Corrigendum: Impact of a tobacco sales ban on the frequency of tobacco consumption in India during the COVID-19 pandemic.

[This corrects the article DOI: 10.18332/tid/161855.].

Epidemiological pattern of COVID-19 and its association with periodontal health in an urban Indian cohort.

Background: Studies have highlighted a possible influence of gingival and periodontal disease (PD) on COVID-19 risk and severity. However, the evidence is based on hospital-based studies and community-level data are sparse. Objectives: We described the epidemiological pattern of SARS-CoV-2 infection in Delhi and evaluated the associations of gingival and PD with incident COVID-19 disease in a regionally representative urban Indian population. Methods: In a prospective study nested within the Centre for Cardiometabolic Risk Reduction in South-Asia (CARRS) study, participants with clinical gingival and periodontal status available at baseline (2014-16) (n = 1,727) were approached between October 2021 to March 2022. Information on COVID-19 incidence, testing, management, severity was collected as per the WHO case criteria along with COVID-19 vaccination status. Absolute incidence of COVID-19 disease was computed by age, sex, and oral health. Differences in rates were tested using log-rank test. Poisson regression models were used to evaluate independent associations between gingival and PD and incidence of COVID-19, adjusted for socio-demographic and behavioral factors, presence of comorbidity, and medication use. Results: Among 1,727 participants, the mean age was 44.0 years, 45.7% were men, 84.5% participants had baseline gingival or PD and 89.4% participants had received at least one dose of COVID-19 vaccine. Overall, 35% (n = 606) participants were tested for COVID-19 and 24% (n = 146/606) tested positive. As per the WHO criteria total number of cases was 210, constituting 12% of the total population. The age and sex-specific rates of COVID-19 were higher among men and older participants, but women aged >60 years had higher rates than men of same age. The incidence rate did not differ significantly between those having gingival or PD and healthy periodontium (19.1 vs. 16.5/1,000 person-years) and there was no difference in risk of COVID-19 by baseline oral disease status. Conclusion: Gingival and PD were not associated with increased risk of COVID-19.

Integrated Quantitative Systems Pharmacology (QSP) Characterizing Viral Dynamics After Intramuscular (IM) Adintrevimab (ADI) Administration in Participants with Mild to Moderate Coronavirus Disease (COVID-19)

Background. ADI is a fully human IgG1 monoclonal antibody engineered to have an extended half-life with high potency and broad neutralization against SARS-CoV-2 and other SARS-like coronaviruses. The goal of our analysis was to develop a QSP model in which ADI concentrations in upper airway (UA) epithelial lining fluid (ELF) were linked to a viral dynamic model to describe the impact of ADI on SARS-CoV-2 viral load relative to placebo. Methods. The QSP model was fit inNONMEMVersion 7.4 using PK data from a Phase 1 study (N=24, IV and IM) and from Phase 2/3 COVID-19 prevention (EVADE;N=659, IM) and treatment (STAMP;N=189, IM) studies. Saliva and NP samples were collected from STAMP study participants (pts) infected with the delta or omicron variants. The viral dynamic model was based on a published model and was modified to include both active (V) and deactivated (DV) virus (Fig). The viral dynamic model was fit to the NP swab viral load data (2 samples/pt) standardized to time since infection based upon recorded symptom onset. Saliva data (7-8 samples/ pt) was fit sequentially using a biophase compartment given the peak viral load was modestly lower and peaked later than Day 1. Viral dynamic model (A) and simulated median (90% PI) NP viral load reduction in ADI-treated or placebo participants for delta (B) and omicron (C) variants Results. The QSP model provided an excellent fit to serum ADI concentrationtime data after estimation of a transit rate to account for IM absorption, plasma volume, and the ADI-neonatal Fc receptor dissociation rate constant. The linked viral dynamic model captured the NP swab viral load data after estimating differences in within-host replication factor (R0) and viral production rate (p) by variant. Maximal ADI-induced effect (Smax) on stimulating viral clearance (c) was fixed to 0.43 based upon prior modeling. ADI concentration in UA ELF resulting in 50% of Smax (SC50) was estimated to be 0.086 for delta and 1.05 mg/L for omicron. Figure B and C show model-based simulated median (90% PI) viral load reduction in ADI-treated or placebo pts for delta and omicron variants. Conclusion. This QSP model, in conjunction with information on new variants available early in outbreaks (IC50, infectivity (R0), viral production rate [each a model parameter]), allows for rapid dose identification in response to emerging variants.

