Surrogates' experience during the COVID-19 pandemic: mental health, social support, and relationship with intended parents.

PURPOSE: To examine surrogates' mental health, social support, and relationship with intended parents (IPs) during the COVID-19 pandemic from March 2020 to February 2022. METHODS: Data were collected between April 29, 2022 and July 31, 2022, at an academic IVF center in Canada using an 85-item online anonymous cross-sectional survey that included three standardized scales measuring mental health (PHQ-4), loneliness, and social support. Eligible surrogates actively involved in surrogacy during the study period received email invitations. RESULTS: The response rate was 50.3% (338/672); 320 submitted surveys were analyzed. Two-thirds (65%) of respondents experienced mental health concerns during the pandemic and were significantly less comfortable about seeking mental health support than those without concerns. Nonetheless, 64% were highly satisfied with their surrogacy experience; 80% received a high level of support from their IPs, and 90% reported a good relationship with them. The final hierarchical regression model identified five significant predictors, explaining 39.4% of the variance in PHQ-4 scores: a prior mental health history, COVID-19 impact on personal life, surrogacy satisfaction, loneliness, and social support. CONCLUSIONS: COVID-19 created an unprecedented challenge to surrogacy care, increasing surrogates' risk of experiencing mental health symptoms. Our data show that IP support and the surrogate-IP relationship were fundamentals to surrogacy satisfaction. The findings are relevant to fertility and mental health practitioners in identifying surrogates who are more susceptible to mental health challenges. Fertility clinics should ensure adequate psychological screening of surrogate candidates and proactively offer mental health support services.

Challenges of Digital Transformation in Education in India

Online learning has been present since the 1960s and has risen in popularity over time. World-class universities have been using online teaching-learning methodologies to fulfill the needs of students who reside far away from academic institutions for more than a decade. Many people predicted that online education would be the way of the future, but with the arrival of COVID-19, online education was imposed upon stakeholders far sooner and more suddenly than expected. When the COVID-19 pandemic broke out, educational institutions began to explore digital ways to keep students studying even when they couldn't be together in person as governments enacted legislation prohibiting large groups of people from gathering for any reason, including education. The future of such a transition looks promising. However, transitioning from one mode of education to another is not easy. Historically, when educators adopt new tools, learning still continues in the conventional manner. Based on the responses of 176 students, this paper studies the challenges of Digital transformation in the Education sector. The research is extremely beneficial in evaluating the scope of societal opposition to change. © 2022 IEEE.

PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning

Of fundamental importance in biochemical and biomedical research is understanding a molecule's biological properties—its structure, its function(s), and its activity(ies). To this end, computational methods in Artificial Intelligence, in particular Deep Learning (DL), have been applied to further biomolecular understanding—from analysis and prediction of protein–protein and protein–ligand interactions to drug discovery and design. While choosing the most appropriate DL architecture is vitally important to accurately model the task at hand, equally important is choosing the features used as input to represent molecular properties in these DL models. Through hypothesis testing, bioinformaticians have created thousands of engineered features for biomolecules such as proteins and their ligands. Herein we present an organizational taxonomy for biomolecular features extracted from 808 articles from across the scientific literature. This objective view of biomolecular features can reduce various forms of experimental and/or investigator bias and additionally facilitate feature selection in biomolecular analysis and design tasks. The resulting dataset contains 1360 nondeduplicated features, and a sample of these features were classified by their properties, clustered, and used to suggest new features. The complete feature dataset (the Public Repository of Engineered Features for Molecular Deep Learning, PREFMoDeL) is released for collaborative sourcing on the web.

Visible-Light-Activated Molecular Machines Kill Fungi by Necrosis Following Mitochondrial Dysfunction and Calcium Overload.

