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1.
Infection, Genetics and Evolution ; : 105299, 2022.
Article in English | ScienceDirect | ID: covidwho-1821418

ABSTRACT

Pneumonia, an acute respiratory tract infection, is one of the major causes of mortality worldwide. Depending on the site of acquisition, pneumonia can be community acquired pneumonia (CAP) or nosocomial pneumonia (NP). The risk of pneumonia, is partially driven by host genetics. CYP1A1 is a widely studied pulmonary CYP family gene primarily expressed in peripheral airway epithelium. The CYP1A1 genetic variants, included in this study, alter the gene activity and are known to contribute in lung inflammation, which may cause pneumonia pathogenesis. In this study, we performed a meta-analysis to establish the possible contribution of CYP1A1 gene, and its three variants (rs2606345, rs1048943 and rs4646903) towards the genetic etiology of pneumonia risk. Using PRISMA guidelines, we systematically reviewed and meta-analysed case-control studies, evaluating risk of pneumonia in patients carrying the risk alleles of CYP1A1 variants. Heterogeneity across the studies was evaluated using I2 statistics. Based on heterogeneity, a random-effect (using maximum likelihood) or fixed-effect (using inverse variance) model was applied to estimate the effect size. Pooled odds ratio (OR) was calculated to estimate the overall effect of the risk allele association with pneumonia susceptibility. Egger's regression test and funnel plot were used to assess publication bias. Subgroup analysis was performed based on pneumonia type (CAP and NP), population, as well as age group. A total of ten articles were identified as eligible studies, which included 3049 cases and 2249 healthy controls. The meta-analysis findings revealed CYP1A1 variants, rs2606345 [T vs G;OR = 1.12 (0.75–1.50);p = 0.02;I2 = 84.89%], and rs1048943 [G vs T;OR = 1.19 (0.76–1.61);p = 0.02;I2 = 0.00%] as risk markers whereas rs4646903 showed no statistical significance for susceptibility to pneumonia. On subgroup analysis, both the genetic variants showed significant association with CAP but not with NP. We additionally performed a spatial analysis to identify the key factors possibly explaining the variability across countries in the prevalence of the coronavirus disease 2019 (COVID-19), a viral pneumonia. We observed a significant association between the risk allele of rs2606345 and rs1048943, with a higher COVID-19 prevalence worldwide, providing us important links in understanding the variability in COVID-19 prevalence.

