Composition of nasopharyngeal microbiota in individuals with SARS-COV-2 infection across three COVID-19 waves in India (preprint)

Background: SARS-COV-2 is an enveloped RNA virus that is responsible for the global pandemic COVID-19. The virus is reported to cause dysbiosis of the Human Nasopharyngeal microbiota, consequently regulating the host immunity and infection pathophysiology. The compositional change in microbial diversity due to the virus has been reported by independent authors in smaller cohorts and different geographical regions, with a few correlating with fungal and bacterial co-infections. Here, we study for the first time, the nasopharyngeal microbial diversity in the COVID-19 patients, across the three waves in India and explore its correlation with the causative virus variant (and/or the severity of symptoms, if any). Methods: We profiled the nasopharyngeal microbiota of 589 Indian subjects, across the three waves (First; n=181, Second; n=217, Third; n=191), which were further categorized as COVID-19 positives and COVID-19 negatives. These respective groups were further divided into subgroups based on the symptoms as Asymptomatic and Symptomatic. The nasopharyngeal swabs were collected from subjects providing samples for diagnostics purposes at the Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India. Using high throughput 16S rRNA gene amplicon-based sequencing, we sequenced and profiled the nasopharyngeal DNA microbiome prior to subjecting them to diversity, composition and network analyses. Results: Patients infected with SARS-COV-2 showed a reduced microbial alpha diversity compared to the COVID-19 negatives, in a wave-dependent manner, as implicated by measuring the alpha diversity indices. Furthermore, the compositional change in the community was found to be significantly associated with the viral load as well as the severity of the symptoms observed in the patients. Preliminary taxonomic analysis indicated that, overall, Firmicutes, Proteobacteria, and Actinobacteriota were amongst the dominating Phyla, while Staphylococcaceae and Corynebacteriaceae were the most abundant Families. Also, the microbiota signatures of the first and third wave were more similar to each other at the phylum level compared to the second wave. However, the abundance of microbes varied greatly between the major groups i.e COVID-19 positives and the negatives at the family level, in the respective waves. A similar observation was made where both the commensals and pathobionts differed in abundance between the patient subgroups. Interestingly, the change in microbial network architecture from first to second wave was driven by opportunistic pathogens such as Paenibacillus, Peptostreptococcus, and Solobacterium while Leptotrichia and Actinomyces were noted to be taxonomic groups driving the changes during the third wave when compared to the second wave. Conclusion: In the Indian cohort examined, SARS-COV-2 infection perturbs the nasopharyngeal microbiome, resulting in lower & varied diversity in the niche, irrespective of the virus variant (& thus, the COVID wave) and the disease severity. Whether these changes assist in COVID-19 disease onset & progression, would be interesting to explore in the future.

Tracking mutational semantics of SARS-CoV-2 genomes.

Natural language processing (NLP) algorithms process linguistic data in order to discover the associated word semantics and develop models that can describe or even predict the latent meanings of the data. The applications of NLP become multi-fold while dealing with dynamic or temporally evolving datasets (e.g., historical literature). Biological datasets of genome-sequences are interesting since they are sequential as well as dynamic. Here we describe how SARS-CoV-2 genomes and mutations thereof can be processed using fundamental algorithms in NLP to reveal the characteristics and evolution of the virus. We demonstrate applicability of NLP in not only probing the temporal mutational signatures through dynamic topic modelling, but also in tracing the mutation-associations through tracing of semantic drift in genomic mutation records. Our approach also yields promising results in unfolding the mutational relevance to patient health status, thereby identifying putative signatures linked to known/highly speculated mutations of concern.

Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?

