Epidemiological and molecular investigation of a hepatitis A outbreak in Tamil Nadu, Southern India

Introduction: Hepatitis A virus causes an acute infection mainly in young children. The present study was carried out to characterize the nature of hepatitis A virus (HAV) involved in an outbreak of jaundice in children. Methodology: Serum and stool samples from five children were sampled from among 26 clinically diagnosed jaundice cases. HAV IgM ELISA and PCR were used for confirmatory diagnosis and molecular characterization by direct amplicon sequencing and analysis. Results: All the serum samples collected from the symptomatic cases were found to be positive for Anti-HAV IgM ELISA as were all the serum samples and stool samples using semi-nested PCR. Phylogenetic analysis revealed that the HAV involved in the outbreak belonged to genotype IIIA. Conclusions: The infection was caused by HAV genotype IIIA. Improved access to clean drinking water, sanitation around drinking water sources and routine chlorination of drinking water in poor and developing countries are needed, as well as childhood HAV vaccination under regular immunization programs in endemic countries

AMPNet: Attention as Message Passing for Graph Neural Networks

Graph Neural Networks (GNNs) have emerged as a powerful representation learning framework for graph-structured data. A key limitation of conventional GNNs is their representation of each node with a singular feature vector, potentially overlooking intricate details about individual node features. Here, we propose an Attention-based Message-Passing layer for GNNs (AMPNet) that encodes individual features per node and models feature-level interactions through cross-node attention during message-passing steps. We demonstrate the abilities of AMPNet through extensive benchmarking on real-world biological systems such as fMRI brain activity recordings and spatial genomic data, improving over existing baselines by 20% on fMRI signal reconstruction, and further improving another 8% with positional embedding added. Finally, we validate the ability of AMPNet to uncover meaningful feature-level interactions through case studies on biological systems. We anticipate that our architecture will be highly applicable to graph-structured data where node entities encompass rich feature-level information.Comment: 16 pages (12 + 4 pages appendix). 5 figures and 7 table

Hyperglycemia and steroid use increase the risk of rhino-orbito-cerebral mucormycosis regardless of COVID-19 hospitalization: Case-control study, India.

BackgroundIn the ongoing COVID-19 pandemic, an increased incidence of ROCM was noted in India among those infected with COVID. We determined risk factors for rhino-orbito-cerebral mucormycosis (ROCM) post Coronavirus disease 2019 (COVID-19) among those never and ever hospitalized for COVID-19 separately through a multicentric, hospital-based, unmatched case-control study across India.MethodsWe defined cases and controls as those with and without post-COVID ROCM, respectively. We compared their socio-demographics, co-morbidities, steroid use, glycaemic status, and practices. We calculated crude and adjusted odds ratio (AOR) with 95% confidence intervals (CI) through logistic regression. The covariates with a p-value for crude OR of less than 0·20 were considered for the regression model.ResultsAmong hospitalised, we recruited 267 cases and 256 controls and 116 cases and 231 controls among never hospitalised. Risk factors (AOR; 95% CI) for post-COVID ROCM among the hospitalised were age 45-59 years (2·1; 1·4 to 3·1), having diabetes mellitus (4·9; 3·4 to 7·1), elevated plasma glucose (6·4; 2·4 to 17·2), steroid use (3·2; 2 to 5·2) and frequent nasal washing (4·8; 1·4 to 17). Among those never hospitalised, age ≥ 60 years (6·6; 3·3 to 13·3), having diabetes mellitus (6·7; 3·8 to 11·6), elevated plasma glucose (13·7; 2·2 to 84), steroid use (9·8; 5·8 to 16·6), and cloth facemask use (2·6; 1·5 to 4·5) were associated with increased risk of post-COVID ROCM.ConclusionsHyperglycemia, irrespective of having diabetes mellitus and steroid use, was associated with an increased risk of ROCM independent of COVID-19 hospitalisation. Rational steroid usage and glucose monitoring may reduce the risk of post-COVID

