The European Nucleotide Archive in 2022.

The European Nucleotide Archive (ENA; https://www.ebi.ac.uk/ena), maintained by the European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), offers those producing data an open and supported platform for the management, archiving, publication, and dissemination of data; and to the scientific community as a whole, it offers a globally comprehensive data set through a host of data discovery and retrieval tools. Here, we describe recent updates to the ENA's submission and retrieval services as well as focused efforts to improve connectivity, reusability, and interoperability of ENA data and metadata.

Multivariate time series short term forecasting using cumulative data of coronavirus.

Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model's effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.

Association of microplastics with heavy metals and antibiotic resistance bacteria/genes in natural ecosystems - A perspective through science mapping approach

Micro and nano-plastics (MNPs) have been considered one of the major emerging contaminants that require immediate attention. Their potential impact on the natural ecosystems is yet to be understood, especially their associations with other contaminants like heavy metals and organisms essential for the sustenance of life, i.e., microbes. Microplastics (MPs) also act as sources and carriers of pollutants, similar to macro and mesoplastics, that leach harmful chemicals such as Polybrominated Diphenyl Ethers (PBDEs), Pharmaceutical and Personal Care Products (PPCPs), Endocrine Disruptive Chemicals (EDCs), etc. They also behave like super sponge materials which adsorb microbes such as antibiotic resistance bacteria (ARBs), and coronavirus, making their concentration much higher than the ambient environment. Among these microbes, heavy metal-resistance (MRGs) and antibiotic-resistance genes (ARGs) carry immense significance. The present study provides an in-depth review analysis of the works published related to the association of MPs to heavy metals and ARGs. 1526 articles were investigated after the dataset was subjected to a three-stage screening process. A scientometric analysis revealing details about the most productive and influential journals, co-authorship details, most influential publications, most cited keywords, and most active countries in the research domain was conducted. This provided significant information regarding various aspects of the published works of literature. Subsequently, a qualitative discussion was carried out wherein a detailed discussion with regard to the trends in research on sub-areas in the broad domain was conducted. This resulted in identifying the gaps in the available literature, which paved the way for providing a framework for future research. Through this study, it is expected that the readers will be exposed to a summary of the overall research that has been conducted to date, and the manuscript will act as a guide for future research.

Molecular Docking and Simulation Analysis of Cyclopeptides as Anticancer Agents

Background: Cancer is a leading cause of death for people worldwide, in addition to the rise in mortality rates attributed to the Covid epidemic. This allows scientists to do additional research. Here, we have selected Integerrimide A, cordy heptapeptide, and Oligotetrapeptide as the three cyclic proteins that will be further studied and investigated in this context.Methods: Docking research was carried out using the protein complexes 1FKB and 1YET, downloaded from the PDB database and used in the docking investigations. Cyclopeptides have been reported to bind molecularly to human HSP90 (Heat shock protein) and FK506. It was possible to locate HSP90 in Protein Data Banks 1YET and 1FKB. HSP90 was retrieved from Protein Data Bank 1YET and 1FKB. Based on these findings, it is possible that the anticancer effects of Int A, Cordy, and Oligo substances could be due to their ability to inhibit the mTOR rapamycin binding domain and the HSP90 Geldanamycin binding domain via the mTOR and mTOR chaperone pathways. During the calculation, there were three stages: system development, energy reduction, and molecular dynamics (also known as molecular dynamics). Each of the three compounds demonstrated a binding affinity for mTOR's Rapamycin binding site that ranged from -6.80 to -9.20 Kcal/mol (FKB12).Results: An inhibition constant Ki of 181.05 nM characterized Cordy A with the highest binding affinity (-9.20 Kcal/mol). Among the three tested compounds, Cordy A was selected for MD simulation. HCT116 and B16F10 cell lines were used to test each compound's anticancer efficacy. Doxorubicin was used as a standard drug. The cytotoxic activity of substances Int A, Cordy A, and Oligo on HCT116 cell lines was found to be 77.65 μM, 145.36 μM, and 175.54 μM when compared to Doxorubicin 48.63 μM, similarly utilizing B16F10 cell lines was found to be 68.63 μM, 127.63 μM, and 139.11 μM to Doxorubicin 45.25 μM.Conclusion: Compound Cordy A was more effective than any other cyclic peptides tested in this investigation.

The 2022 outbreak and the pathobiology of the coxsackie virus [hand foot and mouth disease] in India.