Clinical and Virologic Outcomes with Early Adintrevimab (ADI) Monoclonal Antibody Therapy in Mild and Moderate COVID-19

Background. Peak SARS-CoV-2 viral replication occurs in the upper respiratory tract in presymptomatic and early symptomatic phases. Administration of a monoclonal antibody may be most beneficial in the early time period immediately after symptom onset. Here we describe the effect of early therapy on efficacy in patients receiving ADI. Methods. High risk patients with mild or moderate COVID-19 were enrolled in the ADI treatment study (STAMP), with primary endpoint of COVID-19 related hospitalization or all-cause death through Day 29 in patients with disease due to confirmed or suspected SARS-CoV-2 variants other than Omicron. Patients were randomized 1:1 to receive ADI or placebo administered by a single intramuscular (IM) injection. For this subgroup analysis, patients that had received therapy within 3 days of symptom onset were evaluated. Results. In the overall population, the study met the primary endpoint demonstrating 66% relative risk reduction of COVID-19 hospitalization or all cause death in 336 patients. Among 261 patients receiving therapy within 3 days of symptom onset (n=133 ADI, n=128 placebo), ADI was associated with a statistically significant reduction in the risk of COVID-19-related hospitalization or all-cause death through Day 29 compared with placebo (4 [3%] vs. 15 [11.7%], standardized risk difference -8%, 95% CI: -14.11, -1.86, p=0.0106), demonstrating a 72% standardized relative risk reduction in favor of ADI. When given as early therapy, ADI provided a greater reduction in viral load from baseline to Day 5 compared with placebo as assessed by saliva samples, with an adjusted least-squares mean difference of -0.97 log10 copies/mL (95% CI: -1.540, -0.391;p=0.0011). No study drug related SAEs, including deaths, and no hypersensitivity reactions were reported. Conclusion. Early therapy with a single dose of ADI 300 mg IM provided a 72% reduction in the risk of COVID-19 related hospitalization and all-cause death compared to placebo in high-risk ambulatory patients with mild to moderate COVID-19. Therapy within the first 3 days also led to a greater reduction in viral load compared to placebo and favorable outcomes in patients who are at high risk for progression of disease.

Higher Doses of Adintrevimab, an Extended Half-Life Monoclonal Antibody, for the Treatment and Prevention of COVID-19: Preliminary Results from a Phase 1 Single Ascending-Dose Study

Background. Adintrevimab is a fully human IgG1 monoclonal antibody engineered to have potent and broad neutralization against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other SARS-like CoVs with pandemic potential. Adintrevimab is being assessed in two separate phase 2/3 clinical trials: the EVADE trial for prevention of COVID-19 in both post-exposure and preexposure settings and the STAMP trial for treatment of COVID-19. Here we report higher doses being evaluated in a healthy volunteer study given that emerging variants may have varying susceptibilities to adintrevimab. Previous results 300 mg IM, 600 mg IM, and 500 mg IV cohorts have been reported. Methods. This is an ongoing Phase 1, randomized, placebo (PBO)-controlled, single ascending-dose study of adintrevimab administered intramuscularly (IM) or intravenously (IV) to healthy adults aged 18-50 years with no current SARS-CoV-2 infection. Participants were randomized 8:2 in 3 high dose cohorts (N=10/cohort: n=8 adintrevimab, n=2 PBO): adintrevimab 1200 mg IM, 1200 mg IV, and 4500 mg IV. Safety, tolerability, and pharmacokinetics (PK) were assessed up to 21 days post dose. Results. Overall, 30 participants received adintrevimab (n=24) or PBO (n=6). Blinded safety data for all cohorts and PK for 1200 mg IV are reported. Through 21 days post dose all doses were well-tolerated, with no study drug-related adverse events (AEs), serious AEs, injection site reactions, or hypersensitivity reactions reported. The observed PK profile of the 1200 mg IV dose included Cmax of 423+/-105 mug/ml. Comparison of 500 mg and 1200 mg IV doses indicate dose proportionality of Cmax and exposure (AUC Day 21). Conclusion. A single dose of adintrevimab, up to 4500 mg, was well tolerated. These preliminary safety data and PK support potential use of higher doses of adintrevimab as needed to address emerging SARS-CoV-2 variants.

