receptors (TCR) that recognize cell surface peptides (antigens) attached to major histocompatibility complex (MHC) molecules on the surface of a specific pathogen. These surface MHC proteins tell the T-cells where to attack [5]. The dendritic cells work to activate the adaptive immune response by ingesting viral proteins and turning them into cell surface peptides that bind to MHC molecules, forming peptide-MHC complexes. The TCR of naive T-cells recognize the peptide-MHC complexes and activate the T-cell. For T-cells to become active, they also need to bind to proteins from the dendritic cell via co-simulation. They then undergo clonal expansion and differentiate into effector T-cells [5]. Effector T-cells are also referred to as cytotoxic T lymphocytes (CTLs). They travel through the body to hunt down peptide-MHC presenting pathogens and kill the infected cells by releasing cytotoxic molecules [5].

The adaptive immune response is stimulated by the recognition of pathogen-associated molecular patterns (PAMPs). Within 1-2 weeks after infection, the B-cells produce antibodies while T-cells simultaneously increase proinflammatory cytotoxic molecules in a forceful attempt to eliminate the virus [7]. The uptick in Interleukin cytokines abbreviated as IL-1, IL-6, IL-8, and so on, flood the body with proinflammatory substances, which “chronically increase the stimulation of T-cells, resulting in a cytokine storm and T-cell exhaustion” [9]. T-cell exhaustion not only means that the virus is overwhelming the body’s antibodies but also draining the strength of the T-cell’s ability to eliminate the virus at the source of infected host cells. SARS-CoV-2 is a “high-grade chronic viral infection because it decreases the responsiveness of T-cells leading to a decreased effector function and lower proliferative capacity” [9]. T-cell exhaustion is also linked to an increase in inhibitory receptors that can initiate apoptosis in T-cells. This results in the destruction of T-cells and their co-receptors, further suppressing the T-cells, as well as B-cells and NK cells, all of which are white blood cells (lymphocytes). Thus, explaining the general lymphopenia (the lack of lymphocytes) observed in severe COVID-19 cases and the increased number of cytokines [9]. Viral entry and attachment to ACE2 receptors trigger a vicious cycle of both innate and adaptive immune responses, mounting an intense attack by secreting proinflammatory substances that invite more lymphocytes to try and kill the virus. This releases more cytokines and chemokines [11]. The downregulation of the ACE2 enzyme results in a cascade of chemical reactions that lead to further inflammation and destruction of cells, weakening and damaging the body’s own immune response.

pathologies of a pandemic:

COVID-19 Autopsies
Once the SARS-CoV-2 attaches to alveolar type II cells, it propagates within the cells. Most viral particles cause apoptosis, releasing more self-replicating pulmonary toxins. Figure 3 displays normal ACE2 receptors located in the type II pneumocytes. Healthy alveoli are unobstructed to allow efficient diffusion of oxygen and carbon dioxide with red blood cells.

Figure 3

Figure 3 – “Type I pneumocytes are very thin in order to mediate gas exchange with the bloodstream (via diffusion). Type II pneumocytes secrete a pulmonary surfactant in order to reduce the surface tension within the alveoli” [12].

In contrast to Figure 3, Figure 4 shows the histopathology of alveolar damage (A) and inflammation (B) of the epithelial cells. As the epithelial cells detach from the alveolar wall the alveoli structures collapse and no longer inflate making it hard for patients with severe cases of COVID to breathe [13]. This results in diffuse alveolar damage with fibrin rich hyaline membranes and hemorrhages in the lungs [13]. The histopathology also detected multinucleated cells that lead to pulmonary fibrosis (scarring in the lungs). Infected cells are “abnormally large and often polynucleated cells showing a large cytoplasm with intense staining for the COVID-19 RNA probe” [13]. The viral Spike protein is also largely detected in the histopathology of COVID cases (C). The nuclei of Spike-positive cells appear an intense red stain and have abnormally enlarged cytoplasts (panel h) [13].

Figure 4

Figure 4 – “Histopathological evidence of alveolar damage, inflammation and SARS-CoV-2 infection in COVID-19 lungs” [13].

The cellular destruction detected in the histopathology is macroscopically reflected in the physical damage of lung tissue displayed in Figure 5.

Figure 5

Figure 5 – “Macroscopic appearance of COVID lungs” [13].

In all pathological examinations of patients that died of COVID, their lungs sustained macroscopic damage [13]. Severe cases of COVID reveal congested and firm lungs (A) with “hemorrhagic areas and loss of air spaces (a’, c’)” [13]. As the virus ravages the body, some patients rapidly deteriorate and develop severe inflammation and clotting in the lungs (B) which shows “the thrombosis of large pulmonary vessels, often with multiple thrombi and in one case determining an extensive infarction in the right lobe (Fig. 5B panels a and b)” [13]. The lung’s architecture crumbles as cells lose their integrity and continue to die, thus resulting in the development of Acute Respiratory Distress (ARDS). ARDS develops in about 5% of COVID-19 patients, and of all the infected, the mortality rate remains around 1 to 2% [14]. Autopsies are beginning to reveal that rather than a singular cause of death, many factors seem to be responsible for higher mortality rates in patients that develop critical cases of COVID-19.

