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1.
PLoS One ; 17(3): e0264785, 2022.
Article in English | MEDLINE | ID: covidwho-1745317

ABSTRACT

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure.


Subject(s)
COVID-19/mortality , Hospitalization/statistics & numerical data , Adolescent , Adult , Aged , Aged, 80 and over , Bayes Theorem , COVID-19/epidemiology , COVID-19/etiology , Child , China/epidemiology , Female , Humans , India/epidemiology , Machine Learning , Male , Middle Aged , Models, Statistical , Risk Assessment/methods , Risk Factors , Young Adult
2.
Cureus ; 14(2), 2022.
Article in English | EuropePMC | ID: covidwho-1728467

ABSTRACT

Background In contrast to the first wave, the second COVID-19 wave has taken a huge toll affecting maternal outcomes adversely. The aim of this study was to investigate the consequences of the severity of maternal disease on perinatal outcomes and the risk of vertical transmission and to find out the factors associated with adverse fetomaternal outcomes. Materials and methods This was an ambispective observational study including COVID-19 infected pregnant patients;20-40 years of age irrespective of gestational age admitted at Government Institute of Medical Sciences, UP, India. The patients were divided into two groups: CW 1 (COVID-19 Wave 1): Patients admitted between April 1, 2020 and December 31, 2020 and CW 2 (COVID-19 Wave 2): Patients admitted between April 1, 2021 to May 31, 2021. Data in two groups were compared and analyzed with respect to the clinical profile, laboratory parameters, fetomaternal outcome and the risk of vertical transmission of COVID-19 infection. Results We included 134 eligible patients in the CW1 group and 58 in the CW2 group. Significantly more patients were symptomatic in CW2 (23.1% versus 60.3%, p= <0.001). In CW2, maternal neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP) and D-Dimer were significantly raised along with abnormal chest x-rays. There was a significant increase in maternal mortality in CW2 (1.5% vs 13.7%;p≤0.001). A total of 76 patients delivered in CW1 and 26 in CW2 with increased incidence of cesarean section (43.4%;42.3%), preterm deliveries (28.2%;37%) and low birth weight (34.6%;25.9%) in both waves, the difference among two groups being statistically insignificant. Compared to CW1, perinatal mortality was significantly increased in CW2 (2.2% vs 15.5%;p<0.001). Though nasopharyngeal swab tested positive in four neonates in CW1 and two neonates in CW2, no evidence of vertical transmission was observed even with increased severity of maternal illness. On regression analysis, D-Dimer and CRP were found to have a positive association with maternal and perinatal mortality.  Conclusion The severity of maternal illness proportionately affects the neonatal outcome with no impact on the risk of vertical transmission of infection. D-Dimer and CRP have emerged as independent predictors for maternal and perinatal mortality and hence can be utilized in obstetrics decision-making.

3.
Lancet Infect Dis ; 22(4): 428-429, 2022 04.
Article in English | MEDLINE | ID: covidwho-1586200
4.
Cells ; 10(11)2021 10 26.
Article in English | MEDLINE | ID: covidwho-1488493

ABSTRACT

Inflammasome activation is linked to the aggregation of the adaptor protein ASC into a multiprotein complex, known as the ASC speck. Redistribution of cytosolic ASC to this complex has been widely used as a readout for inflammasome activation and precedes the downstream proteolytic release of the proinflammatory cytokines, IL-1ß and IL-18. Although inflammasomes are important for many diseases such as periodic fever syndromes, COVID-19, gout, sepsis, atherosclerosis and Alzheimer's disease, only a little knowledge exists on the precise and cell type specific occurrence of inflammasome activation in patient samples ex vivo. In this report, we provide detailed information about the optimal conditions to reliably identify inflammasome activated monocytes by ASC speck formation using a modified flow cytometric method introduced by Sester et al. in 2015. Since no protocol for optimal sample processing exists, we tested human blood samples for various conditions including anticoagulant, time and temperature, the effect of one freeze-thaw cycle for PBMC storage, and the fast generation of a positive control. We believe that this flow cytometric protocol will help researchers to perform high quality translational research in multicenter studies, and therefore provide a basis for investigating the role of the inflammasome in the pathogenesis of various diseases.