Invasive fungal infections are a growing public health threat. As fungi become increasingly resistant to existing drugs, new antifungals are urgently needed. Here, it is reported that 405-nm-visible-light-activated synthetic molecular machines (MMs) eliminate planktonic and biofilm fungal populations more effectively than conventional antifungals without resistance development. Mechanism-of-action studies show that MMs bind to fungal mitochondrial phospholipids. Upon visible light activation, rapid unidirectional drilling of MMs at ≈3 million cycles per second (MHz) results in mitochondrial dysfunction, calcium overload, and ultimately necrosis. Besides their direct antifungal effect, MMs synergize with conventional antifungals by impairing the activity of energy-dependent efflux pumps. Finally, MMs potentiate standard antifungals both in vivo and in an ex vivo porcine model of onychomycosis, reducing the fungal burden associated with infection.

COVID-19 Vaccine Safety Surveillance in Early Pregnancy in the United States: Design Factors Affecting the Association Between Vaccine and Spontaneous Abortion.

In the Vaccine Safety Datalink (VSD), we previously reported no association between COVID-19 vaccination in early pregnancy and spontaneous abortion (SAB). The current study aims to understand how time since vaccine roll-out or other methodologic factors could affect results. Using a case-control design and generalized estimating equations, we estimated the odds ratios (OR) of COVID-19 vaccination in the 28 days before a SAB or last date of the surveillance period (index date) in ongoing pregnancies and occurrence of SAB, across cumulative 4-week periods from December 2020 through June 2021. Using data from a single site, we evaluated alternate methodologic approaches: increasing the exposure window to 42 days, modifying the index date from the last day to the midpoint of the surveillance period, and constructing a cohort design with a time-dependent exposure model. A protective effect (OR 0.78; 95% Confidence Interval (CI): 0.69-0.89), observed with 3-cumulative periods ending March 8, 2021, was attenuated when surveillance extended to June 28, 2021 (OR: 1.02; 95% CI: 0.96-1.08). We observed a lower OR for a 42-day as compared to a 28-day window. The time-dependent model showed no association. Timing of the surveillance appears to be an important factor affecting the observed vaccine-SAB association.

COVID-19-Associated Pulmonary Aspergillosis Isolates Are Genomically Diverse but Similar to Each Other in Their Responses to Infection-Relevant Stresses.

Secondary infections caused by the pulmonary fungal pathogen Aspergillus fumigatus are a significant cause of mortality in patients with severe coronavirus disease 19 (COVID-19). Even though epithelial cell damage and aberrant cytokine responses have been linked to susceptibility to COVID-19-associated pulmonary aspergillosis (CAPA), little is known about the mechanisms underpinning copathogenicity. Here, we analyzed the genomes of 11 A. fumigatus isolates from patients with CAPA in three centers from different European countries. CAPA isolates did not cluster based on geographic origin in a genome-scale phylogeny of representative A. fumigatus isolates. Phenotypically, CAPA isolates were more similar to the A. fumigatus A1160 reference strain than to the Af293 strain when grown in infection-relevant stresses, except for interactions with human immune cells wherein macrophage responses were similar to those induced by the Af293 reference strain. Collectively, our data indicate that CAPA isolates are genomically diverse but are more similar to each other in their responses to infection-relevant stresses. A larger number of isolates from CAPA patients should be studied to better understand the molecular epidemiology of CAPA and to identify genetic drivers of copathogenicity and antifungal resistance in patients with COVID-19. IMPORTANCE Coronavirus disease 2019 (COVID-19)-associated pulmonary aspergillosis (CAPA) has been globally reported as a life-threatening complication in some patients with severe COVID-19. Most of these infections are caused by the environmental mold Aspergillus fumigatus, which ranks third in the fungal pathogen priority list of the WHO. However, little is known about the molecular epidemiology of Aspergillus fumigatus CAPA strains. Here, we analyzed the genomes of 11 A. fumigatus isolates from patients with CAPA in three centers from different European countries, and carried out phenotypic analyses with a view to understanding the pathophysiology of the disease. Our data indicate that A. fumigatus CAPA isolates are genomically diverse but are more similar to each other in their responses to infection-relevant stresses.

17-year trends of body mass index, overweight, and obesity among adolescents from 2005 to 2021, including the COVID-19 pandemic: a Korean national representative study.