The fallout from the hyperactive immune response disrupts regular oxygen diffusion from the alveoli into the capillaries and consequently to the rest of the body. This commonly leaves fluid and dead cells, resulting in pneumonia, a condition in which patients experience symptoms such as coughing, fever, and rapid or shallow breathing [14]. If oxygen levels in the blood continue to drop, patients rely on breathing assistance by a ventilator to forcefully push oxygen into damaged lungs “riddled with white opacities where black space—air—should be” [14]. The presence of opacities in the lungs indicate the development of pneumonia into ARDS, which was found in the autopsy of a 77-year-old man with a history of comorbidities, including hypertension and the removal of his spleen (splenectomy) [15]. The decedent exhibited chills and an intermittent fever but no cough for 6 days. On March 20, 2020, emergency medical services responded to a call, stating that the deceased was experiencing weakness, fever, and shortness of breath. In route to the hospital, the decedent went into cardiac arrest and died shortly after reaching the hospital [15]. A postmortem nasopharyngeal swab was administered and came back positive for SARS-CoV-2.

Figure 6

Figure 7
Figure 6 – Normal chest X-Ray of healthy lungs [16]. Figure 7 – “Lesion segmentation results of three COVID-19 cases displayed using the software post-processing platform” [17].

 

Figure 7 shows opacities in the CT “of typical COVID-19 infection cases at three different infection stages: the early stage, progressive stage, and severe stage” [17]. Figure 7 highlights these opacities in red, which appear to intensify and cover more of the lung CT as the virus increases in severity (a-c). Patient 4 (c) exhibits what medical examiners refer to as a “complete whiteout” of the lungs. Indicating reduced air flow, whereas the normal scan of healthy lungs (Figure 6) has a black background, representing the transparency of free and unrestricted airflow.

The postmortem radiography of the deceased 77-year-old man showed “Diffuse, dense bilateral airspace consolidations (complete “whiteout”)” [15]. In most cases of severe COVID-19 “the greatest severity of CT findings is visible around day 10 after the symptom onset. Acute respiratory distress syndrome is the most common indication for transferring patients with COVID-19 to the ICU” [18].

ARDS in connection to SARS-CoV-2 was first documented in Wuhan, Hubei, China in December 2019 with over 90,000 deaths associated with organ dysfunction, particularly progressive respiratory failure and the formation of blood clots resulting in the highest mortality rates [19]. Another autopsy from Hamburg, Germany conducted on the first 12 documented consecutive cases of COVID-19 related deaths revealed that there was not only profuse alveolar damage in 8 out of the 12 patients but also a high rate of clotting resulting in death. 75% of patients that died were males within an age range of 52 to 87 years and 7 out of 12 patients autopsied (58%) presented venous thromboembolism, as displayed in Figure 7. A pulmonary embolism was the direct cause of death in 4 of the deceased [20].

Figure 8


Figure 8 – “Macroscopic autopsy findings: A. Patchy aspect of the lung surface (case 1). B. Cutting surface of the lung in case 4. C. Pulmonary embolism (case 3). D. Deep venous thrombosis (case 5)” [20].

The formation of clots results in pulmonary vasoconstriction, or the constriction of arteries and halting of blood delivery to the arteries and capillaries in the lungs. Blood cannot travel to the lungs, so oxygen levels drop. As a result, a cytokine storm from our hyperactive immune system occurs, destroying the alveolus and the endothelium and causing clots to form. Smaller clots come together and form a fatal giant blood clot, or the clots can break apart and travel to other parts of the body, causing a blockage and inadequate blood supply to organs or other parts of the body [19]. If the blood supply to fingers, toes, and other extremities is cut off by a clot, it is referred to as ischemia and often results in the amputation of digits and appendages once the flesh begins to die [19].

When SARS-CoV-2 enters the alveolar cells in the lungs via the ACE2 receptors, it can directly attack organs and indirectly cause damage to other organs by triggering a hyperactive immune response (cytokine storm). When the viral particles trigger a cytokine storm, they cause further inflammation of the lungs resulting in plummeting oxygen levels and the formation of blood clots in the arteries (arterial thrombosis).