2.
Singh, Prateek, Ujjainiya, Rajat, Prakash, Satyartha, Naushin, Salwa, Sardana, Viren, Bhatheja, Nitin, Singh, Ajay Pratap, Barman, Joydeb, Kumar, Kartik, Gayali, Saurabh, Khan, Raju, Rawat, Birendra Singh, Tallapaka, Karthik Bharadwaj, Anumalla, Mahesh, Lahiri, Amit, Kar, Susanta, Bhosale, Vivek, Srivastava, Mrigank, Mugale, Madhav Nilakanth, Pandey, C. P.; Khan, Shaziya, Katiyar, Shivani, Raj, Desh, Ishteyaque, Sharmeen, Khanka, Sonu, Rani, Ankita, Promila, Sharma, Jyotsna, Seth, Anuradha, Dutta, Mukul, Saurabh, Nishant, Veerapandian, Murugan, Venkatachalam, Ganesh, Bansal, Deepak, Gupta, Dinesh, Halami, Prakash M.; Peddha, Muthukumar Serva, Veeranna, Ravindra P.; Pal, Anirban, Singh, Ranvijay Kumar, Anandasadagopan, Suresh Kumar, Karuppanan, Parimala, Rahman, Syed Nasar, Selvakumar, Gopika, Venkatesan, Subramanian, Karmakar, Malay Kumar, Sardana, Harish Kumar, Kothari, Anamika, Parihar, Devendra Singh, Thakur, Anupma, Saifi, Anas, Gupta, Naman, Singh, Yogita, Reddu, Ritu, Gautam, Rizul, Mishra, Anuj, Mishra, Avinash, Gogeri, Iranna, Rayasam, Geethavani, Padwad, Yogendra, Patial, Vikram, Hallan, Vipin, Singh, Damanpreet, Tirpude, Narendra, Chakrabarti, Partha, Maity, Sujay Krishna, Ganguly, Dipyaman, Sistla, Ramakrishna, Balthu, Narender Kumar, A, Kiran Kumar, Ranjith, Siva, Kumar, B. Vijay, Jamwal, Piyush Singh, Wali, Anshu, Ahmed, Sajad, Chouhan, Rekha, Gandhi, Sumit G.; Sharma, Nancy, Rai, Garima, Irshad, Faisal, Jamwal, Vijay Lakshmi, Paddar, Masroor Ahmad, Khan, Sameer Ullah, Malik, Fayaz, Ghosh, Debashish, Thakkar, Ghanshyam, Barik, S. K.; Tripathi, Prabhanshu, Satija, Yatendra Kumar, Mohanty, Sneha, Khan, Md Tauseef, Subudhi, Umakanta, Sen, Pradip, Kumar, Rashmi, Bhardwaj, Anshu, Gupta, Pawan, Sharma, Deepak, Tuli, Amit, Ray chaudhuri, Saumya, Krishnamurthi, Srinivasan, Prakash, L.; Rao, Ch V.; Singh, B. N.; Chaurasiya, Arvindkumar, Chaurasiyar, Meera, Bhadange, Mayuri, Likhitkar, Bhagyashree, Mohite, Sharada, Patil, Yogita, Kulkarni, Mahesh, Joshi, Rakesh, Pandya, Vaibhav, Mahajan, Sachin, Patil, Amita, Samson, Rachel, Vare, Tejas, Dharne, Mahesh, Giri, Ashok, Mahajan, Sachin, Paranjape, Shilpa, Sastry, G. Narahari, Kalita, Jatin, Phukan, Tridip, Manna, Prasenjit, Romi, Wahengbam, Bharali, Pankaj, Ozah, Dibyajyoti, Sahu, Ravi Kumar, Dutta, Prachurjya, Singh, Moirangthem Goutam, Gogoi, Gayatri, Tapadar, Yasmin Begam, Babu, Elapavalooru Vssk, Sukumaran, Rajeev K.; Nair, Aishwarya R.; Puthiyamadam, Anoop, Valappil, Prajeesh Kooloth, Pillai Prasannakumari, Adrash Velayudhan, Chodankar, Kalpana, Damare, Samir, Agrawal, Ved Varun, Chaudhary, Kumardeep, Agrawal, Anurag, Sengupta, Shantanu, Dash, Debasis.
Computers in Biology and Medicine ; 146:105419, 2022.
Article in English | ScienceDirect | ID: covidwho-1803804

ABSTRACT

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%–80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

3.
Nat Commun ; 13(1): 1726, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1773977

ABSTRACT

Immunization is expected to confer protection against infection and severe disease for vaccines while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies of an observational cohort of healthcare workers, we show that during a Severe Acute Respiratory Syndrome -Coronavirus 2 Delta-variant outbreak in Delhi, 25.3% (95% Confidence Interval 16.9-35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare workers were infected within less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, Inter Quartile Range 374) and without (342 U/ml, Inter Quartile Range 497), as was the induction of neutralization activity to wildtype. This was not vaccine failure since vaccine effectiveness estimate based on infection rates in an unvaccinated cohort were about 70% and most infections were asymptomatic. We find that while ChAdOx1-nCoV19 vaccination remains effective in preventing severe infections, it is unlikely to be completely able to block transmission and provide herd immunity.


Subject(s)
Asymptomatic Infections , COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Health Personnel , Humans , Immunization , SARS-CoV-2 , Vaccination
4.
Lancet Infect Dis ; 22(4): 473-482, 2022 04.
Article in English | MEDLINE | ID: covidwho-1757985