Outbreaks of HFMD in children aged <5 years have been reported worldwide and the major causative agents are Coxsackievirus (CV) A16, enterovirus (EV)-A71 and recently CVA6. In India, HFMD is a disease that is not commonly reported. The purpose of the study was to identify the enterovirus type(s) associated with large outbreak of Hand, foot, and mouth disease during COVID-19 pandemic in 2022. Four hundred and twenty five clinical samples from 196-suspected cases were collected from different parts of the country. This finding indicated the emergence of CVA6 in HFMD along with CVA16, soon after the gradual easing of non-pharmaceutical interventions during-pandemic COVID-19 and the relevance of continued surveillance of circulating enterovirus types in the post-COVID pandemic era.

Tracing COVID-19 Trails in Wastewater: A Systematic Review of SARS-CoV-2 Surveillance with Viral Variants

The emergence of new variants of SARS-CoV-2 associated with varying infectivity, pathogenicity, diagnosis, and effectiveness against treatments challenged the overall management of the COVID-19 pandemic. Wastewater surveillance (WWS), i.e., monitoring COVID-19 infections in communities through detecting viruses in wastewater, was applied to track the emergence and spread of SARS-CoV-2 variants globally. However, there is a lack of comprehensive understanding of the use and effectiveness of WWS for new SARS-CoV-2 variants. Here we systematically reviewed published articles reporting monitoring of different SARS-CoV-2 variants in wastewater by following the PRISMA guidelines and provided the current state of the art of this study area. A total of 80 WWS studies were found that reported different monitoring variants of SARS-CoV-2 until November 2022. Most of these studies (66 out of the total 80, 82.5%) were conducted in Europe and North America, i.e., resource-rich countries. There was a high variation in WWS sampling strategy around the world, with composite sampling (50/66 total studies, 76%) as the primary method in resource-rich countries. In contrast, grab sampling was more common (8/14 total studies, 57%) in resource-limited countries. Among detection methods, the reverse transcriptase polymerase chain reaction (RT-PCR)-based sequencing method and quantitative RT-PCR method were commonly used for monitoring SARS-CoV-2 variants in wastewater. Among different variants, the B1.1.7 (Alpha) variant that appeared earlier in the pandemic was the most reported (48/80 total studies), followed by B.1.617.2 (Delta), B.1.351 (Beta), P.1 (Gamma), and others in wastewater. All variants reported in WWS studies followed the same pattern as the clinical reporting within the same timeline, demonstrating that WWS tracked all variants in a timely way when the variants emerged. Thus, wastewater monitoring may be utilized to identify the presence or absence of SARS-CoV-2 and follow the development and transmission of existing and emerging variants. Routine wastewater monitoring is a powerful infectious disease surveillance tool when implemented globally.

Severe COVID-19-associated hyperinflammatory syndrome versus classic hemophagocytic lymphohistiocytosis: similarities, differences, and the way forward.

The hyperinflammatory immune response in severe COVID-19 infection shares features with secondary hemophagocytic lymphohistiocytosis (sHLH) in the form of fever, cytopenia, elevated inflammatory markers, and high mortality. There are contrasting opinions regarding utility of HLH 2004 or HScore in the diagnosis of severe COVID-19-related hyperinflammatory syndrome (COVID-HIS). This was a retrospective study of 47 patients of severe COVID-19 infection, suspected to have COVID-HIS and 22 patients of sHLH to other illnesses, to evaluate the diagnostic utility and limitations of HLH 2004 and/or HScore in context to COVID-HIS and to also evaluate the utility of Temple criteria for predicting severity and outcome in COVID-HIS. Clinical findings, hematological, and biochemical parameters along with the predictor of mortality were compared between two groups. Only 6.4% (3/47) of cases fulfilled ≥5/8 HLH 2004 criteria and only 40.52% (19/47) of patients showed HScore >169 in COVID-HIS group. 65.9% (31/47) satisfied the Temple criteria in COVID-HIS as compared with 40.9% (9/22) in the non-COVID group (p = 0.04). Serum ferritin (p = 0.02), lactate dehydrogenase (p = 0.02), direct bilirubin (p = 0.02), and C-reactive protein (p = 0.03) were associated with mortality in COVID-HIS. Both HScore and HLH-2004 criteria perform poorly for identifying COVID-HIS. Presence of bone marrow hemophagocytosis may help to identify about one-third of COVID-HIS missed by the Temple Criteria.

Review on distribution, fate, and management of potentially toxic elements in incinerated medical wastes.

