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1.
Front Cell Infect Microbiol ; 12: 868414, 2022.
Article in English | MEDLINE | ID: covidwho-1779936

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an enormous burden on the healthcare system worldwide as a consequence of its new emerging variants of concern (VOCs) since late 2019. Elucidating viral genome characteristics and its influence on disease severity and clinical outcome has been one of the crucial aspects toward pandemic management. Genomic surveillance holds the key to identify the spectrum of mutations vis-à-vis disease outcome. Here, in our study, we performed a comprehensive analysis of the mutation distribution among the coronavirus disease 2019 (COVID-19) recovered and mortality patients. In addition to the clinical data analysis, the significant mutations within the two groups were analyzed for their global presence in an effort to understand the temporal dynamics of the mutations globally in comparison with our cohort. Interestingly, we found that all the mutations within the recovered patients showed significantly low global presence, indicating the possibility of regional pool of mutations and the absence of preferential selection by the virus during the course of the pandemic. In addition, we found the mutation S194L to have the most significant occurrence in the mortality group, suggesting its role toward a severe disease progression. Also, we discovered three mutations within the mortality patients with a high cohort and global distribution, which later became a part of variants of interest (VOIs)/VOCs, suggesting its significant role in enhancing viral characteristics. To understand the possible mechanism, we performed molecular dynamics (MD) simulations of nucleocapsid mutations, S194L and S194*, from the mortality and recovered patients, respectively, to examine its impacts on protein structure and stability. Importantly, we observed the mutation S194* within the recovered to be comparatively unstable, hence showing a low global frequency, as we observed. Thus, our study provides integrative insights about the clinical features, mutations significantly associated with the two different clinical outcomes, its global presence, and its possible effects at the structural level to understand the role of mutations in driving the COVID-19 pandemic.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/genetics , Genome, Viral , Humans , Mutation , Pandemics , Phylogeny , SARS-CoV-2/genetics
2.
Front Microbiol ; 13: 763169, 2022.
Article in English | MEDLINE | ID: covidwho-1753386

ABSTRACT

Vaccine development against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been of primary importance to contain the ongoing global pandemic. However, studies have demonstrated that vaccine effectiveness is reduced and the immune response is evaded by variants of concern (VOCs), which include Alpha, Beta, Delta, and, the most recent, Omicron. Subsequently, several vaccine breakthrough (VBT) infections have been reported among healthcare workers (HCWs) due to their prolonged exposure to viruses at healthcare facilities. We conducted a clinico-genomic study of ChAdOx1 (Covishield) VBT cases in HCWs after complete vaccination. Based on the clinical data analysis, most of the cases were categorized as mild, with minimal healthcare support requirements. These patients were divided into two sub-phenotypes based on symptoms: mild and mild plus. Statistical analysis showed a significant correlation of specific clinical parameters with VBT sub-phenotypes. Viral genomic sequence analysis of VBT cases revealed a spectrum of high- and low-frequency mutations. More in-depth analysis revealed the presence of low-frequency mutations within the functionally important regions of SARS-CoV-2 genomes. Emphasizing the potential benefits of surveillance, low-frequency mutations, D144H in the N gene and D138Y in the S gene, were observed to potentially alter the protein secondary structure with possible influence on viral characteristics. Substantiated by the literature, our study highlights the importance of integrative analysis of pathogen genomic and clinical data to offer insights into low-frequency mutations that could be a modulator of VBT infections.

3.
Microbiol Spectr ; 10(2): e0272921, 2022 Apr 27.
Article in English | MEDLINE | ID: covidwho-1752776

ABSTRACT

Since its advent, the pandemic has caused havoc in multiple waves due partly to amplified transmissibility and immune escape to vaccines. Delhi, India also witnessed brutal multiple peaks causing exponential rise in cases. Here we had retrospectively investigated clade variation, emergence of new lineages and varied clinical characteristics during those three peaks in order to understand the trajectory of the ongoing pandemic. In this study, a total of 123,378 samples were collected for a time span of 14 months (1 June 2020 to 3 August 2021) encompassing three different peaks in Delhi. A subset of 747 samples was processed for sequencing. Complete clinical and demographic details of all the enrolled cases were also collected. We detected 26 lineages across three peaks nonuniformly from 612 quality passed samples. The first peak was driven by diverse early variants, while the second one by B.1.36 and B.1.617.2, unlike third peak caused entirely by B.1.617.2. A total of 18,316 mutations with median of 34 were reported. Majority of mutations were present in less than 1% of samples. Differences in clinical characteristics across three peaks was also reported. To be ahead of the frequently changing course of the ongoing pandemic, it is of utmost importance that novel lineages be tracked continuously. Prioritized sequencing of sudden local outburst and community hot spots must be done to swiftly detect a novel mutation/lineage of potential clinical importance. IMPORTANCE Genome surveillance of the Delhi data provides a more detailed picture of diverse circulating lineages. The added value that the current study provides by clinical details of the patients is of importance. We looked at the shifting patterns of lineages, clinical characteristics and mutation types and mutation load during each successive infection surge in Delhi. The importance of widespread genomic surveillance cannot be stressed enough to timely detect new variants so that appropriate policies can be immediately implemented upon to help control the infection spread. The entire idea of genomic surveillance is to arm us with the clues as to how the novel mutations and/or variants can prove to be more transmissible and/or fatal. In India, the densely populated cities have an added concern of the huge burden that even the milder variants of the virus combined with co-morbidity can have on the community/primary health care centers.