ABSTRACT

BACKGROUND: SARS-CoV-2 variants of concern (VOCs) have threatened COVID-19 vaccine effectiveness. We aimed to assess the effectiveness of the ChAdOx1 nCoV-19 vaccine, predominantly against the delta (B.1.617.2) variant, in addition to the cellular immune response to vaccination. METHODS: We did a test-negative, case-control study at two medical research centres in Faridabad, India. All individuals who had a positive RT-PCR test for SARS-CoV-2 infection between April 1, 2021, and May 31, 2021, were included as cases and individuals who had a negative RT-PCR test were included as controls after matching with cases on calendar week of RT-PCR test. The primary outcome was effectiveness of complete vaccination with the ChAdOx1 nCoV-19 vaccine against laboratory-confirmed SARS-CoV-2 infection. The secondary outcomes were effectiveness of a single dose against SARS-CoV-2 infection and effectiveness of a single dose and complete vaccination against moderate-to-severe disease among infected individuals. Additionally, we tested in-vitro live-virus neutralisation and T-cell immune responses to the spike protein of the wild-type SARS-CoV-2 and VOCs among healthy (anti-nucleocapsid antibody negative) recipients of the ChAdOx1 nCoV-19 vaccine. FINDINGS: Of 2379 cases of confirmed SARS-CoV-2 infection, 85 (3·6%) were fully vaccinated compared with 168 (8·5%) of 1981 controls (adjusted OR [aOR] 0·37 [95% CI 0·28-0·48]), giving a vaccine effectiveness against SARS-CoV-2 infection of 63·1% (95% CI 51·5-72·1). 157 (6·4%) of 2451 of cases and 181 (9·1%) of 1994) controls had received a single dose of the ChAdOx1 nCoV-19 vaccine (aOR 0·54 [95% CI 0·42-0·68]), thus vaccine effectiveness of a single dose against SARS-CoV-2 infection was 46·2% (95% CI 31·6-57·7). One of 84 cases with moderate-to-severe COVID-19 was fully vaccinated compared with 84 of 2295 cases with mild COVID-19 (aOR 0·19 [95% CI 0·01-0·90]), giving a vaccine effectiveness of complete vaccination against moderate-to-severe disease of 81·5% (95% CI 9·9-99·0). The effectiveness of a single dose against moderate-to-severe disease was 79·2% (95% CI 46·1-94·0); four of 87 individuals with moderate-to-severe COVID-19 had received a single dose compared with 153 of 2364 participants with mild disease (aOR 0·20 [95% CI 0·06-0·54]). Among 49 healthy, fully vaccinated individuals, neutralising antibody responses were lower against the alpha (B.1.1.7; geometric mean titre 244·7 [95% CI 151·8-394·4]), beta (B.1.351; 97·6 [61·2-155·8]), kappa (B.1.617.1; 112·8 [72·7-175·0]), and delta (88·4 [61·2-127·8]) variants than against wild-type SARS-CoV-2 (599·4 [376·9-953·2]). However, the antigen-specific CD4 and CD8 T-cell responses were conserved against both the delta variant and wild-type SARS-CoV-2. INTERPRETATION: The ChAdOx1 nCoV-19 vaccine remained effective against moderate-to-severe COVID-19, even during a surge that was dominated by the highly transmissible delta variant of SARS-CoV-2. Spike-specific T-cell responses were maintained against the delta variant. Such cellular immune protection might compensate for waning humoral immunity. FUNDING: Department of Biotechnology India, Council of Scientific and Industrial Research India, and Fondation Botnar.


Subject(s)
COVID-19 , SARS-CoV-2 , Antibody Formation , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines , Case-Control Studies , Humans , Vaccination
5.
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
6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-311097

ABSTRACT

Background: COVID-19 pneumonia has been associated with severe acute hypoxia, sepsis-like states, thrombosis and chronic sequelae including persisting hypoxia and fibrosis. The molecular hypoxia response pathway has been associated with such pathologies and our recent observations on anti-hypoxic and anti-inflammatory effects of whole aqueous extract of Adhatoda Vasica (AV) prompted us to explore its effects on relevant preclinical mouse models. Methods: : In this study, we tested the effect of whole aqueous extract of AV , in murine models of bleomycin induced pulmonary fibrosis, Cecum Ligation and Puncture (CLP) induced sepsis, and siRNA induced hypoxia-thrombosis phenotype. The effect on lung of AV treated naïve mice was also studied at transcriptome level. We also determined if the extract may have any direct effect on SARS-CoV2 replication Results: : Oral administration AV extract attenuates increased airway inflammation, levels of transforming growth factor-b1 (TGF-b1), IL-6, HIF-1α and improves the overall survival rates of mice in the models of pulmonary fibrosis and sepsis and rescues the siRNA induced inflammation and associated blood coagulation phenotypes in mice. We observed downregulation of hypoxia, inflammation, TGF-b1, and angiogenesis genes and upregulation of adaptive immunity-related genes in the lung transcriptome . AV treatment also reduced the viral load in Vero cells infected with SARS-CoV2. Conclusion: Our results provide a scientific rationale for this ayurvedic herbal medicine in ameliorating the hypoxia-hyperinflammation features and highlights the repurposing potential of AV in COVID-19-like conditions.