Using Epidemic Modeling, Machine Learning and Control Feedback Strategy for Policy Management of COVID-19

Coronavirus disease (COVID-19) is one of the world's most challenging pandemics, affecting people around the world to a great extent. Previous studies investigating the COVID-19 pandemic forecast have either lacked generalization and scalability or lacked surveillance data. City administrators have also often relied heavily on open-loop, belief-based decision-making, preventing them from identifying and enforcing timely policies. In this paper, we conduct mathematical and numerical analyses based on closed-loop decisions for COVID-19. Combining epidemiological theories with machine learning models gives this study a more accurate prediction of COVID-19's growth, and suggests policies to regulate it. The Susceptible, Infectious, and Recovered (SIR) model was analyzed using a machine learning model to estimate the optimal constant parameters, which are the recovery and infection rates of the coupled nonlinear differential equations that govern the epidemic model. To modulate the optimized parameters that regulate pandemic suppression and mitigation, a systematically designed feedback-based strategy was implemented. We also used pulse width modulation to modify on-off signals in order to regulate policy enforcement according to established metrics, such as infection recovery ratios. It was possible to determine what type of policy should be implemented in the country, as well as how long it should be implemented. Using datasets from John Hopkins University for six countries, India, Iran, Italy, Germany, Japan, and the United States, we show that our 30-day prediction errors are almost less than 3%. Our model proposes a threshold mechanism for policy control that divides the policy implementation into seven states, for example, if Infection Recovery Ratio (IRR) >80, we suggest a complete lockdown, vs if 10 ¡IRR ¡20, we suggest encouraging people to stay at home and organizations to work at 50% capacity. All countries which implemented a policy control strategy at an early stage were accurately predicted by our model. Furthermore, it was determined that the implementation of closed-loop strategies during a pandemic at different times effectively controlled the pandemic.

RL Based Unsupervised Video Summarization Framework for Ultrasound Imaging

The need for summarizing long medical scan videos for automatic triage in Emergency Departments and transmission of the summarized videos for telemedicine has gained significance during the COVID-19 pandemic. However, supervised learning schemes for summarizing videos are infeasible as manual labeling of scans for large datasets is impractical by frontline clinicians. This work presents a methodology to summarize ultrasound videos using completely unsupervised learning schemes and is validated on Lung Ultrasound videos. A Convolutional Autoencoder and a Transformer decoder is trained in an unsupervised reinforcement learning setup i.e., without supervised labels in the whole workflow. Novel precision and recall computation for ultrasound videos is also presented employing which high Precision and F1 scores of 64.36% and 35.87% with an average video compression rate of 78% is obtained when validated against clinically annotated cases. Even though demonstrated using lung ultrasound videos, our approach can be readily extended to other imaging modalities. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Preliminary Results from a Phase 1 Single Ascending-Dose Study Assessing Safety, Serum Viral Neutralizing Antibody Titers (sVNA), and Pharmacokinetic (PK) Profile of ADG20: An Extended Half-Life Monoclonal Antibody Being Developed for the Treatment and Prevention of Coronavirus Disease (COVID-19)