Subject(s)
CARD Signaling Adaptor Proteins/metabolism , Flow Cytometry/methods , Inflammasomes/immunology , Anticoagulants , Flow Cytometry/standards , Humans , Inflammasomes/metabolism , Leukocytes, Mononuclear/cytology , Leukocytes, Mononuclear/immunology , Leukocytes, Mononuclear/metabolism , Monocytes/cytology , Monocytes/immunology , Monocytes/metabolism , Specimen Handling , Temperature , Time Factors
5.
Front Microbiol ; 12: 653399, 2021.
Article in English | MEDLINE | ID: covidwho-1389208

ABSTRACT

Co-infection with ancillary pathogens is a significant modulator of morbidity and mortality in infectious diseases. There have been limited reports of co-infections accompanying SARS-CoV-2 infections, albeit lacking India specific study. The present study has made an effort toward elucidating the prevalence, diversity and characterization of co-infecting respiratory pathogens in the nasopharyngeal tract of SARS-CoV-2 positive patients. Two complementary metagenomics based sequencing approaches, Respiratory Virus Oligo Panel (RVOP) and Holo-seq, were utilized for unbiased detection of co-infecting viruses and bacteria. The limited SARS-CoV-2 clade diversity along with differential clinical phenotype seems to be partially explained by the observed spectrum of co-infections. We found a total of 43 bacteria and 29 viruses amongst the patients, with 18 viruses commonly captured by both the approaches. In addition to SARS-CoV-2, Human Mastadenovirus, known to cause respiratory distress, was present in a majority of the samples. We also found significant differences of bacterial reads based on clinical phenotype. Of all the bacterial species identified, ∼60% have been known to be involved in respiratory distress. Among the co-pathogens present in our sample cohort, anaerobic bacteria accounted for a preponderance of bacterial diversity with possible role in respiratory distress. Clostridium botulinum, Bacillus cereus and Halomonas sp. are anaerobes found abundantly across the samples. Our findings highlight the significance of metagenomics based diagnosis and detection of SARS-CoV-2 and other respiratory co-infections in the current pandemic to enable efficient treatment administration and better clinical management. To our knowledge this is the first study from India with a focus on the role of co-infections in SARS-CoV-2 clinical sub-phenotype.

6.
Nature ; 594(7862): 265-270, 2021 06.
Article in English | MEDLINE | ID: covidwho-1246377

ABSTRACT

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.


Subject(s)
Blockchain , Clinical Decision-Making/methods , Confidentiality , Datasets as Topic , Machine Learning , Precision Medicine/methods , COVID-19/diagnosis , COVID-19/epidemiology , Disease Outbreaks , Female , Humans , Leukemia/diagnosis , Leukemia/pathology , Leukocytes/pathology , Lung Diseases/diagnosis , Machine Learning/trends , Male , Software , Tuberculosis/diagnosis
7.
J Midlife Health ; 11(4): 240-249, 2020.
Article in English | MEDLINE | ID: covidwho-1076790

ABSTRACT

BACKGROUND: COVID-19 has shown a definite association with gender, a predilection for males in terms of morbidity and mortality. The indirect evidence of the protective effect of estrogen has been shown by Channappanavar, in the animal model and Ding T. in a multihospital study from China, suggesting menopause as independent risk factor and estrogen is negatively correlated with severity. OBJECTIVE: Study the clinical profile and outcomes in premenopausal and menopausal. Covid-19-infected women and analyzed the effect of menstrual status on the outcome. MATERIALS AND METHODS: A retrospective cohort study conducted on 147 mild and moderate category COVID-19 females admitted between May and August 2020 using hospital records and telephonic follow-up. Two groups formed based on menstrual status: group-1 (premenopausal/estrogenic) and Group-2 (menopausal/hypoestrogenic). Hospital stay duration was considered as primary, while the category of disease on admission, clinical course, the requirement of oxygen, and mortality and residual symptoms were taken as a secondary outcome to compare the groups. RESULTS: Overall Group-1 had significantly more of mild disease, while Group-2 had moderate cases (39 [76.5%] vs. 14 [14.6%] P < 0.01). Menopausal group has significantly more requirement of oxygen (32 [62.7%] vs. 20 [20.8%]), ventilation (14 [27.5%] vs. 1 [1%]) progression-to-severe disease (23.5% vs. 7.3%) and prolonged hospital stay ([14.1 ± 8.9 vs. 8.6 ± 3.9 days] P < 0.01). However, multivariate logistic regression failed to show a significant association between hospital stay and progression with menopause. Ferritin and residual symptoms found significantly higher in menopausal. CONCLUSIONS: No definite association was found between menopause and COVID-19 outcome with hospital stay duration or disease progression in our study.