Medical wastes include all solid and liquid wastes that are produced during the treatment, diagnosis, and immunisation of animals and humans. A significant proportion of medical waste is infectious, hazardous, radioactive, and contains potentially toxic elements (PTEs) (i.e., heavy metal (loids)). PTEs, including arsenic (As), cadmium (Cd), lead (Pb) and mercury (Hg), are mostly present in plastic, syringes, rubber, adhesive plaster, battery wastes of medical facilities in elemental form, as well as oxides, chlorides, and sulfates. Incineration and sterilisation are the most common technologies adopted for the safe management and disposal of medical wastes, which are primarily aimed at eliminating deadly pathogens. The ash materials derived from the incineration of hazardous medical wastes are generally disposed of in landfills after the solidification/stabilisation (S/S) process. In contrast, the ash materials derived from nonhazardous wastes are applied to the soil as a source of nutrients and soil amendment. The release of PTEs from medical waste ash material from landfill sites and soil application can result in ecotoxicity. The present study is a review paper that aims to critically review the dynamisms of PTEs in various environmental media after medical waste disposal, the environmental and health implications of their poor management, and the common misconceptions regarding medical waste.

Moving forward with COVID-19: Future research prospects of wastewater-based epidemiology methodologies and applications.

Wastewater-based epidemiology (WBE) has been demonstrated for its great potential in tracking of coronavirus disease 2019 (COVID-19) transmission among populations despite some inherent methodological limitations. These include non-optimized sampling approaches and analytical methods; stability of viruses in sewer systems; partitioning/retention in biofilms; and the singular and inaccurate back-calculation step to predict the number of infected individuals in the community. Future research is expected to (1) standardize best practices in wastewater sampling, analysis and data reporting protocols for the sensitive and reproducible detection of viruses in wastewater; (2) understand the in-sewer viral stability and partitioning under the impacts of dynamic wastewater flow, properties, chemicals, biofilms and sediments; and (3) achieve smart wastewater surveillance with artificial intelligence and big data models. Further specific research is essential in the monitoring of other viral pathogens with pandemic potential and subcatchment applications to maximize the benefits of WBE beyond COVID-19.

An analysis of emergency surgery on coronavirus disease positive patients and their outcomes during the coronavirus pandemic: A retrospective observational study

Background The primary objective of this study was to determine the outcome of emergency surgery in coronavirus disease 2019 (COVID-19) patients with regard to presently existing physical status, and highlight its subspecialty distribution. Methods This retrospective observational study was done on all patients who underwent emergency surgery between March 2020 and Dec 2021 and were positive for COVID-19. Data collection included the age of the patients, gender, diagnosis, the type of surgery performed, and outcome. Physical status was assessed, as per Modified Medical Research Council Dyspnoea Scale (MMRC) and Metabolic Equivalent Scale (METS). Results A total of 89 patients were analyzed from March 2020 to Dec 2021. There were 63 females and 26 males. The average age of the males was 53.8 ± 8.9 years and the average age of the females was 29.1 ± 4.6 years. The maximum number of surgeries done was lower segment cesarean section (57.3%). 55 out of 60 (91%) cases had a good grade on the MMRC scale (Grade 0 and 1). 3 patients had Grade 4 MMRC scale and all 3 were oncology cases. As per the METS scale, 56/60 (93.3%) patients had METS >10. Conclusion This study has demonstrated that 55 out of 60 (91%) of cases had a good grade on the MMRC scale (Grade 0 and 1) 6 months to 1-year post-surgery. As per the METS scale, 56/60 (93.3%) patients had METS >10. Most of the cases were asymptomatic COVID-19-positive and presently have good physical status as determined by the study.

CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images

Purpose COVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason. Methods We aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe. Results An accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures. Conclusion We strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation.

Pollution free UV-C radiation to mitigate COVID-19 transmission.

The high rate of transmission of the COVID-19 virus has brought various types of disinfection techniques, for instance, hydrogen peroxide vaporization, microwave generating steam, UV radiation, and dry heating, etc. to prevent the further transmission of the virus. The chemical-based techniques are predominantly used for sanitization of hands, buildings, hospitals, etc. However, these chemicals may affect the health of humans and the environment in unexplored aspects. Furthermore, the UV lamp-based radiation sanitization technique had been applied but has not gained larger acceptability owing to its limitation to penetrate different materials. Therefore, the optical properties of materials are especially important for the utilization of UV light on such disinfection applications. The germicidal or microorganism inactivation application of UV-C has only been in-use in a closed chamber, due to its harmful effect on human skin and the eye. However, it is essential to optimize UV for its use in an open environment for a larger benefit to mitigate the virus spread. In view of this, far UV-C (222nm) based technology has emerged as a potential option for the sanitization in open areas and degradation of microorganisms present in aerosol during the working conditions. Hence, in the present review article, efforts have been made to evaluate the technical aspects of UV (under the different spectrum and wavelength ranges) and the control of COVID 19 virus spread in the atmosphere including the possibilities of the human body sanitization in working condition.