7.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311095

ABSTRACT

Background: The importance of hypoxia inducible factor-1 α (HIF-1α) stabilization in uncontrolled infection and inflammation is widely accepted. Several inhibitors of HIF signalling are in clinical trials for malignancy, ischemia and inflammatory diseases. Increased hypoxia is being reported to be an important modifier for several pathological features of COVID-19 such as impaired immunity, hyper-inflammation, thrombosis, lung injury and sepsis. Methods: : In this study we tested the effect of whole aqueous extract Adhatoda Vasica (AV), that our group has shown to have anti-hypoxic and anti-inflammatory effects, on various outcomes of hypoxic response. Effects of AV were assessed in preclinical mouse models of pulmonary fibrosis, bacterial sepsis and siRNA induced hypoxia-thrombosis phenotype. Therapeutic relevance of AV in current pandemic were also examined through transcriptome and molecular docking analysis. Results: : Oral administration AV extract attenuated the increased levels of airway inflammation, collagen content, transforming growth factor-β1 (TGF-β1), IL-6, HIF-1α and improved the overall survival rate in bleomycin treated and Cecum Ligation and Puncture (CLP) induced mice. AV treatment also rescued the prolyl hydroxylase domain 2 ( phd 2 ) siRNA induced HIF-1α and associated blood coagulation phenotypes in mice. Transcriptome analysis of lungs of AV treated naïve mice reveal downregulation of hypoxia, inflammation, TGF-β1 and angiogenesis and upregulation of adaptive immunity related genes. These genes and pathways show opposite expression in transcriptome of BALF and PBMCs of SARS-CoV2 infected patient. Molecular docking of AV constituents presents in extract reveal many molecules with low binding energy (≤ -8) to multiple SARS-CoV2 and host target proteins that are relevant in viral entry and replication. Conclusion: Our results provide a scientific rationale for this ayurvedic herbal medicine in ameliorating the anti-inflammatory and anti-HIF-1α effect for potential use in management of COVID19 patients.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-317173

ABSTRACT

The SARS-CoV-2 B.1.617.2 (Delta) variant was first identified in the state of Maharashtra in late 2020 and has spread throughout India, displacing the B.1.1.7 (Alpha) variant and other pre-existing lineages. Mathematical modelling indicates that the growth advantage is most likely explained by a combination of increased transmissibility and immune evasion. Indeed in vitro, the delta variant is less sensitive to neutralising antibodies in sera from recovered individuals, with higher replication efficiency as compared to the Alpha variant. In an analysis of vaccine breakthrough in over 100 healthcare workers across three centres in India, the Delta variant not only dominates vaccine-breakthrough infections with higher respiratory viral loads compared to non-delta infections (Ct value of 16.5 versus 19), but also generates greater transmission between HCW as compared to B.1.1.7 or B.1.617.1 (p=0.02). In vitro, the Delta variant shows 8 fold approximately reduced sensitivity to vaccine-elicited antibodies compared to wild type Wuhan-1 bearing D614G. Serum neutralising titres against the SARS-CoV-2 Delta variant were significantly lower in participants vaccinated with ChadOx-1 as compared to BNT162b2 (GMT 3372 versus 654, p<0001). These combined epidemiological and in vitro data indicate that the dominance of the Delta variant in India has been most likely driven by a combination of evasion of neutralising antibodies in previously infected individuals and increased virus infectivity. Whilst severe disease in fully vaccinated HCW was rare, breakthrough transmission clusters in hospitals associated with the Delta variant are concerning and indicate that infection control measures need continue in the post-vaccination era.