OBJECTIVE: There is a lack of pediatric studies that have analyzed trends in mean body mass index (BMI) and the prevalence of obesity and overweight over a period that includes the mid-stage of the COVID-19 pandemic. Thus, we aimed to investigate trends in BMI, overweight, and obesity among Korean adolescents from 2005 to 2021, including the COVID-19 pandemic. SUBJECTS AND METHODS: We used data from the Korea Youth Risk Behavior Web-based Survey (KYRBS), which is nationally representative of South Korea. The study included middle- and high-school students between the ages of 12 and 18. We examined trends in mean BMI and prevalence of obesity and/or overweight during the COVID-19 pandemic and compared these to those of pre-pandemic trends in each subgroup by gender, grade, and residential region. RESULTS: Data from 1,111,300 adolescents (mean age: 15.04 years) were analyzed. The estimated weighted mean BMI was 20.48 kg/m2 (95% CI, 20.46-20.51) between 2005 and 2007, and this was 21.61 kg/m2 (95% CI, 21.54-21.68) in 2021. The prevalence of overweight and obesity was 13.1% (95% CI, 12.9-13.3%) between 2005 and 2007 and 23.4% (95% CI, 22.8-24.0%) in 2021. The mean BMI and prevalence of obesity and overweight have gradually increased over the past 17 years; however, the extent of change in mean BMI and in the prevalence of obesity and overweight during the pandemic was distinctly less than before. The 17-year trends in the mean BMI, obesity, and overweight exhibited a considerable rise from 2005 to 2021; however, the slope during the COVID-19 pandemic (2020-2021) was significantly less prominent than in the pre-pandemic (2005-2019). CONCLUSIONS: These findings enable us to comprehend long-term trends in the mean BMI of Korean adolescents and further emphasize the need for practical prevention measures against youth obesity and overweight.

Unsupervised Multi-Latent Space RL Framework for Video Summarization in Ultrasound Imaging.

The COVID-19 pandemic has highlighted the need for a tool to speed up triage in ultrasound scans and provide clinicians with fast access to relevant information. To this end, we propose a new unsupervised reinforcement learning (RL) framework with novel rewards to facilitate unsupervised learning by avoiding tedious and impractical manual labelling for summarizing ultrasound videos. The proposed framework is capable of delivering video summaries with classification labels and segmentations of key landmarks which enhances its utility as a triage tool in the emergency department (ED) and for use in telemedicine. Using an attention ensemble of encoders, the high dimensional image is projected into a low dimensional latent space in terms of: a) reduced distance with a normal or abnormal class (classifier encoder), b) following a topology of landmarks (segmentation encoder), and c) the distance or topology agnostic latent representation (autoencoders). The summarization network is implemented using a bi-directional long short term memory (Bi-LSTM) which utilizes the latent space representation from the encoder. Validation is performed on lung ultrasound (LUS), that typically represent potential use cases in telemedicine and ED triage acquired from different medical centers across geographies (India and Spain). The proposed approach trained and tested on 126 LUS videos showed high agreement with the ground truth with an average precision of over 80% and average F1 score of well over 44 ±1.7 %. The approach resulted in an average reduction in storage space of 77% which can ease bandwidth and storage requirements in telemedicine.

Gastrointestinal organoids in the study of viral infections.

Viruses are among the most prevalent enteric pathogens. While virologists historically relied on cell lines and animal models, human intestinal organoids (HIOs) continue to grow in popularity. HIOs are non-transformed, stem cell derived, ex vivo cell cultures that maintain the cell type diversity of the intestinal epithelium. They offer higher throughput than standard animal models while more accurately mimicking the native tissue of infection than transformed cell lines. Here, we review recent literature that highlights virological advances facilitated by HIOs. We discuss the variations and limitations of HIOs, but also how HIOs have allowed for the cultivation of previously uncultivatable viruses and how they have offered insight into tropism, entry, replication kinetics, and host-pathogen interactions. In each case, we discuss exemplary viruses and archetypal studies. We discuss how the speed and flexibility of HIO-based studies contributed to our knowledge of SARS-CoV-2 and anti-viral therapeutics. Finally, we discuss current limitations of HIOs and future directions to overcome these.

Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization.