T-cell dysfunction as a potential contributing factor in post-COVID-19 mucormycosis.

BACKGROUND: The second wave of COVID-19 in India was followed by large number of mucormycosis cases. Indiscriminate use of immunosuppressive drugs, underlying diseases such as diabetes, cancers, or autoimmune diseases was thought to be the cause. However, the mortality was not as high as that seen in non-COVID mucormycosis. OBJECTIVE: To study the detailed characteristics of T-cells for evaluating the underlying differences in the T-cell immune dysfunction in post-COVID and non-COVID mucor patients. MATERIAL AND METHOD: The study included histopathologically confirmed cases of mucor (13 post-COVID, 13 non-COVID) and 15 healthy individuals (HI). Expression of T-cell activation (CD44, HLADR, CD69, CD38) and exhaustion (CTLA, PD-1, LAG-3 and TIM-3) markers was evaluated by flow cytometry. RESULTS: All cases showed significant depletion of T-cells compared to HI. Both post-COVID and non-COVID groups showed increased activation and exhaustion as compared to HI. Non-COVID mucor group showed significant activation of CD4+ T cells for HLADR and CD38 (p = .025, p = .054) and marked T-cell exhaustion in form of expression of LAG-3 on both CD4+ T and CD8+ T cells in comparison with post-COVID patients (p = .011, p = .036). Additionally, co-expression of PD-1 & LAG-3 and LAG-3 & TIM-3 on CD8+ T cells was statistically significant in non-COVID mucor patients (p = .016, p = .027). CONCLUSION: Immunosuppression in non-COVID mucor showed pronounced exhaustion of T-cells in comparison to post-COVID mucor cases implicating T-cell immune dysfunction is much more severe in non-COVID mucor which are in a state of continuous activation followed by extreme exhaustion leading to poorer outcome.

An analysis of emergency surgery on coronavirus disease positive patients and their outcomes during the coronavirus pandemic: A retrospective observational study.

Background: The primary objective of this study was to determine the outcome of emergency surgery in coronavirus disease 2019 (COVID-19) patients with regard to presently existing physical status, and highlight its subspecialty distribution. Methods: This retrospective observational study was done on all patients who underwent emergency surgery between March 2020 and Dec 2021 and were positive for COVID-19. Data collection included the age of the patients, gender, diagnosis, the type of surgery performed, and outcome. Physical status was assessed, as per Modified Medical Research Council Dyspnoea Scale (MMRC) and Metabolic Equivalent Scale (METS). Results: A total of 89 patients were analyzed from March 2020 to Dec 2021. There were 63 females and 26 males. The average age of the males was 53.8 ± 8.9 years and the average age of the females was 29.1 ± 4.6 years. The maximum number of surgeries done was lower segment cesarean section (57.3%). 55 out of 60 (91%) cases had a good grade on the MMRC scale (Grade 0 and 1). 3 patients had Grade 4 MMRC scale and all 3 were oncology cases. As per the METS scale, 56/60 (93.3%) patients had METS >10. Conclusion: This study has demonstrated that 55 out of 60 (91%) of cases had a good grade on the MMRC scale (Grade 0 and 1) 6 months to 1-year post-surgery. As per the METS scale, 56/60 (93.3%) patients had METS >10. Most of the cases were asymptomatic COVID-19-positive and presently have good physical status as determined by the study.

Intensive critical care and management of asthmatic and smoker patients in COVID-19 infection.

This century's most serious catastrophe, COVID-19, has been dubbed "the most life-threatening disaster ever". Asthmatic persons are even more prone to COVID-19's complex interplay with the underlying inflammatory condition. In order to protect themselves against COVID-19, asthmatic patients must be very vigilant in their usage of therapeutic techniques and drugs (e.g., bronchodilators, 5-lipoxygenase inhibitors), which may be accessed to deal with mild, moderate, and severe COVID-19 indications. People with asthma may have more severe COVID-19 symptoms, which may lead to a worsening of their condition. Several cytokines were found to be elevated in the bronchial tracts of patients with acute instances of COVID-19, suggesting that this ailment may aggravate asthma episodes by increasing inflammation. The intensity of COVID-19 symptoms is lessened in patients with asthma who have superior levels of T-cells. Several antibiotics, antivirals, antipyretics, and anti-inflammatory drugs have been suggested to suppress COVID-19 symptoms in asthmatic persons. Furthermore, smokers are more likely to have aggravated repercussions in COVID-19 infection. Being hospitalized to critical care due to COVID-19, needing mechanical breathing, and suffering from serious health repercussions, are all possible outcomes for someone who has previously smoked. Smoking damages airways and alveoli, which significantly raises the risk of COVID-19-related health complications. Patients with a previous record of smoking are predisposed to severe COVID-19 disease symptoms that essentially require a combination of bronchodilators, mucolytics, antivirals, and antimuscarinic drugs, to cope with the situation. The present review discusses the care and management of asthmatic and smoker patients in COVID-19 infection.