Subject(s)
COVID-19 , SARS-CoV-2 , COVID-19/epidemiology , Genomics , Humans , Mutation , Phylogeny , Retrospective Studies , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics
4.
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
5.
Budhiraja, Sandeep, Tarai, Bansidhar, Jain, Dinesh, Aggarwal, Mona, Indrayan, Abhaya, Das, Poonam, Mishra, R. S.; Bali, Supriya, Mahajan, Monica, Kirtani, Jay, Tickoo, Rommel, Soni, Pankaj, Nangia, Vivek, Lall, Ajay, Kishore, Nevin, Jain, Ashish, Singh, Omender, Singh, Namrita, Kumar, Ashok, Saxena, Prashant, Dewan, Arun, Aggarwal, Ritesh, Mehra, Mukesh, Jain, Meenakshi, Nakra, Vimal, Sharma, B. D.; Pandey, Praveen Kumar, Singh, Y. P.; Arora, Vijay, Jain, Suchitra, Chhabra, Ranjana, Tuli, Preeti, Boobna, Vandana, Joshi, Alok, Aggarwal, Manoj, Gupta, Rajiv, Aneja, Pankaj, Dhall, Sanjay, Arora, Vineet, Chugh, Inder Mohan, Garg, Sandeep, Mittal, Vikas, Gupta, Ajay, Jyoti, Bikram, Sharma, Puneet, Bhasin, Pooja, Jain, Shakti, Singhal, R. K.; Bhasin, Atul, Vardani, Anil, Pal, Vivek, Pande, Deepak Gargi, Gulati, Tribhuvan, Nayar, Sandeep, Kalra, Sunny, Garg, Manish, Pande, Rajesh, Bag, Pradyut, Gupta, Arpit, Sharma, Jitin, Handoo, Anil, Burman, Purabi, Gupta, Ajay Kumar, Choudhary, Pankaj Nand, Gupta, Ashish, Gupta, Puneet, Joshi, Sharad, Tayal, Nitesh, Gupta, Manish, Khanna, Anita, Kishore, Sachin, Sahay, Shailesh, Dang, Rajiv, Mishra, Neelima, Sekhri, Sunil, Srivastava, Dr Rajneesh Chandra, Agrawal, Dr Mitali Bharat, Mathur, Mohit, Banwari, Akash, Khetarpal, Sumit, Pandove, Sachin, Bhasin, Deepak, Singh, Harpal, Midha, Devender, Bhutani, Anjali, Kaur, Manpreet, Singh, Amarjit, Sharma, Shalini, Singla, Komal, Gupta, Pooja, Sagar, Vinay, Dixit, Ambrish, Bajpai, Rashmi, Chachra, Vaibhav, Tyagi, Puneet, Saxena, Sanjay, Uniyal, Bhupesh, Belwal, Shantanu, Aier, Imliwati, Singhal, Mini, Khaduri, Ankit.
IJID Regions ; 2022.
Article in English | ScienceDirect | ID: covidwho-1708321

ABSTRACT

Objective : To get better insights into the extent of secondary bacterial and fungal infections in Indian hospitalized patients and to assess how these alter the course of COVID-19 so that the control measures can be suggested. Methods : This is a retrospective, multicentre study where data of all RT-PCR positive COVID-19 patients was accessed from Electronic Health Records (EHR) of a network of 10 hospitals across 5 North Indian states, admitted during the period from March 2020 to July 2021. Results : Of 19852 RT-PCR positive SARS-CO2 patients admitted during the study period, 1940 (9.8%) patients developed SIs. Patients with SIs were 8 years older on average (median age 62.6 years versus 54.3 years;P<0.001) than those without SIs. The risk of SIs was significantly (p < 0.001) associated with age, severity of disease at admission, diabetes, ICU admission, and ventilator use. The most common site of infection was urinary tract infection (UTI) (41.7%), followed by blood stream infection (BSI) (30.8%), sputum/BAL/ET fluid (24.8%), and the least was pus/wound discharge (2.6%). Gram negative bacilli (GNB) were the commonest organisms (63.2%), followed by Gram positive cocci (GPC) (19.6%) and fungus (17.3%). Most of the patients with SIs were on multiple antimicrobials – the most used were the BL-BLI for GNBs (76.9%) followed by carbapenems (57.7%), cephalosporins (53.9%) and antibiotics carbapenem resistant Enterobacteriaceae (47.1%). The usage of empirical antibiotics for GPCs was in 58.9% and of antifungals in 56.9% of cases, and substantially more than the results obtained by culture. The average stay in hospital for patients with SIs was almost twice than those without SIs (median 13 days versus 7 days). The overall mortality in the group with SIs (40.3%) was more than 8 times of that in those without SIs (4.6%). Only 1.2% of SI patients with mild COVID-19 at presentation died, while 17.5% of those with moderate disease and 58.5% of those with severe COVID-19 died (P< 0.001). The mortality was the highest in those with BSI (49.8%), closely followed by those with HAP (47.9%), and then UTI and SSTI (29.4% each). The mortality in diabetic patients with SIs was 45.2% while in non-diabetics it was 34.3% (p < 0.001). Conclusions : Secondary bacterial and fungal infections complicate the course of COVID-19 hospitalised patients. These patients tend to have a much longer stay in hospital, higher requirement for oxygen and ICU care, and significantly high mortality. The group most vulnerable to this complication are those with more severe COVID-19 illness, elderly, and diabetic patients. Judicious empiric use of combination antimicrobials in this set of vulnerable COVID-19 patients can save lives. It is desirable to have a region or country specific guidelines for appropriate use of antibiotics and antifungals to prevent their overuse.