Background. ADG20 is a fully human IgG1 monoclonal antibody engineered to have high potency and broad neutralization against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other SARS-like CoVs with pandemic potential by binding to a highly conserved epitope in the receptor-binding domain (RBD) of the spike protein. The Fc region of ADG20 has been modified to provide an extended half-life. ADG20 is in clinical development for the treatment and prevention of COVID-19. Methods. This is an ongoing Phase 1, randomized, placebo (PBO)-controlled, single ascending-dose study of ADG20 administered intramuscularly (IM) or intravenously (IV) to healthy adults aged 18-50 years with no evidence of prior or current SARS-CoV-2 infection. Participants were randomized 8:2 in 3 cohorts (N=10/cohort: n=8 ADG20, n=2 PBO): ADG20 300 mg IM, 500 mg IV, and 600 mg IM. Safety, tolerability, PK, and sVNA titers were assessed up to 3 months post dose. Serum ADG20 concentrations were measured with a validated hybrid ligand binding liquid chromatography-mass spectrometry (MS)/MS assay. sVNA titers against authentic SARS-CoV-2 were determined by a plaque reduction neutralization assay. Results. Overall, 30 participants received ADG20 (n=24) or PBO (n=6). Blinded safety data for all cohorts and PK/sVNA titer data for the 300 mg IM cohort are reported. Through a minimum of 10 weeks post dose, no study drug-related adverse events (AEs), serious AEs, injection site reactions, or hypersensitivity reactions were reported. The observed preliminary PK profile was dose proportional, consistent with an extended half-life monoclonal antibody, and well predicted by translational physiologically-based PK modeling. The measured 50% sVNA titer (MN50;geometric mean [coefficient of variation, %]) was 1382 (32.7%) 13 days after a single 300 mg IM dose. These values are within the range of peak serum neutralizing antibody titers reported for COVID-19 mRNA vaccines. Conclusion. A single dose of ADG20, up to 600 mg IM, was well tolerated. Preliminary PK and sVNA titer profiles support the ongoing Phase 2/3 trials of ADG20 at a 300 mg IM dose for the prevention of COVID-19 (EVADE: NCT04859517) and treatment of ambulatory patients with mild to moderate COVID-19 (STAMP: NCT04805671).

Use of a Whole-Body Quantitative System Pharmacology Physiologically-Based Pharmacokinetic (QSP/PBPK) Model to Support Dose Selection of ADG20: An Extended Half-Life Monoclonal Antibody Being Developed for the Prevention of Coronavirus Disease (COVID-19)

Background. ADG20 is a fully human IgG1 monoclonal antibody engineered to have potent and broad neutralization against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other SARS-like CoVs with pandemic potential as well as an extended-half-life. ADG20 is administered intramuscularly (IM). A QSP/PBPK model was constructed to support dose selection for a COVID-19 Phase 2/3 prevention trial (EVADE: NCT04859517). Methods. A QSP/PBPK model and a CDC reference adult body weight distribution (45-150 kg) were used to simulate 1000 concentration-time profiles for candidate single-dose regimens of ADG20 (150-450 mg IM). As serum virus neutralizing antibody (sVNA) titers are reportedly a key correlate of protection from COVID-19, a regression equation between time-matched serum ADG20 concentrations (following a 300 mg IM dose) and sVNA titers was developed using measured titers against authentic SARS-CoV-2 determined by a plaque reduction neutralization assay. Projected ADG20 serum concentrations relative to neutralization potency in vitro (90% inhibitory concentration [IC90]) for authentic SARSCoV-2 were also evaluated. Results. The measured 50% neutralization titer (MN50;geometric mean [coefficient of variation, %]) was 1382 (32.7%) 13 days after a single 300 mg IM dose of ADG20. This was within the range of peak sVNA titers reported for COVID-19 vaccine recipients. Using the linear equation relating serum ADG20 concentration to time matched individual MN50 titers and the QSP/PBPK median PK prediction, the anticipated median MN50 exceeded the threshold for protection from SARS-CoV-2 infection established in a non-human primate adoptive transfer model for up to 52 weeks. Based on the QSP/PBPK median PK prediction, median ADG20 serum concentrations are projected to remain >100-fold above the ADG20 IC90 value of 0.011 mg/L against authentic SARS-CoV-2 for up to 52 weeks (Figure). Conclusion. Following administration of a single 300 mg IM dose, sVNA titers and concentrations of ADG20 are projected to remain above thresholds anticipated to be required for protection against COVID-19 for up to 52 weeks. These data support the evaluation of a single ADG20 300 mg IM dose for the prevention of COVID-19.