Pharmacologically active compounds with known biological targets were evaluated for inhibition of SARS-CoV-2 infection in cell and tissue models to help identify potent classes of active small molecules and to better understand host-virus interactions. We evaluated 6,710 clinical and preclinical compounds targeting 2,183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target, and cell interactome produced cellular networks important for infection. This analysis revealed 389 small molecules with micromolar to low nanomolar activities, representing >12 scaffold classes and 813 host targets. Representatives were evaluated for mechanism of action in stable and primary human cell models with SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of host factor dependencies and treatments for viral diseases.

Sentimental Analysis on Online Education Using Machine Learning Models

Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students’ mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students’ learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils’ feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students’ feelings. Students’ favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Masked Face Recognition and Liveness Detection Using Deep Learning Technique

Face recognition has been the most successful image processing application in recent times. Most work involving image analysis uses face recognition to automate attendance management systems. Face recognition is an identification process to verify and authenticate the person using their facial features. In this study, an intelligent attendance management system is built to automate the process of attendance. Here, while entering, a person’s image will get captured. The model will detect the face;then the liveness model will verify whether there is any spoofing attack, then the masked detection model will check whether the person has worn the mask or not. In the end, face recognition will extract the facial features. If the person’s features match the database, their attendance will be marked. In the face of the COVID-19 pandemic, wearing a face mask is mandatory for safety measures. The current face recognition system is not able to extract the features properly. The Multi-task Cascaded Convolutional Networks (MTCNN) model detects the face in the proposed method. Then a classification model based on the architecture of MobileNet V2 is used for liveness and mask detection. Then the FaceNet model is used for extracting the facial features. In this study, two different models for the recognition have been built, one for people with masks another one for people without masks. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with 7 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. METHODS: Our study includes individuals with positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) in the Washington Disease Reporting System with available viral genome data, from 1 December 2020 to 14 January 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination. RESULTS: In total, 58 848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95% confidence interval [CI] 2.40-4.26), Beta (HR 2.85, 95% CI 1.56-5.23), Delta (HR 2.28 95% CI 1.56-3.34), or Alpha (HR 1.64, 95% CI 1.29-2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95% CI .56-1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination. CONCLUSIONS: Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

COVID-19 susceptibility and clinical outcomes in autoimmune inflammatory rheumatic diseases (AIRDs): a systematic review and meta-analysis.

OBJECTIVE: This meta-analysis aims to assess the susceptibility to and clinical outcomes of COVID-19 in autoimmune inflammatory rheumatic disease (AIRD) and following AIRD drug use. MATERIALS AND METHODS: We included observational and case-controlled studies assessing susceptibility and clinical outcomes of COVID-19 in patients with AIRD as well as the clinical outcomes of COVID-19 with or without use of steroids and conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). RESULTS: Meta-analysis including three studies showed that patients with AIRD are not more susceptible to COVID-19 compared to patients without AIRD or the general population (OR: 1.11, 95% CI: 0.58 to 2.14). Incidence of severe outcomes of COVID-19 (OR: 1.34, 95% CI: 0.76 to 2.35) and COVID-19 related death (OR: 1.21, 95% CI: 0.68 to 2.16) also did not show significant difference. The clinical outcomes of COVID-19 among AIRD patients with and without csDMARD or steroid showed that both use of steroid (OR: 1.69, 95% CI: 0.96 to 2.98) or csDMARD (OR: 1.35, 95% CI: 0.63 to 3.08) had no effect on clinical outcomes of COVID-19. CONCLUSIONS: AIRD does not increase susceptibility to COVID-19, not affecting the clinical outcome of COVID-19. Similarly, the use of steroids or csDMARDs for AIRD does not worsen the clinical outcome.