8.
Cureus ; 12(12): e12116, 2020 Dec 16.
Article in English | MEDLINE | ID: covidwho-1013550

ABSTRACT

Objective Our study aimed to assess the mental health outcomes and coping strategies among healthcare workers (HCWs) in an already over-burdened maternity ward and labour room during the coronavirus disease 2019 (COVID-19) pandemic. Methods This cross-sectional questionnaire survey was conducted using Google Forms (Google LLC, Mountain View, CA), which included demographic characteristics, perceived stressors, and validated scales: the Depression, Anxiety and Stress Scale - 21 Items (DASS-21), Insomnia Severity Index, and the Brief Coping Orientation to Problems Experienced (Brief COPE) scale. The results were evaluated and compared among COVID-19 caregivers and other HCWs. Results A total of 184 participants were included in the study, out of which 112 (60.9%) were COVID-19 caregivers. Overall, HCWs managing COVID-19 patients experienced significantly higher levels of depression, anxiety, and stress. They often adopted an avoidant coping style (p-value: 0.006). The results of binary logistic regression analysis revealed that living with family and perceiving multiple stressors appeared to be associated with increased risk of anxiety while being a COVID-19 caregiver and appeared to be a risk factor for stress. Avoidant coping was found to be associated with insomnia while approach coping was less associated with anxiety. The most prevalent stressor among HCWs at our institute was distancing from family and friends (62%) followed by fear of getting infected (51.1%). Compared to other HCWs, the stressors perceived in significantly higher proportion by COVID-19 caregivers included distancing from family and friends (p-value: 0.003), scarcity of workforce (p-value: 0.005), and dealing with non-cooperative patients (p-value: <0.001). Conclusion We would request the immediate attention of the concerned authorities to implement interventions to buffer the impact of COVID-19 in the already stressed-out maternity wards and labour rooms.

9.
Immunity ; 53(6): 1296-1314.e9, 2020 12 15.
Article in English | MEDLINE | ID: covidwho-965599

ABSTRACT

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.


Subject(s)
COVID-19/metabolism , Erythroid Cells/pathology , Megakaryocytes/physiology , Plasma Cells/physiology , SARS-CoV-2/physiology , Adult , Aged , Aged, 80 and over , Biomarkers , Blood Circulation , COVID-19/immunology , Cells, Cultured , Cohort Studies , Disease Progression , Female , Gene Expression Profiling , Humans , Male , Middle Aged , Proteomics , Sequence Analysis, RNA , Severity of Illness Index , Single-Cell Analysis
10.
Pathogens ; 9(11)2020 Nov 04.
Article in English | MEDLINE | ID: covidwho-909053

ABSTRACT

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has challenged the research community globally to innovate, interact, and integrate findings across hierarchies. Research on SARS-CoV-2 has produced an abundance of data spanning multiple parallels, including clinical data, SARS-CoV-2 genome architecture, host response captured through transcriptome and genetic variants, microbial co-infections (metagenome), and comorbidities. Disease phenotypes in the case of COVID-19 present an intriguing complexity that includes a broad range of symptomatic to asymptomatic individuals, further compounded by a vast heterogeneity within the spectrum of clinical symptoms displayed by the symptomatic individuals. The clinical outcome is further modulated by the presence of comorbid conditions at the point of infection. The COVID-19 pandemic has produced an expansive wealth of literature touching many aspects of SARS-CoV-2 ranging from causal to outcome, predisposition to protective (possible), co-infection to comorbidity, and differential mortality globally. As challenges provide opportunities, the current pandemic's challenge has underscored the need and opportunity to work for an integrative approach that may be able to thread together the multiple variables. Through this review, we have made an effort towards bringing together information spanning across different domains to facilitate researchers globally in pursuit of their response to SARS-CoV-2.

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