EMPLOYING ACOUSTIC FEATURES TO AID NEURAL NETWORKS TOWARDS PLATFORM AGNOSTIC LEARNING IN LUNG ULTRASOUND IMAGING

With the recent outbreak of COVID-19, ultrasound is fast becoming an inevitable diagnostic tool for regular and continuous monitoring of the lung. However, lung ultrasound (LUS) is unique in the perspective that, the artefacts created by acoustic wave propagation is aiding clinicians in diagnosis. In this work, a novel approach is presented to extract acoustic wave propagation driven features such as acoustic shadows, local phase-based feature symmetry, and integrated backscattering to automatically detect the pleura and to aid a pretrained neural network to classify the severity of lung infection based on the region below pleura. A detailed analysis of the proposed approach on LUS images over the infection to full recovery period of ten confirmed COVID-19 subjects across 400 videos shows an average five-fold cross-validation accuracy, sensitivity, and specificity of 97%, 92%, and 98% respectively over randomly selected 5000 frames. The results and analysis show that, when the input dataset is limited and diverse as in the case of COVID-19 pandemic, an aided effort of combining acoustic propagation-based features along with the gray scale images, as proposed in this work, improves the performance of the neural network significantly even when tested against a completely new data acquisition. © 2021 IEEE.

Perceiving Machine Learning Algorithms to Analyze COVID-19 Radiographs

Coronavirus disease, also referred to as COVID-19, is a contagious illness generated by a respiratory virus. There has been an exponential increase with the amount of patients affected with COVID-19 that has put an exceptional burden on the medical care frameworks across the world. Analysis of COVID-19 disease from the images of Chest X-ray may help isolate high-risk patients, while test results are anticipated upon. With most X-ray frameworks currently digitized, there is no transportation time required for the samples, hence making it easier for the health care workers to analyze it. In this work, we demonstrate the potential of ResNet, which is a CNN, to diagnose Chest X-ray images. These images can be classified into Normal, COVID, or Viral Pneumonia efficiently using ResNet. As a result, the probability of detecting patients with COVID-19 is maximized through higher accuracy. Empirical analysis exhibits that the proposed neural network strategy is better than Support Vector Machine, Naive Bayes algorithm, Logistic Regression, and k-NN. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 and tobacco cessation: lessons from India.

OBJECTIVES: The Government of India prohibited the sale of tobacco products during the COVID-19 lockdown to prevent the spread of the SARS-CoV-2 virus. This study assessed the tobacco cessation behaviour and its predictors among adult tobacco users during the initial COVID-19 lockdown period in India. METHODS: A cross-sectional study was conducted with 801 adult tobacco users (both smoking and smokeless tobacco) in two urban metropolitan cities of India over a 2-month period (July to August 2020). The study assessed complete tobacco cessation and quit attempts during the lockdown period. Logistic and negative binomial regression models were used to study the correlates of tobacco cessation and quit attempts, respectively. RESULTS: In total, 90 (11.3%) tobacco users reported that they had quit using tobacco after the COVID-19 lockdown period. Overall, a median of two quit attempts (interquartile range 0-6) was made by tobacco users. Participants with good knowledge on the harmful effects of tobacco use and COVID-19 were significantly more likely to quit tobacco use (odds ratio [OR] 2.2; 95% confidence interval [CI] 1.2-4.0) and reported more quit attempts (incidence risk ratio 5.7; 95% CI 2.8-11.8) compared to those with poor knowledge. Participants who had access to tobacco products were less likely to quit tobacco use compared to those who had no access (OR 0.3; 95% CI 0.2-0.5]. CONCLUSIONS: Access restrictions and correct knowledge on the harmful effects of tobacco use and COVID-19 can play an important role in creating a conducive environment for tobacco cessation among users.