MOTIVATION: Continuous emergence of new variants through appearance/accumulation/disappearance of mutations is a hallmark of many viral diseases. SARS-CoV-2 variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of variants and huge scale of genomic data have added to the challenges of tracing the mutations/variants and their relationship to infection severity (if any). RESULTS: We explored the suitability of virus-genotype guided machine-learning in infection prognosis and identification of features/mutations-of-interest. Total 199,519 outcome-traced genomes, representing 45,625 nucleotide-mutations, were employed. Among these, post data-cleaning, Low and High severity genomes were classified using an integrated model (employing virus genotype, epitopic-influence and patient-age) with consistently high ROC-AUC (Asia:0.97 ± 0.01, Europe:0.94 ± 0.01, N.America:0.92 ± 0.02, Africa:0.94 ± 0.07, S.America:0.93 ± 03). Although virus-genotype alone could enable high predictivity (0.97 ± 0.01, 0.89 ± 0.02, 0.86 ± 0.04, 0.95 ± 0.06, 0.9 ± 0.04), the performance was not found to be consistent and the models for a few geographies displayed significant improvement in predictivity when the influence of age and/or epitope was incorporated with virus-genotype (Wilcoxon p_BH < 0.05). Neither age or epitopic-influence or clade information could out-perform the integrated features. A sparse model (6 features), developed using patient-age and epitopic-influence of the mutations, performed reasonably well (>0.87 ± 0.03, 0.91 ± 0.01, 0.87 ± 0.03, 0.84 ± 0.08, 0.89 ± 0.05). High-performance models were employed for inferring the important mutations-of-interest using Shapley Additive exPlanations (SHAP). The changes in HLA interactions of the mutated epitopes of reference SARS-CoV-2 were then subsequently probed. Notably, we also describe the significance of a 'temporal-modeling approach' to benchmark the models linked with continuously evolving pathogens. We conclude that while machine learning can play a vital role in identifying relevant mutations and factors driving the severity, caution should be exercised in using the genotypic signatures for predictive prognosis.

IoT Based System for Home Monitoring of Human Vital Signs

Due to current pandemic situation, it is difficult for old/aged people to go out to hospitals for consulting the doctor. The people in rural areas cannot obtain professional healthcare services or emergency medical facilities due to long distance to hospital from their home, lack of doctors, hospitals and also lack of specialist doctor. The people in urban areas cannot find time to go to hospitals for their monthly checkup or for any small health issues. Hence the solution for all these issues or problems is, health care monitoring system using technology called IoT. As we are in the generation of industry 4.0, technologies such as IoT, big data, machine learning, artificial intelligence, play a vital role in our day-To-day life. As the technology is growing day by day, life is also becoming much simpler, better, faster, smart with the use of these technologies. Also by the application of these technologies, we can reduce human efforts, where by sitting at one place we can perform many tasks. Health care being a global issue, especially in India with more population, where most of who stay in rural areas are deprived of health care services. With industry 4.0 technology, we can build a IoT based device for monitoring the human vital signs, Using which, we can communicate between networked devices wirelessly, which would help the patient to get better treatment or better consultation from doctor without consulting the doctor physically. In the current situation, this system can also be effectively used for constantly monitoring covid patients requiring home isolation. © 2021 IEEE.

Tracking mutational semantics of SARS-CoV-2 genomes (preprint)

Genomes have an inherent context dictated by the order in which the nucleotides and higher order genomic elements are arranged in the DNA/RNA. Learning this context is a daunting task, governed by the combinatorial complexity of interactions possible between ordered elements of genomes. Can natural language processing be employed on these orderly, complex and also evolving datatypes (genomic sequences) to reveal the latent patterns or context of genomic elements (e.g Mutations)? Here we present an approach to understand the mutational landscape of Covid-19 by treating the temporally changing (continuously mutating) SARS-CoV-2 genomes as documents. We demonstrate how the analogous interpretation of evolving genomes to temporal literature corpora provides an opportunity to use dynamic topic modeling (DTM) and temporal Word2Vec models to delineate mutation signatures corresponding to different Variants-of-Concerns and tracking the semantic drift of Mutations-of-Concern (MoC). We identified and studied characteristic mutations affiliated to Covid-infection severity and tracked their relationship with MoCs. Our ground work on utility of such temporal NLP models in genomics could supplement ongoing efforts in not only understanding the Covid pandemic but also provide alternative strategies in studying dynamic phenomenon in biological sciences through data science (especially NLP, AI/ML).