10.
Vaccines (Basel) ; 10(1)2021 Dec 31.
Article in English | MEDLINE | ID: covidwho-1580341

ABSTRACT

This study elucidated the clinical, humoral immune response and genomic analysis of vaccine breakthrough (VBT) infections after ChAdOx1 nCoV-19/Covishield vaccine in healthcare workers (HCWs). Amongst 1858 HCWs, 1639 had received either two doses (1346) or a single dose (293) of ChAdOx1 nCoV-19 vaccine. SARS-CoV-2 IgG antibodies and neutralizing antibodies were measured in the vaccinated group and the development of SARS-CoV-2 infection was monitored.Forty-six RT-PCR positive samples from the 203 positive samples were subjected to whole genome sequencing (WGS). Of the 203 (10.92%) infected HCWs, 21.46% (47/219) were non-vaccinated, which was significantly more than 9.52% (156/1639) who were vaccinated and infection was higher in doctors and nurses. Unvaccinated HCWs had 1.57 times higher risk compared to partially vaccinated HCWs and 2.49 times higher risk than those who were fully vaccinated.The partially vaccinated were at higher risk than the fully vaccinated (RR 1.58). Antibody non-response was seen in 3.44% (4/116), low antibody levels in 15.51% (18/116) and medium levels were found in 81.03% (94/116). Fully vaccinated HCWs had a higher antibody response at day 42 than those who were partially vaccinated (8.96 + 4.00 vs. 7.17 + 3.82). Whole genome sequencing of 46 samples revealed that the Delta variant (B.1.617.2) was predominant (69.5%). HCWs who had received two doses of vaccine showed better protection from mild, moderate, or severe infection, with a higher humoral immune response than those who had received a single dose. The genomic analysis revealed the predominance of the Delta variant (B.1.617.2) in the VBT infections.

11.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-296195

ABSTRACT

Immunization is expected to confer protection against infection and severe disease for vaccinees, while reducing risks to unimmunized populations by inhibiting transmission. Here, based on serial serological studies, we show that during a severe SARS-CoV2 Delta-variant outbreak in Delhi, 25.3% (95% CI 16.9 - 35.2) of previously uninfected, ChAdOx1-nCoV19 double vaccinated, healthcare-workers (HCW) were infected within a period of less than two months, based on serology. Induction of anti-spike response was similar between groups with breakthrough infection (541 U/ml, IQR 374) or not (342 U/ml, IQR 497), as was induction of neutralization activity to wildtype. Most infections were unrecognized. The Delta-variant thus causes frequent unrecognized breakthrough infections in adequately immunized subjects, reducing any herd-effect of immunity, and requiring reinstatement of preventive measures such as masking.

12.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-295504

ABSTRACT

The SARS-CoV-2 B.1.617.2 (Delta) variant was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha). In vitro , B.1.617.2 is 6-fold less sensitive to serum neutralising antibodies from recovered individuals, and 8-fold less sensitive to vaccine-elicited antibodies as compared to wild type Wuhan-1 bearing D614G. Serum neutralising titres against B.1.617.2 were lower in ChAdOx-1 versus BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies against the receptor binding domain (RBD) and N-terminal domain (NTD), in particular to the clinically approved bamlavinimab and imdevimab monoclonal antibodies. B.1.617.2 demonstrated higher replication efficiency in both airway organoid and human airway epithelial systems as compared to B.1.1.7, associated with B.1.617.2 spike being in a predominantly cleaved state compared to B.1.1.7. Additionally we observed that B.1.617.2 had higher replication and spike mediated entry as compared to B.1.617.1, potentially explaining B.1.617.2 dominance. In an analysis of over 130 SARS-CoV-2 infected healthcare workers across three centres in India during a period of mixed lineage circulation, we observed substantially reduced ChAdOx-1 vaccine efficacy against B.1.617.2 relative to non-B.1.617.2. Compromised vaccine efficacy against the highly fit and immune evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era.

13.
Sci Rep ; 11(1): 23210, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1545637

ABSTRACT

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66-0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.


Subject(s)
COVID-19/complications , Deep Learning , Expert Systems , Image Processing, Computer-Assisted/methods , Pneumonia/diagnosis , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Algorithms , COVID-19/virology , Humans , Incidence , India/epidemiology , Neural Networks, Computer , Pneumonia/diagnostic imaging , Pneumonia/epidemiology , Pneumonia/virology , Retrospective Studies , SARS-CoV-2/isolation & purification
14.
Science ; 374(6570): 995-999, 2021 Nov 19.
Article in English | MEDLINE | ID: covidwho-1526449