Higher rates of diabetic ketoacidosis and admission to the paediatric intensive care unit among newly diagnosed children with type 1 diabetes in Kuwait during the COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic might have a multifaceted effect on children with type 1 diabetes (T1D), either directly through infection itself or indirectly due to measures implemented by health authorities to control the pandemic. OBJECTIVE: To compare data on children newly diagnosed with T1D in Kuwait during the COVID-19 pandemic to the pre-pandemic period. RESEARCH DESIGN AND METHODS: We analysed data on children aged 12 years or less registered in the Childhood-Onset Diabetes electronic Registry (CODeR) in Kuwait. Data were incidence rate (IR), diabetic ketoacidosis (DKA), and its severity and admission to the paediatric intensive care unit (PICU). RESULTS: The IR of T1D was 40.2 per 100,000 (95% CI; 36.0-44.8) during the COVID-19 pandemic period and was not statistically different from pre-pandemic. A higher proportion of incident T1D cases presented with DKA and were admitted to the PICU during the pandemic (52.2% vs. 37.8%: p Ë‚ 0.001, 19.8% vs. 10.9%; p = 0.002, respectively). The COVID-19 pandemic was positively associated with presentation of DKA and admission to PICU (AOR = 1.73; 95% CI, 1.13-2.65; p = 0.012, AOR = 2.04; 95% CI, 1.13-3.67; p = 0.018, respectively). Children of families with a positive history for diabetes were less likely to present with DKA and get admitted to the PICU during the COVID-19 pandemic (AOR = 0.38; 95% CI, 0.20-0.74; p = 0.004, AOR = 0.22; 95% CI, 0.08-0.61; p = 0.004, respectively). CONCLUSION: High rates of DKA at presentation and admission to PICU in incident T1D cases during the COVID-19 pandemic warrant further studies and effective mitigation efforts through increasing awareness, early detection, and timely intervention.

Accelerated antibody discovery targeting the SARS-CoV-2 spike protein for COVID-19 therapeutic potential.

BACKGROUND: Rapid deployment of technologies capable of high-throughput and high-resolution screening is imperative for timely response to viral outbreaks. Risk mitigation in the form of leveraging multiple advanced technologies further increases the likelihood of identifying efficacious treatments in aggressive timelines. METHODS: In this study, we describe two parallel, yet distinct, in vivo approaches for accelerated discovery of antibodies targeting the severe acute respiratory syndrome coronavirus-2 spike protein. Working with human transgenic Alloy-GK mice, we detail a single B-cell discovery workflow to directly interrogate antibodies secreted from plasma cells for binding specificity and ACE2 receptor blocking activity. Additionally, we describe a concurrent accelerated hybridoma-based workflow utilizing a DiversimAb™ mouse model for increased diversity. RESULTS: The panel of antibodies isolated from both workflows revealed binding to distinct epitopes with both blocking and non-blocking profiles. Sequence analysis of the resulting lead candidates uncovered additional diversity with the opportunity for straightforward engineering and affinity maturation. CONCLUSIONS: By combining in vivo models with advanced integration of screening and selection platforms, lead antibody candidates can be sequenced and fully characterized within one to three months.