Conclusion

SARS-CoV-2 is a multi-organ scourge, but it primarily attacks the lung by first attaching its spike protein to the host cell’s ACE2 receptors. This prevents the lungs from regulating their function because it inhibits ANG II protein breakdown, causing increased alveolar damage and inflammation of the lungs. The virion proteins create proinflammatory responses in the innate immune response and compromise an effective adaptive immune response. As the virus progresses the number of neutrophils from the innate immune response increase while the number of helpful lymphocytes (T-cells and B-cells) decrease. The ACE2 receptors overstimulate the innate and adaptive immune response to produce more proinflammatory molecules to eliminate the virus, thus causing more destruction to the body and its immune response. Autopsies of COVID-19 victims show ongoing cellular death and collapse of the respiratory system caused by inflammation and alveolar damage that eventually develop into ARDS. Extreme inflammation induced by the immune response causes difficulties in breathing and clotting in the lungs. Radiography of progressive stages of COVID identify opacities in lung CTs indicating obstructed airways and alveolar deterioration. Postmortem examinations reveal gross destruction of the lung tissue, such as pulmonary artery thrombosis, vasoconstriction, lung infarction, or pulmonary embolism. Progressive organ and respiratory failure and abnormal clotting are all contributing factors to the cause of death in the most severe cases of COVID-19.

SARS-CoV-2 efficiently exploits weaknesses not only within our innate and adaptive immune systems across sex, age, race, and ethnicity, but it also exploits weaknesses within our societies. The etymological origins of Pandemic are rooted in pandēmos , which is Greek for ‘all’ (pan)+ ‘people’ (demos). When simplified, pandemic literally means “all people” but the priorities of leadership across the world reveal that not all people suffer the burden of this pandemic equally. Regarding the United States’ approach to the pandemic, this quote from the Atlantic’s article “Why Some People Get Sicker Than Others” is sufficient; “the damage of disease and a global pandemic is not a mystery. Often, it’s a matter of what societies choose to tolerate. America has empty hotels while people sleep in parking lots. Food is destroyed every day while people go hungry. Americans are forced to endure the physiological stresses of financial catastrophe while corporations are bailed out. With the coronavirus, we do not have vulnerable populations so much as we have vulnerabilities as a population. Our immune system is not strong” [21].

References:
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2. Stokes, Erin K., Laura D. Zambrano, Kayla N. Anderson, Ellyn P. Marder, Kala M. Raz, Suad El Burai Felix, Yunfeng Tie, and Kathleen E. Fullerton. “Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020.” MMWR. Morbidity and Mortality Weekly Report 69, no. 24 (2020): 759-65. doi:10.15585/mmwr.mm6924e2.

3. Wiersinga, W. Joost, Andrew Rhodes, Allen C. Cheng, Sharon J. Peacock, and Hallie C. Prescott. “Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19).” Jama 324, no. 8 (2020): 782. doi:10.1001/jama.2020.12839.

4. “Naming the Coronavirus Disease (COVID-19) and the Virus That Causes It.” World Health Organization. Accessed May 31, 2021. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it.

5. “How The Body Reacts To Viruses – HMX: Harvard Medical School.” HMX | Harvard Medical School. February 19, 2021. Accessed May 31, 2021. https://onlinelearning.hms.harvard.edu/hmx/immunity/.

6. Drexler, Madeline. What You Need to Know about Infectious Disease. Washington, D.C., WA: National Academies Press, 2010.

7. Yuki, Koichi, Miho Fujiogi, and Sophia Koutsogiannaki. “COVID-19 Pathophysiology: A Review.” Clinical Immunology 215 (2020): 108427. doi:10.1016/j.clim.2020.108427.

8. “Image Library.” Centers for Disease Control and Prevention. February 10, 2020. Accessed May 31, 2021. https://www.cdc.gov/media/subtopic/images.htm.

9. Yazdanpanah, Fereshteh, Michael R. Hamblin, and Nima Rezaei. “The Immune System and COVID-19: Friend or Foe?” Life Sciences 256 (2020): 117900. doi:10.1016/j.lfs.2020.117900.

10. Lee, Amanda J., and Ali A. Ashkar. “The Dual Nature of Type I and Type II Interferons.” Frontiers in Immunology 9 (2018). doi:10.3389/fimmu.2018.02061.

11. Scully, Eileen P., Jenna Haverfield, Rebecca L. Ursin, Cara Tannenbaum, and Sabra L. Klein. “Considering How Biological Sex Impacts Immune Responses and COVID-19 Outcomes.” Nature Reviews Immunology 20, no. 7 (2020): 442-47. doi:10.1038/s41577-020-0348-8.

12. Cornell, Brent. “Lung Tissue.” BioNinja. 2016. Accessed May 31, 2021. https://ib.bioninja.com.au/options/option-d-human-physiology/d6-transport-of-respiratory/lung-tissue.html.

