A comparative study of fatty acids profile of two Indian major carps (Gibelion catla, Hamilton, 1822 and Cirrhinus mrigala, Hamilton, 1822) using value added fish feed

Freshwater fishes are not only a major source of protein but they also possess nutritionally valuable lipids in the form of Polyunsaturated fatty acids (PUFAs), which play a crucial role in the normal growth, disease prevention, development, cardiovascular health and reproduction of human. The present study was performed to determine the incorporation rate of fatty acids profile and their composition in two common freshwater carps as Gibelion catla and Cirrhinus mrigala (in situ trial and experimental) in the different experimental time period (0 days, i.e. initial, 90 days and 180 days) by using of value added feed like flaxseed (?-linolenic acids, 51.26% – 54.94%) and soybean oil (?-linolenic acids, 7.95%-9.01%) as omega-3 supplements. To determine the specific growth pattern Length-Weight Relationships (LWRs) are analyzed where it showed positive allometric growth (b=3.20 in 90 days, b=3.11 in 180 days for Catla and b=3.18 in 90 days, b=3.1 in 180 days for Mrigala fish). The Gas Chromatography-Mass Spectrometry (GC/MS) method also confirmed that the percentages of EPA (eicosapentaenoic acid) and DHA (docosahexaenoic acid) increased significantly (P< 0.05) in experimental (0.096a±0.41, initial; 5.16a±0.27, 90 days; 6.21b±0.36, 180 days Catla fish species and 0.019a±0.96 initial; 3.74b±0.37, 90 days; 3.50a±0.46 180 days for Mrigal fish species) fishes rather than controls (4.28a±0.27, 90 days; 4.36b±0.36, 180 days for Catla species and 2.24b±0.31 90 days; 2.50a±0.11 180 days for Mrigal species). Therefore, it was clearly indicated that formulated diet performed significantly to maintain the positive allometric growth as well as successive enrichment of PUFAs in experimental specimens, which is beneficial for human health as high source of protein and PUFAs as well

Comparative Genomics, Evolutionary Epidemiology, and RBD-hACE2 Receptor Binding Pattern in B.1.1.7 (Alpha) and B.1.617.2 (Delta) Related to Their Pandemic Response in UK and India

BACKGROUND: The massive increase in COVID-19 infection had generated a second wave in India during May-June 2021 with a critical pandemic situation. The Delta variant (B.1.617.2) was a significant factor during the second wave. Conversely, the UK had passed through the crucial phase of the pandemic from November to December 2020 due to B.1.1.7. The study tried to comprehend the pandemic response in the UK and India to the spread of the B.1.1.7 (Alpha, UK) variant and B.1.617.2 (Delta, India) variant. METHODS: This study was performed in three directions to understand the pandemic response of the two emerging variants. First, we served comparative genomics, such as genome sequence submission patterns, mutational landscapes, and structural landscapes of significant mutations (N501Y, D614G, L452R, E484Q, and P681R). Second, we performed evolutionary epidemiology using molecular phylogenetics, scatter plots of the cluster evaluation, country-wise transmission pattern, and frequency pattern. Third, the receptor binding pattern was analyzed using the Wuhan reference strain and the other two variants. RESULTS: The study analyzed the country-wise and region-wise genome sequences and their submission pattern, molecular phylogenetics, scatter plot of the cluster evaluation, country-wise geographical distribution and transmission pattern, frequency pattern, entropy diversity, and mutational landscape of the two variants. The structural pattern was analyzed in the N501Y, D614G L452R, E484Q, and P681R mutations. The study found increased molecular interactivity between hACE2-RBD binding of B.1.1.7 and B.1.617.2 compared to the Wuhan reference strain. Our receptor binding analysis showed a similar indication pattern for hACE2-RBD of these two variants. However, B.1.617.2 offers slightly better stability in the hACE2-RBD binding pattern through MD simulation than B.1.1.7. CONCLUSION: The increased hACE2-RBD binding pattern of B.1.1.7 and B.1.617.2 might help to increase the infectivity compared to the Wuhan reference strain