Overcoming the Digital Divide in the Post-COVID-19 "Reset": Enhancing Group Virtual Visits with Community Health Workers.

The COVID-19 pandemic created numerous barriers to the implementation of participant-facing research. For most, the pandemic required rapid transitioning to all virtual platforms. During this pandemic, the most vulnerable populations are at highest risk of falling through the cracks of engagement in clinical care and research. Nonetheless, we argue that we should reframe the discussion to consider how this transition may create opportunities to engage extensively to reach populations. Here, we present our experience in Atlanta (Georgia, United States) in transitioning a group visit model for South Asian immigrants to a virtual platform and the pivotal role community members in the form of community health workers can play in building capacity among participants. We provide details on how this model helped address common barriers to group visit models in clinical practice and how our community health worker team innovatively addressed the digital challenges of working with an elderly population with limited English proficiency.

Patient experiences and perceptions of chronic disease care during the COVID-19 pandemic in India: a qualitative study.

OBJECTIVE: People with chronic conditions are known to be vulnerable to the COVID-19 pandemic. This study aims to describe patients' lived experiences, challenges faced by people with chronic conditions, their coping strategies, and the social and economic impacts of the COVID-19 pandemic. DESIGN, SETTING AND PARTICIPANTS: We conducted a qualitative study using a syndemic framework to understand the patients' experiences of chronic disease care, challenges faced during the lockdown, their coping strategies and mitigators during the COVID-19 pandemic in the context of socioecological and biological factors. A diverse sample of 41 participants with chronic conditions (hypertension, diabetes, stroke and cardiovascular diseases) from four sites (Delhi, Haryana, Vizag and Chennai) in India participated in semistructured interviews. All interviews were audio recorded, transcribed, translated, anonymised and coded using MAXQDA software. We used the framework method to qualitatively analyse the COVID-19 pandemic impacts on health, social and economic well-being. RESULTS: Participant experiences during the COVID-19 pandemic were categorised into four themes: challenges faced during the lockdown, experiences of the participants diagnosed with COVID-19, preventive measures taken and lessons learnt during the COVID-19 pandemic. A subgroup of participants faced difficulties in accessing healthcare while a few reported using teleconsultations. Most participants reported adverse economic impact of the pandemic which led to higher reporting of anxiety and stress. Participants who tested COVID-19 positive reported experiencing discrimination and stigma from neighbours. All participants reported taking essential preventive measures. CONCLUSION: People with chronic conditions experienced a confluence (reciprocal effect) of COVID-19 pandemic and chronic diseases in the context of difficulty in accessing healthcare, sedentary lifestyle and increased stress and anxiety. Patients' lived experiences during the pandemic provide important insights to inform effective transition to a mixed realm of online consultations and 'distanced' physical clinic visits.

Accelerated Antibody Discovery Targeting the SARS-CoV-2 Spike Protein for COVID-19 Therapeutic Potential (preprint)

Rapid deployment of technologies capable of high-throughput and high-resolution screening is imperative for timely response to viral outbreaks. Risk mitigation in the form of leveraging multiple advanced technologies further increases the likelihood of identifying efficacious treatments in an aggressive timeline. In this study, we describe two parallel, yet distinct, in vivo approaches for accelerated discovery of antibodies targeting the SARS-CoV-2 spike protein. Working with human transgenic Alloy-GK mice, we detail a single B-cell discovery workflow to directly interrogate antibodies secreted from plasma cells for binding specificity and ACE2 receptor blocking activity. Additionally, we describe a concurrent accelerated hybridoma-based workflow utilizing a DiversimAb mouse model for increased diversity. The panel of antibodies isolated from both workflows revealed binding to distinct epitopes with both blocking and non-blocking profiles. Sequence analysis of the resulting lead candidates uncovered additional diversity with the opportunity for straightforward engineering and affinity maturation. By combining in vivo models with advanced integration of screening and selection platforms, lead antibody candidates can be sequenced and fully characterized within one to three months.