The structures that shape news consumption: Evidence from the early period of the COVID-19 pandemic

Researchers and practitioners increasingly believe that journalism must improve its relationship with audiences to increase the likelihood that people will consume and support news. In this paper, we argue that this assumption overlooks the importance of structural- and individual-level factors in shaping news audience behavior. Drawing on Giddens? theory of structuration, we suggest that, when it comes to the amount of time that people devote to news, consumers? choices are guided more by life circumstances than by news preferences. To illustrate this point, we draw on a combination of interview and audience analytics data collected when so many people?s life circumstances changed: the start of the COVID-19 pandemic. We find that people consumed more news during the early months of the pandemic than normal because (1) they had more time on their hands due to things like shelter-in-place orders, layoffs, and shifts to working from home and (2) they were more interested in understanding the coronavirus? spread and risks as well as the preventative measures being pursued. We conclude that journalists should embrace ?journalistic humility,? thereby acknowledging and accepting that they have much less control over the reception of their work than they would like to believe.

Clinical characteristics and treatments of multi-system inflammatory syndrome in children: a systematic review.

OBJECTIVE: Multisystem inflammatory syndrome in children (MIS-C) can occur in association with coronavirus disease 2019 (COVID-19). It is not easy to differentiate MIS-C from severe COVID-19 or Kawasaki disease based on symptoms. The aim of this study was to describe the clinical and laboratory characteristics of MIS-C. PATIENTS AND METHODS: We searched PubMed/Medline for case series and reports of MIS-C published until June 20, 2020. From a total of nine articles involving 45 cases, various clinical and laboratory data were extracted. Each target case was evaluated by using different diagnostic criteria. RESULTS: The average age at onset of MIS-C was 8.6 years. In 80% of cases, the age of patients ranged from 5 to 15 years. Fever (100%) and shock (82%) were the most common presenting symptoms. Sixty percent of cases met the diagnostic criteria for typical or atypical Kawasaki disease. Biomarkers indicative of inflammation, coagulopathy, or cardiac injury were characteristically elevated as follows: ferritin (mean: 1,061 ng/mL), CRP (217 mg/L), ESR (69 mm/hr), IL-6 (214.8 pg/mL), TNFα (63.4 pg/mL), D-dimer (3,220 ng/mL), PT (15.5 s), troponin I (1,006 ng/L), and BNP (12,150 pg/mL). Intravenous immunoglobulin was administered in all target cases, and inotropic agents were commonly used as well. No case of death was observed. CONCLUSIONS: This study demonstrated that MIS-C is a serious condition that presents with fever, rash, as well as cardiovascular and gastrointestinal symptoms. Although it is challenging to differentiate MIS-C from Kawasaki disease or severe COVID-19, initiation of appropriate treatments through early diagnosis is warranted.

Decomposing the Temporal Signature of Nitrogen Dioxide Declines during the COVID-19 Pandemic in UK Urban Areas

On March 23, 2020, a national lockdown was imposed in the UK to limit interpersonal contact and the spread of COVID-19. Human mobility patterns were drastically adjusted as individuals complied with stay-at-home orders, changed their working patterns, and moved increasingly in the proximity of their home. Such behavioural changes brought about many spillover impacts, among which the sharp and immediate reduction in the concentration of nitrogen-based pollutants throughout the country. This work explores the extent to which urban Nitrogen Dioxide (NO2) concentration responds to changes in human behaviour, in particular human mobility patterns and commuting. We model the dynamic and responsive change in NO2 concentration in the period directly following national lockdown and respective opening orders. Using the national urban air quality monitoring network we generate a synthetic NO2 concentration series built from a time series of historic data to compare expected modelled trends to the actual observed patterns in 2020. A series of pre- and post-estimators are modelled to understand the scale of concentration responsiveness to human activity and varying ability of areas across the UK to comply with the lockdown closing and response to openings. Specifically, these are linked to workday commuting times and observed patterns of human mobility change obtained from Google mobility reports. We find a strong and robust co-movement of air pollution concentration and work-related mobility – concentrations of NO2 during typical weekday commuting hours saw a higher relative drop, moving in tandem with patterns of human mobility around workplaces over the course of lockdowns and openings. While NO2 concentrations remained relatively low around the time of reopening, particularly during commuting hours, there is a relatively fast responsiveness rate to concentrations increasing quickly in line with human activity. With one of the key Government advice for workers to take staggered transportation into work and lessen the burden of rush hours and adopting more flexible work-home arrangements, our results would suggest that reductions in NO2 in urban areas are particularly responsive to broader human patterns and dynamics over time as we transitioned towards new working routines.