ABSTRACT

Delhi, the national capital of India, experienced multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreaks in 2020 and reached population seropositivity of >50% by 2021. During April 2021, the city became overwhelmed by COVID-19 cases and fatalities, as a new variant, B.1.617.2 (Delta), replaced B.1.1.7 (Alpha). A Bayesian model explains the growth advantage of Delta through a combination of increased transmissibility and reduced sensitivity to immune responses generated against earlier variants (median estimates: 1.5-fold greater transmissibility and 20% reduction in sensitivity). Seropositivity of an employee and family cohort increased from 42% to 87.5% between March and July 2021, with 27% reinfections, as judged by increased antibody concentration after a previous decline. The likely high transmissibility and partial evasion of immunity by the Delta variant contributed to an overwhelming surge in Delhi.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Genome, Viral , Adolescent , Adult , COVID-19/immunology , COVID-19/transmission , Child , Humans , Immune Evasion , India/epidemiology , Molecular Epidemiology , Phylogeny , Reinfection , Seroepidemiologic Studies , Young Adult
15.
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.

16.
Front Med (Lausanne) ; 8: 631769, 2021.
Article in English | MEDLINE | ID: covidwho-1389197

ABSTRACT

Background: SARS-CoV-2 infection may not provide long lasting post-infection immunity. While hundreds of reinfections have reported only a few have been confirmed. Whole genome sequencing (WGS) of the viral isolates from the different episodes is mandatory to establish reinfection. Methods: Nasopharyngeal (NP), oropharyngeal (OP) and whole blood (WB) samples were collected from paired samples of four individuals who were suspected of SARS-CoV-2 reinfection based on distinct clinical episodes and RT-PCR tests. Details from their case record files and investigations were documented. RNA was extracted from the NP and OP samples and subjected to WGS, and the nucleotide and amino acid sequences were subjected to genome and protein-based functional annotation analyses. Serial serology was performed for Anti-N IgG, Anti- S1 RBD IgG, and sVNT (surrogate virus neutralizing test). Findings: Three patients were more symptomatic with lower Ct values and longer duration of illness. Seroconversion was detected soon after the second episode in three patients. WGS generated a genome coverage ranging from 80.07 to 99.7%. Phylogenetic analysis revealed sequences belonged to G, GR and "Other" clades. A total of 42mutations were identified in all the samples, consisting of 22 non-synonymous, 17 synonymous, two in upstream, and one in downstream regions of the SARS-CoV-2 genome. Comparative genomic and protein-based annotation analyses revealed differences in the presence and absence of specific mutations in the virus sequences from the two episodes in all four paired samples. Interpretation: Based on the criteria of genome variations identified by whole genome sequencing and supported by clinical presentation, molecular and serological tests, we were able to confirm reinfections in two patients, provide weak evidence of reinfection in the third patient and unable to rule out a prolonged infection in the fourth. This study emphasizes the importance of detailed analyses of clinical and serological information as well as the virus's genomic variations while assessing cases of SARS-CoV-2 reinfection.

20.
Biosens Bioelectron ; 187: 113280, 2021 Sep 01.
Article in English | MEDLINE | ID: covidwho-1213052

ABSTRACT

In order to define public health policies, simple, inexpensive and robust detection methods for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are vital for mass-testing in resource limited settings. The current choice of molecular methods for identification of SARS-CoV-2 infection includes nucleic acid-based testing (NAT) for viral genetic material and antigen-based testing for viral protein identification. Host exposure is detected using antibody detection assays. While NATs require sophisticated instrument and trained manpower, antigen tests are plagued by their low sensitivity and specificity. Thus, a test offering sensitive detection for presence of infection as a colorimetric readout holds promise to enable mass testing in resource constrained environments by minimally trained personnel. Here we present a novel HRPZyme Assisted Recognition of Infection by Optical Measurement (HARIOM) assay which combines specificity of NATs with sensitivity of enzymatic assays resulting in enhanced signal to noise ratios in an easily interpretable colorimetric readout. Using this assay, we could detect up to 102 copies of synthetic viral RNA spiked in saliva as a detection matrix. Validating our assay on suspected human subjects, we found concordance with PCR based readouts with visible colorimetric distinction between positive and negative samples in less than an hour. We believe that this assay holds the potential to aid in mass screening to detect SARS-CoV-2 infection by facilitating colorimetric detection with minimal resources and less trained personnel.


Subject(s)
Biosensing Techniques , COVID-19 , Humans , Nucleic Acid Amplification Techniques , RNA, Viral , SARS-CoV-2 , Saliva , Sensitivity and Specificity
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