13. Bussani, Rossana, Edoardo Schneider, Lorena Zentilin, Chiara Collesi, Hashim Ali, Luca Braga, Maria Concetta Volpe, Andrea Colliva, Fabrizio Zanconati, Giorgio Berlot, Furio Silvestri, Serena Zacchigna, and Mauro Giacca. “Persistence of Viral RNA, Pneumocyte Syncytia and Thrombosis Are Hallmarks of Advanced COVID-19 Pathology.” EBioMedicine 61 (2020): 103104. doi:10.1016/j.ebiom.2020.103104.

14. Wadman, Meredith. “How Does Coronavirus Kill? Clinicians Trace a Ferocious Rampage through the Body, from Brain to Toes.” Science, 2020. doi:10.1126/science.abc3208.

15. Barton, Lisa M., Eric J. Duval, Edana Stroberg, Subha Ghosh, and Sanjay Mukhopadhyay. “COVID-19 Autopsies, Oklahoma, USA.” American Journal of Clinical Pathology 153, no. 6 (2020): 725-33. doi:10.1093/ajcp/aqaa062.

16. Gaillard, Frank. “Normal Chest X-ray: Radiology Case.” Radiopaedia Blog RSS. Accessed May 31, 2021. https://radiopaedia.org/cases/normal-chest-x-ray?lang=us.Case courtesy of Assoc Prof Frank Gaillard, Radiopaedia.org, rID: 8304

17. Wang, Xingrui, Qinglin Che, Xiaoxiao Ji, Xinyi Meng, Lang Zhang, Rongrong Jia, Hairong Lyu, Weixian Bai, Lingjie Tan, and Yanjun Gao. “Correlation between Lung Infection Severity and Clinical Laboratory Indicators in Patients with COVID-19: A Cross-sectional Study Based on Machine Learning.” BMC Infectious Diseases 21, no. 1 (2021). doi:10.1186/s12879-021-05839-9.

18. Salehi, Sana, Aidin Abedi, Sudheer Balakrishnan, and Ali Gholamrezanezhad. “Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients.” American Journal of Roentgenology 215, no. 1 (2020): 87-93. doi:10.2214/ajr.20.23034.

19. Magro, Cynthia, J. Justin Mulvey, David Berlin, Gerard Nuovo, Steven Salvatore, Joanna Harp, Amelia Baxter-Stoltzfus, and Jeffrey Laurence. “Complement Associated Microvascular Injury and Thrombosis in the Pathogenesis of Severe COVID-19 Infection: A Report of Five Cases.” Translational Research 220 (2020): 1-13. doi:10.1016/j.trsl.2020.04.007.

20. Wichmann, Dominic, Jan-Peter Sperhake, Marc Lütgehetmann, Stefan Steurer, Carolin Edler, Axel Heinemann, Fabian Heinrich, Herbert Mushumba, Inga Kniep, Ann Sophie Schröder, Christoph Burdelski, Geraldine De Heer, Axel Nierhaus, Daniel Frings, Susanne Pfefferle, Heinrich Becker, Hanns Bredereke-Wiedling, Andreas De Weerth, Hans-Richard Paschen, Sara Sheikhzadeh-Eggers, Axel Stang, Stefan Schmiedel, Carsten Bokemeyer, Marylyn M. Addo, Martin Aepfelbacher, Klaus Püschel, and Stefan Kluge. “Autopsy Findings and Venous Thromboembolism in Patients With COVID-19.” Annals of Internal Medicine 173, no. 4 (2020): 268-77. doi:10.7326/m20-2003.

21. Hamblin, James. “Why Some People Get Sicker Than Others.” The Atlantic. August 19, 2020. Accessed May 31, 2021. https://www.theatlantic.com/health/archive/2020/04/coronavirus-immune-response/610228/.

Among Virions

August 20, 2021

By Jordan Chen, Biochemical Engineering ‘24

 

What are viruses? Miniscule packages of protein and genetic material, smaller than all but the smallest cells, relatively simple structures on the boundaries of what we consider living. Undetectable to the human eye, these invisible contagions are rarely on the minds of the average person, occupying a semantic space in public consciousness more often than they are understood for their material reality. Stories are more likely to be described as “viral” than an actual virus, yet when the COVID-19 pandemic washed over the world at the end of 2019, the public suddenly had to confront that which was seemingly abiotic, simple, and small. However, the impact of the COVID-19 pandemic exceeded that unassuming material reality. With the shuttering of the global economy, mass death, political crisis, confusion, hysteria, and science without immediate answers, it’s become clear that the sum of COVID-19’s viral components is much more than the whole.

To emphasize this idea in the piece, coronavirus virions are depicted as massive and detailed larger than earth bodies, in a vital bloody red, surrounding and overwhelming the relatively simply shaded globe. What was formerly small, simple, and nonliving, can now be dramatically understood as larger than life, having created complex predicaments, and having taken on a life of its own in its assault against the world. This digital artwork was created in Blender.

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