Analysis and forecasting of air quality index based on satellite data.

OBJECTIVE: The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. MATERIALS AND METHODS: Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). RESULTS: AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. CONCLUSIONS: Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone.

Purpose of Review: Fine particulate matter (PM2.5) and ground-level ozone (O3) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM2.5 and O3 are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM2.5 and O3. Recent Findings: Global warming has led to a general increase in biogenic emissions, which can enhance the formation of O3 and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM2.5; however, O3 was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O3 to NO x . Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions. Summary: Atmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.

Single-cell multiomics revealed the dynamics of antigen presentation, immune response and T cell activation in the COVID-19 positive and recovered individuals.

Introduction: Despite numerous efforts to describe COVID-19's immunological landscape, there is still a gap in our understanding of the virus's infections after-effects, especially in the recovered patients. This would be important to understand as we now have huge number of global populations infected by the SARS-CoV-2 as well as variables inclusive of VOCs, reinfections, and vaccination breakthroughs. Furthermore, single-cell transcriptome alone is often insufficient to understand the complex human host immune landscape underlying differential disease severity and clinical outcome. Methods: By combining single-cell multi-omics (Whole Transcriptome Analysis plus Antibody-seq) and machine learning-based analysis, we aim to better understand the functional aspects of cellular and immunological heterogeneity in the COVID-19 positive, recovered and the healthy individuals. Results: Based on single-cell transcriptome and surface marker study of 163,197 cells (124,726 cells after data QC) from the 33 individuals (healthy=4, COVID-19 positive=16, and COVID-19 recovered=13), we observed a reduced MHC Class-I-mediated antigen presentation and dysregulated MHC Class-II-mediated antigen presentation in the COVID-19 patients, with restoration of the process in the recovered individuals. B-cell maturation process was also impaired in the positive and the recovered individuals. Importantly, we discovered that a subset of the naive T-cells from the healthy individuals were absent from the recovered individuals, suggesting a post-infection inflammatory stage. Both COVID-19 positive patients and the recovered individuals exhibited a CD40-CD40LG-mediated inflammatory response in the monocytes and T-cell subsets. T-cells, NK-cells, and monocyte-mediated elevation of immunological, stress and antiviral responses were also seen in the COVID-19 positive and the recovered individuals, along with an abnormal T-cell activation, inflammatory response, and faster cellular transition of T cell subtypes in the COVID-19 patients. Importantly, above immune findings were used for a Bayesian network model, which significantly revealed FOS, CXCL8, IL1ß, CST3, PSAP, CD45 and CD74 as COVID-19 severity predictors. Discussion: In conclusion, COVID-19 recovered individuals exhibited a hyper-activated inflammatory response with the loss of B cell maturation, suggesting an impeded post-infection stage, necessitating further research to delineate the dynamic immune response associated with the COVID-19. To our knowledge this is first multi-omic study trying to understand the differential and dynamic immune response underlying the sample subtypes.

Leveraging PEPFAR-Supported Health Information Systems for COVID-19 Pandemic Response.

Since 2003, the US President's Emergency Plan for AIDS Relief (PEPFAR) has supported implementation and maintenance of health information systems for HIV/AIDS and related diseases, such as tuberculosis, in numerous countries. As the COVID-19 pandemic emerged, several countries conducted rapid assessments and enhanced existing PEPFAR-funded HIV and national health information systems to support COVID-19 surveillance data collection, analysis, visualization, and reporting needs. We describe efforts at the US Centers for Disease Control and Prevention (CDC) headquarters in Atlanta, Georgia, USA, and CDC country offices that enhanced existing health information systems in support COVID-19 pandemic response. We describe CDC activities in Haiti as an illustration of efforts in PEPFAR countries. We also describe how investments used to establish and maintain standards-based health information systems in resource-constrained settings can have positive effects on health systems beyond their original scope.