A deep generative model of the SARS-CoV-2 spike protein predicts future variants

AbstractSARS-CoV-2 has demonstrated a robust ability to adapt in response to environmental pressures—increasing viral transmission and evading immune surveillance by mutating its molecular machinery. While viral sequencing has allowed for the early detection of emerging variants, methods to predict mutations before they occur remain limited. This work presents SpikeGPT2, a deep generative model based on ProtGPT2 and fine-tuned on SARS-CoV-2 spike (S) protein sequences deposited in the NIH Data Hub before May 2021. SpikeGPT2 achieved 88.8% next-residue prediction accuracy and successfully predicted amino acid substitutions found only in a held-out set of spike sequences deposited on or after May 2021, to which SpikeGPT2 was never exposed. When compared to several other methods, SpikeGPT2 achieved the best performance in predicting such future mutations. SpikeGPT2 also predicted several novel variants not present in the NIH SARS-CoV-2 Data Hub. A binding affinity analysis of all 54 generated substitutions identified 5 (N439A, N440G, K458T, L492I, and N501Y) as predicted to simultaneously increase S/ACE2 affinity, and decrease S/tixagevimab+cilgavimab affinity. Of these, N501Y has already been well-described to increase transmissibility of SARS-CoV-2. These findings indicate that SpikeGPT2 and other similar models may be employed to identify high-risk future variants before viral spread has occurred.</jats:p

Representing Cells As Sentences Enables Natural Language Processing For Single Cell Transcriptomics

Gene expression matrices commonly used in single-cell transcriptomics cannot be directly analyzed with tools developed for natural languages. Restructuring these matrices as abundance-ordered sequences of genes allows the generation of cell sentences: rank-normalized, positionally encoded sequence-structured expression data. The rank-normalization procedure also minimizes batch effects from differential sequencing depth, and comparison of cell sentences against other tools for batch integration shows that cell sentences achieve comparable performance in batch effect removal and biological effect preservation. After transformation, cell sentences can be analyzed using any existing tools from natural language processing that take text as input, enabling a host of new ways to process and understand single-cell transcriptomics data. As an example, a machine translation approach is applied to cells from neural retina, unifying cell and gene representations across species. Finally, this approach can also be used to transform a number of other data modalities to sequential formats, including imaging data. Testing of neural network architectures pretrained on language tasks against those specialized for vision tasks shows that in low-data scenarios, language models can outperform vision models in image classification tasks. These findings suggest homology in the underlying structure of natural language and natural images which may be of particular interest in machine learning for medical imaging, where small datasets are the norm

A survey-wide association study to identify youth-specific correlates of major depressive episodes.

BACKGROUND:Major depressive disorder is a common disease with high mortality and morbidity worldwide. Though peak onset is during late adolescence, the prevalence of major depressive disorder remains high throughout adulthood. Leveraging an association study design, this study screened a large number of variables in the 2017 National Survey on Drug Use and Health to characterize differences between adult and youth depression across a wide array of phenotypic measurements. METHODS:All dichotomous variables were manually identified from the survey for association screening. Association between each dichotomous variable and past-year major depressive episode (MDE) occurrence was calculated as an odds ratio for adults (≥18 years) and youth (12-17 years), and tested for significance with Fischer's exact test. Logarithm of the calculated odds ratios were plotted and fitted to a linear model to assess correlation between adult and youth risk factors. RESULTS:Many of the screened variables showed similar association between past-year depressive episode occurrence in youth and adults; Lin's concordance correlation coefficient between adult and youth associations was 0.91 (95% CI 0.89-0.92). Differentially associated variables were identified, tracking: female sex, alcohol use, cigarette use, marijuana use, Medicaid/CHIP coverage, cognitive changes due to a mental, physical or emotional condition, and respondents' identification of a single depressive event as the worst experienced. CONCLUSIONS:While some youth-specific correlates of major depressive episodes were identified through screening, including some novel associations, most examined variables showed similar association with youth and adult depression. Further study of results is warranted, especially concerning the finding of increased association between marijuana use and depressive episodes in youth