Strategy to prevent infection from Covid-19 among security officers of tertiary care centre: A preexperimental study.

BACKGROUND: The extensive spread of Covid-19 pandemic globally became the main cause of concern for everyone, including security officers working in a health care setting. OBJECTIVE: To assess the effectiveness of instructional module for Covid-19 prevention among hospital security officers. METHODS AND MATERIALS: A preexperimental study was conducted at a tertiary care hospital from North India. A total of 344 security officers were selected by the convenient sampling technique. A self-structured knowledge and practice questionnaires and instructional module were developed based on the guidelines released by World Health Organization, Centre for Disease Control and Prevention and Ministry of Health and Family Welfare. Knowledge and practice were pretested, followed by the implementation of a video cum discussion instructional module for Covid-19 prevention. A posttest of knowledge and practice assessment was done after 7 days by using the same questionnaire. Descriptive and inferential statistics were used to compute and analyse the data. RESULTS: The mean age of participants was 29.5 ± 2.25; mos participants (75%) were male security officers. Knowledge and practice scores improved after the implementation of instructional module as mean scores of pretest to mean posttest scores had shown a significant difference (P = 0.00). In practice, instructional module was significantly effective, except for touching hair again and again, as it could be a source of covid-19 infection. CONCLUSION: This study finding highlights the significance of training security officers about the prevention of Covid-19.

Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups?

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understanding the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. Results indicate that the immune recognition potential of SARS-CoV2 epitopes tend to vary between different ethnic groups. While the South Asians are likely to recognize higher number of CD8-specific epitopes, Europeans are likely to identify higher number of CD4-specific epitopes. We also hypothesize and provide clues that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gaging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ∼40,000 viral genomes.

(Machine) Learning the mutation signatures of SARS-CoV-2: a primer for predictive prognosis (preprint)

MotivationContinuous emergence of new variants through appearance, accumulation and disappearance of mutations in viruses is a hallmark of many viral diseases. SARS-CoV-2 and its variants have particularly exerted tremendous pressure on global healthcare system owing to their life threatening and debilitating implications. The sheer plurality of the variants and huge scale of genome sequence data available for Covid19 have added to the challenges of traceability of mutations of concern. The latter however provides an opportunity to utilize SARS-CoV-2 genomes and the mutations therein as big data records to comprehensively classify the variants through the (machine) learning of mutation patterns. The unprecedented sequencing effort and tracing of disease outcomes provide an excellent ground for identifying important mutations by developing machine learnt models or severity classifiers using mutation profile of SARS-CoV-2. This is expected to provide a significant impetus to the efforts towards not only identifying the mutations of concern but also exploring the potential of mutation driven predictive prognosis of SARS-CoV-2. ResultsWe describe how a graduated approach of building various severity specific machine learning classifiers, using only the mutation corpus of SARS-CoV-2 genomes, can potentially lead to the identification of important mutations and guide potential prognosis of infection. We demonstrate the applicability of model derived important mutations and use of Shapley values in order to identify the significant mutations of concern as well as for developing sparse models of outcome classification. A total of 77,284 outcome traced SARS-CoV-2 genomes were employed in this study which represented a total corpus of 30346 unique nucleotide mutations and 18647 amino acid mutations. Machine learning models pertaining to graduated classifiers of target outcomes namely Asymptomatic, Mild, Symptomatic/Moderate, Severe and Fatal were built considering the TRIPOD guidelines for predictive prognosis. Shapley values for model linked important mutations were employed to select significant mutations leading to identification of less than 20 outcome driving mutations from each classifier. We additionally describe the significance of adopting a temporal modeling approach to benchmark the predictive prognosis linked with continuously evolving pathogens. A chronologically distinct sampling is important in evaluating the performance of models trained on past data in accurately classifying prognosis linked with genomes of future (observed with new mutations). We conclude that while machine learning approach can play a vital role in identifying relevant mutations, caution should be exercised in using the mutation signatures for predictive prognosis in cases where new mutations have accumulated along with the previously observed mutations of concern. Contactsharmila.mande@tcs.com Supplementary informationSupplementary data are enclosed.