Exploring Large Language Models for Code Explanation

Automating code documentation through explanatory text can prove highly beneficial in code understanding. Large Language Models (LLMs) have made remarkable strides in Natural Language Processing, especially within software engineering tasks such as code generation and code summarization. This study specifically delves into the task of generating natural-language summaries for code snippets, using various LLMs. The findings indicate that Code LLMs outperform their generic counterparts, and zero-shot methods yield superior results when dealing with datasets with dissimilar distributions between training and testing sets.Comment: Accepted at the Forum for Information Retrieval Evaluation 2023 (IRSE Track

Overview of Chatbots with special emphasis on artificial intelligence-enabled ChatGPT in medical science

The release of ChatGPT has initiated new thinking about AI-based Chatbot and its application and has drawn huge public attention worldwide. Researchers and doctors have started thinking about the promise and application of AI-related large language models in medicine during the past few months. Here, the comprehensive review highlighted the overview of Chatbot and ChatGPT and their current role in medicine. Firstly, the general idea of Chatbots, their evolution, architecture, and medical use are discussed. Secondly, ChatGPT is discussed with special emphasis of its application in medicine, architecture and training methods, medical diagnosis and treatment, research ethical issues, and a comparison of ChatGPT with other NLP models are illustrated. The article also discussed the limitations and prospects of ChatGPT. In the future, these large language models and ChatGPT will have immense promise in healthcare. However, more research is needed in this direction

Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection.

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development

A Detailed Overview of SARS-CoV-2 Omicron: Its Sub-Variants, Mutations and Pathophysiology, Clinical Characteristics, Immunological Landscape, Immune Escape, and Therapies

The COVID-19 pandemic has created significant concern for everyone. Recent data from many worldwide reports suggest that most infections are caused by the Omicron variant and its sub-lineages, dominating all the previously emerged variants. The numerous mutations in Omicron’s viral genome and its sub-lineages attribute it a larger amount of viral fitness, owing to the alteration of the transmission and pathophysiology of the virus. With a rapid change to the viral structure, Omicron and its sub-variants, namely BA.1, BA.2, BA.3, BA.4, and BA.5, dominate the community with an ability to escape the neutralization efficiency induced by prior vaccination or infections. Similarly, several recombinant sub-variants of Omicron, namely XBB, XBD, and XBF, etc., have emerged, which a better understanding. This review mainly entails the changes to Omicron and its sub-lineages due to it having a higher number of mutations. The binding affinity, cellular entry, disease severity, infection rates, and most importantly, the immune evading potential of them are discussed in this review. A comparative analysis of the Delta variant and the other dominating variants that evolved before Omicron gives the readers an in-depth understanding of the landscape of Omicron’s transmission and infection. Furthermore, this review discusses the range of neutralization abilities possessed by several approved antiviral therapeutic molecules and neutralizing antibodies which are functional against Omicron and its sub-variants. The rapid evolution of the sub-variants is causing infections, but the broader aspect of their transmission and neutralization has not been explored. Thus, the scientific community should adopt an elucidative approach to obtain a clear idea about the recently emerged sub-variants, including the recombinant variants, so that effective neutralization with vaccines and drugs can be achieved. This, in turn, will lead to a drop in the number of cases and, finally, an end to the pandemic

The landscape of neoantigens and its clinical applications: From immunobiology to cancer vaccines

Since millions of cancer-related deaths and diagnoses exist yearly, malignant tumors are a primary worldwide health concern. A promising method for treating cancer is tumor immunotherapy, which focuses on neoantigens. Neoantigens are tumor-specific antigens expressed on cancer cells due to genetic changes, viral infections, or other biological processes. They serve as excellent immune system targets to identify and attack cancerous cells. Neoantigens are more immunogenic than tumor-associated antigens (TAAs) because they lack central tolerance. Successful clinical trials of neoantigen-based vaccines have raised interest in individualized tumor immunotherapy. Furthermore, neoantigens represent a significant advancement in cancer immunotherapy, offering the potential for personalized and effective tumor treatments. The identification, synthesis, and application of neoantigen-based vaccines hold promise for improving patient outcomes and revolutionizing cancer treatment approaches. This review focuses on the significance of neoantigens in cancer immunotherapy, their classification and identification, the synthesis of neoantigen vaccines, clinical trials and the principles underlying their therapeutic efficacy