Insights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV2 - Naushin, Salwa; Sardana, Viren; Ujjainiya, Rajat; Bhatheja, Nitin; Kutum, Rintu; Bhaskar, Akash Kumar; Pradhan, Shalini; Prakash, Satyartha; Khan, Raju; Rawat, Birendra Singh; Tallapaka, Karthik Bharadwaj; Anumalla, Mahesh; et al. (eLife, 2021)
To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92-2.59, <0.0001), use of public transport (1.79, 1.43-2.24, <0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, <0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys - 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; et al. (Computers in Biology and Medicine, 2022)
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.
Keywords: BBV152; Covaxin; COVID-19; Ensemble methods; Infection; machine learning; SARS-CoV-2

Hexagonal boron nitride quantum dots embedded on layer-by-layer films for peroxidase-assisted colorimetric detection of β-galactosidase producing pathogens - Majumdar, Sristi; Gogoi, Devipriya; Boruah, Purna K.; Thakur, Ashutosh; Sarmah, Priyakhee; Gogoi, Parishmita; Sarkar, Sanjib; Pachani, Priyakshi; Manna, Prasenjit; Saikia, Ratul; Chaturvedi, Vikash; Shelke, Manjusha V.; et al. (ACS Applied Materials and Interfaces, 2024)
Pathogen detection has become a major research area all over the world for water quality surveillance and microbial risk assessment. Therefore, designing simple and sensitive detection kits plays a key role in envisaging and evaluating the risk of disease outbreaks and providing quality healthcare settings. Herein, we have designed a facile and low-cost colorimetric sensing strategy for the selective and sensitive determination of beta-galactosidase producing pathogens. The hexagonal boron nitride quantum dots (h-BN QDs) were established as a nanozyme that showed prominent peroxidase-like activity, which catalyzes 3,3 `,5,5 `-tetramethylbenzidine (TMB) oxidation by H2O2. The h-BN QDs were embedded on a layer-by-layer assembled agarose biopolymer. The beta-galactosidase enzyme partially degrades beta-1,4 glycosidic bonds of agarose polymer, resulting in accessibility of h-BN QDs on the solid surface. This assay can be conveniently conducted and analyzed by monitoring the blue color formation due to TMB oxidation within 30 min. The nanocomposite was stable for more than 90 days and was showing TMB oxidation after incubating it with Escherichia coli (E. coli). The limit of detection was calculated to be 1.8 x 10(6) and 1.5 x 10(6) CFU/mL for E. coli and Klebsiella pneumonia (K. pneumonia), respectively. Furthermore, this novel sensing approach is an attractive platform that was successfully applied to detect E. coli in spiked water samples and other food products with good accuracy, indicating its practical applicability for the detection of pathogens in real samples.
Keywords: biopolymer; colorimetric; h-BN quantum dots; nanozyme; pathogens; Sensor