Assessment of knowledge, attitude, and practice of recent diagnostic aids for oral cancer among dental students

Aim: The aim of the present study was to assess the knowledge, attitude and practice of recent diagnostic aids for oral cancer among dental students. Materials and Methods: This study was conducted among 100 students using a 15-item self-administered questionnaire during COVID-19 pandemic period involving both undergraduates and postgraduate dental students.It was formatted on google forms and shared through social media. The data collected was analysed statistically. Results: A total of 100 dental studentsparticipated in the survey. The total knowledge, attitude, and practice about recent diagnostic aids for oral cancer (based on summing all the positive responses--for all the relevant questions--and calculating the percentage) was 90% for post-graduates (highest), 88% for interns group, 84% for final years and 80% for third years (least) respectively.The results of this study show that postgraduates had good knowledge about recent diagnostic aids for oral cancer when compared to other groups. cancer at earlier stages as they routinely examine oral cavity.

Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups? (preprint)

The SARS-CoV2 mediated Covid-19 pandemic has impacted humankind at an unprecedented scale. While substantial research efforts have focused towards understand the mechanisms of viral infection and developing vaccines/ therapeutics, factors affecting the susceptibility to SARS-CoV2 infection and manifestation of Covid-19 remain less explored. Given that the Human Leukocyte Antigen (HLA) system is known to vary among ethnic populations, it is likely to affect the recognition of the virus, and in turn, the susceptibility to Covid-19. To understand this, we used bioinformatic tools to probe all SARS-CoV2 peptides which could elicit T-cell response in humans. We also tried to answer the intriguing question of whether these potential epitopes were equally immunogenic across ethnicities, by studying the distribution of HLA alleles among different populations and their share of cognate epitopes. We provide evidence that the newer mutations in SARS-CoV2 are unlikely to alter the T-cell mediated immunogenic responses among the studied ethnic populations. The work presented herein is expected to bolster our understanding of the pandemic, by providing insights into differential immunological response of ethnic populations to the virus as well as by gauging the possible effects of mutations in SARS-CoV2 on efficacy of potential epitope-based vaccines through evaluating ~40000 viral genomes.

What if we perceive SARS-CoV-2 genomes as documents? Topic modelling using Latent Dirichlet Allocation to identify mutation signatures and classify SARS-CoV-2 genomes (preprint)

Topic modeling is frequently employed for discovering structures (or patterns) in a corpus of documents. Its utility in text-mining and document retrieval tasks in various fields of scientific research is rather well known. An unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) has particularly been utilized for identifying latent (or hidden) topics in document collections and for deciphering the words that define one or more topics using a generative statistical model. Here we describe how SARS-CoV-2 genomic mutation profiles can be structured into a Bag of Words to enable identification of signatures (topics) and their probabilistic distribution across various genomes using LDA. Topic models were generated using ~47000 novel corona virus genomes (considered as documents), leading to identification of 16 amino acid mutation signatures and 18 nucleotide mutation signatures (equivalent to topics) in the corpus of chosen genomes through coherence optimization. The document assumption for genomes also helped in identification of contextual nucleotide mutation signatures in the form of conventional N-grams (e.g. bi-grams and tri-grams). We validated the signatures obtained using LDA driven method against the previously reported recurrent mutations and phylogenetic clades for genomes. Additionally, we report the geographical distribution of the identified mutation signatures in SARS-CoV-2 genomes on the global map. Use of the non-phylogenetic albeit classical approaches like topic modeling and other data centric pattern mining algorithms is therefore proposed for supplementing the efforts towards understanding the genomic diversity of the evolving SARS-CoV-2 genomes (and other pathogens/microbes).