Seroprevalence of COVID-19 IgG Antibody in Resident and Fellow Physicians in Milwaukee, Wisconsin: Analysis of a Cross-Sectional Survey.

PURPOSE: Medical trainees are likely at differential risk of exposure to COVID-19 per respective clinical activity. We sought to determine the seroprevalence of COVID-19 antibody (Ab) among resident and fellow physicians with varying degrees of exposure to COVID-19. METHODS: A cross-sectional study of Milwaukee-based resident and fellow physicians, encompassing December 2019-June 2020, was conducted. Relevant variables of interest were ascertained by survey and payroll data, and Abbott ARCHITECT Ab test (index cut-off of ≥1.4) was performed. Descriptive statistics were generated, with 95% CI calculated for the study's primary outcome of seroprevalence. RESULTS: Among survey respondents (92 of 148, 62%), 61% were male, 44% were non-White, mean age was 31 years, 94% had no underlying conditions, and 52% were either family or internal medicine residents. During the study period, ≥32% reported cough, headache, or sore throat and 62% traveled outside of Wisconsin. Overall, 83% thought they had a COVID-19 exposure at work and 33% outside of work; 100% expressed any exposure. Of those exposed at work, 56% received COVID-19 pay, variously receiving 69 mean hours (range: 0-452). Ultimately, 82% (75 of 92) had an Ab test completed; 1 individual (1.3%; 95% CI: 0.0-3.9) tested seropositive, was not previously diagnosed, and had received COVID-19 pay. CONCLUSIONS: The low Ab seroprevalence found in resident and fellow physicians was similar to the concurrently reported 3.7% Ab-positive rate among 2456 Milwaukee-based staff in the same integrated health system. Ultimately, COVID-19 seroconversion may be nominal in properly protected resident and fellow physicians despite known potential exposures.

Neutrophil Count is Predicitive of Mortality in COVID(Long): An Easy-to-Use Biomarker Based Modelto Predict Mortality at Admission

Introduction: The second wave of COVID has been devastating inIndia and many developing countries. The mortality has been reported40% higher than in the first wave overwhelming the nation's healthinfrastructure. Despite better understanding of the disease andestablished treatment protocols including steroids and heparin;thesecond wave was disastrous. Subsequent waves have the potential tofurther cripple health care deliveries affecting non COVID care alsoacross many developing economies. It is then important to identifyand triage high risk patients to best use the limited resources.Aims &Objectives: The objective of this study was to identifypotential predictors of mortality in the second wave who accessedhealth care at our academic setup.Materials &Methods: All patients admitted at our centre from 01February through June 15 2021 were included in the analysis. Areduced set of potential predictor variables was selected a priori,which included routine investigations sent on patient admission at ourcenter. These were bundled as groups namely;coagulation markers(INR, APTT, Fibrinogen, d-Dimer), Inflammatory markers (ESR,CRP, Ferritin), Hemogram, Liver function tests, Renal function tests,Arterial blood gas analytics and Glucose levels at admission (measured using the arterial blood gas analyzer).We used a two stage model building process.Result: We collected data from 790 patients. The overall mortalityrate was 10% (79 patients). The median age of patients in the cohortwas 57 years (range 1-99). Patients travelled a distance of 25 km (1-262 km) to seek care. We identified 78 candidate predictor variablesmeasured at hospital admission.n entering variables into a logisticregression model [least absolute shrinkage and selection operator] 4variables were retained within the final model. We identified 4important (Table 1) predictors of mortality by using this modelling:LDH, Oxygen Saturation in Abg (SO2), Neutrophil count and Glucose level at admission >LDH 675 U/L, Oxygen SO2 C 94%Neutrophil count C 7000/mm3 and Glucose value > 132 mg/dL].Using a ROC a 'c' measure of 0.834 corresponded to the modeldiscriminating the response.Conclusions: In our analysis, 4 variables which include LDH, Oxygen saturation, Neutrophil count and Glucose measurements atadmission are important predictors of mortality. Their role need moreresearch;possibly reflective of roles of NETs in the inflammatorycascade of severe covid.