6.
Open Forum Infect Dis ; 8(12): ofab452, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1569723

ABSTRACT

BACKGROUND: The ongoing pandemic of coronavirus disease 2019 (COVID-19) has overwhelmed healthcare facilities and raises an important novel concern of nosocomial transmission of Candida species in the intensive care units (ICUs). METHODS: We evaluated the incidence and risk factors for development of candidemia in 2384 COVID-19 patients admitted during August 2020-January 2021 in ICUs of 2 hospitals (Delhi and Jaipur) in India. A 1:2 case-control matching was used to identify COVID-19 patients who did not develop candidemia as controls. RESULTS: A total of 33 patients developed candidemia and accounted for an overall incidence of 1.4% over a median ICU stay of 24 days. A 2-fold increase in the incidence of candidemia in COVID-19 versus non-COVID-19 patients was observed with an incidence rate of 14 and 15/1000 admissions in 2 ICUs. Candida auris was the predominant species (42%) followed by Candida tropicalis. Multivariable regression analysis revealed the use of tocilizumab, duration of ICU stay (24 vs 14 days), and raised ferritin level as an independent predictor for the development of candidemia. Azole resistance was observed in C auris and C tropicalis harboring mutations in the azole target ERG11 gene. Multilocus sequence typing (MLST) identified identical genotypes of C tropicalis in COVID-19 patients, raising concern for nosocomial transmission of resistant strains. CONCLUSIONS: Secondary bacterial infections have been a concern with the use of tocilizumab. In this cohort of critically ill COVID-19 patients, tocilizumab was associated with the development of candidemia. Surveillance of antifungal resistance is warranted to prevent transmission of multidrug-resistant strains of nosocomial yeasts in COVID-19 hospitalized patients.

7.
J Virol Methods ; 298: 114300, 2021 12.
Article in English | MEDLINE | ID: covidwho-1433624

ABSTRACT

The QIAstat-Dx SARS-CoV-2 panel is a multiplex cartridge based assay based on real time PCR which can detect 17 respiratory viruses, including the novel coronavirus SARS-CoV-2. A syndromic approach is the need of the hour for COVID-19 diagnostics among patients presenting with respiratory symptoms. The present study was done to evaluate 120 archived respiratory clinical specimens for SARS-CoV-2 on the SARS-CoV-2 panel. Further, 27 specimens were tested for other respiratory viruses, in comparison with the BioFire RP1.7 platform. The sensitivity and specificity for SARS-CoV-2 on SARS panel was found to be 90.00 % and 100 % respectively, indicating good diagnostic accuracy. The positive predictive value was found to be 100 %, negative predictive value was found to be 99.93 % and accuracy was 99.93 %. Detection of other respiratory viruses observed a concordance of 77.7 %. Despite advantages of speed, minimal expertise and accurate results; significant costs and discrepancies at Ct >35 remain important limitations of the SARS panel.


Subject(s)
COVID-19 , Viruses , Humans , Real-Time Polymerase Chain Reaction , SARS-CoV-2 , Sensitivity and Specificity
8.
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.

9.
Emerg Infect Dis ; 26(11): 2694-2696, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-734338

ABSTRACT

In New Delhi, India, candidemia affected 15 critically ill coronavirus disease patients admitted to an intensive care unit during April-July 2020. Candida auris accounted for two thirds of cases; case-fatality rate was high (60%). Hospital-acquired C. auris infections in coronavirus disease patients may lead to adverse outcomes and additional strain on healthcare resources.


Subject(s)
Betacoronavirus , Candida , Candidiasis/virology , Coronavirus Infections/microbiology , Cross Infection/microbiology , Pneumonia, Viral/microbiology , Adult , Aged , Antifungal Agents/therapeutic use , COVID-19 , Candidiasis/drug therapy , Candidiasis/epidemiology , Coronavirus Infections/drug therapy , Coronavirus Infections/epidemiology , Critical Illness , Cross Infection/drug therapy , Cross Infection/epidemiology , Drug Resistance, Multiple , Female , Humans , India/epidemiology , Male , Microbial Sensitivity Tests , Middle Aged , Pandemics , Pneumonia, Viral/drug therapy , Pneumonia, Viral/epidemiology , SARS-CoV-2
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