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Valproic acid (VPA) is a well-established anticonvulsant drug discovered serendipitously and marketed for the treatment of epilepsy, migraine, bipolar disorder and neuropathic pain. Apart from this, VPA has potential therapeutic applications in other central nervous system (CNS) disorders and in various cancer types. Since the discovery of its anticonvulsant activity, substantial efforts have been made to develop structural analogues and derivatives in an attempt to increase potency and decrease adverse side effects, the most significant being teratogenicity and hepatotoxicity. Most of these compounds have shown reduced toxicity with improved potency. The simple structure of VPA offers a great advantage to its modification. This review briefly discusses the pharmacology and molecular targets of VPA. The article then elaborates on the structural modifications in VPA including amide-derivatives, acid and cyclic analogues, urea derivatives and pro-drugs, and compares their pharmacological profile with that of the parent molecule. The current challenges for the clinical use of these derivatives are also discussed. The review is expected to provide necessary knowledgebase for the further development of VPA-derived compounds.

Recent Technological Advances in cell-sorting and high throughput sequencing have enabled unprecedented insight into the cell at the resolution of single cells and have further led to a massive accumulation of single omics data such as genomics, proteomics, epigenomics and transcriptomics etc. In this article, we have presented the dissection of the single-cell resolution of gene expression, which in turn reshapes a large number of transcriptomic studies. We have presented the pre-processing and clustering of human uveal melanoma disease

Every year over 1.5 million men develop prostate cancer and it is also a significant cause of mortality among men. There are multiple factors upregulating prostate cancer, which includes mutation in the genetics, ethnicity or hereditary. Out of 23 MMPs, there are 4 MMPs which are mostly upregulated in prostate cancer. MMPs function by deteriorating several proteins which are present in the extracellular matrix. Catharanthus roseus is a medicinal plant which has antidiabetic, anticancer, antispasmodic and antihypertensive properties. So, the prime focus of the study is to identify phytochemicals present in Catharanthus roseus that are potent against MMPs which are usually upregulated in prostate cancer, i.e., MMP9, MMP1, MMP10, and MMP13 via static simulation having lesser or no side effects.

Merkel Cell Carcinoma (MCC) is a destructive form of neuroendocrine carcinoma that represents the 2nd highest cause of skin cancer related deaths despite being a relatively rare form of cancer. Different risk factors have been associated with MCC such as advancement age, immunosuppression, and ultraviolet light exposure, polyomavirus infection and a current, concurrent or previous diagnosis of Chronic Lymphocytic Leukaemia (CLL). However, the exact mechanism that leads to MCC and its inherent biology is yet to be fully researched and explored. The current treatments for MCC are a combination of surgery and radiation or chemo-radiation. So far, only two drugs have been approved by FDA for the treatment of MCC, with both drugs being immune checkpoint inhibitors. However, the drug approval from FDA was accelerated based on promising results from a relatively small number of patients, which means that the more research and trials are undergoing currently considering the fact that a very small cohort was used for clinical trials. Presently there are no biomarkers indicative of MCC and additional research is essential. With the advent of bioinformatics and microarray technology, in silico approaches pertaining to ‘omics’ data analysis, shall allow identification of hub genes and miRNA to broaden our understanding of MCC biology and their clinical utility.

Human parainfluenza viruses (HPIVs) are ( -)ssRNA viruses belonging to Paramyoviridaie family. They are one of the leading causes of mortality in infants and young children and can cause ailments like croup, bronchitis, and pneumonia. Currently, no antiviral medications or vaccines are available to effectively treat parainfluenza. This necessitates the search for a novel and effective treatment. Computer-aided drug design (CADD) methodology can be utilized to discover target-based inhibitors with high accuracy in less time. A library of 45 phytocompounds with immunomodulatory properties was prepared. Thereafter, molecular docking studies were conducted to characterize the binding behavior of ligand in the binding pocket of HPIV3 HN protein. The physicochemical properties for screened compounds were computed, and the top hits from docking studies were further analyzed and validated using molecular dynamics simulation studies using the Desmond module of Schrodinger Suite 2021-1, followed by MM/GBSA analysis. The compounds CID:72276 (1) and CID:107905 (2) emerged as lead compounds of our in silico investigation. Further in vitro studies will be required to prove the efficacy of lead compounds as inhibitors and to determine the exact mechanism of their inhibition. Computational studies predict three natural flavonoids to inhibit the HN protein of HPIV3.

Melanoma is a type of skin cancer. Numerous genes and their proteins are strongly associated with melanoma susceptibility. This study aims to use an in silico method to identify genetic variants in the melanoma susceptibility gene. The COSMIC database was queried for genes and cross-referenced with three environment-gene interaction databases (EGP, SeattleSNPs and CTD) to identify shared genes. The majority of approved skin melanoma drugs were found to act on the protein serine/threonine-protein kinase (B-RAF) encoded by the BRAF gene, which was also present in all three referenced databases. Comprehensive computational analysis was performed to predict deleterious genetic variants associated with skin melanoma, and the nsSNPs G469V and G469E were prioritized based on their predicted deleterious effects. Molecular dynamic simulation analysis of the B-RAF protein mutants G469V and G469E reveals that variations in the amino acid conformation at the drug binding site result in inconsistency in drug interaction. Additionally, this analysis showed that the G469V and G469E mutants have lower binding energy for dabrafenib than the wild type. The population with the highest frequency of each deleterious and pathogenic variant has been determined. The study's findings would support the development of more effective treatment strategies for skin melanoma. Communicated by Ramaswamy H. Sarma.

CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea is to extract the features from sequence of mRNA using CNN and use LSTM network for prediction of miRNA. The model has learned the basic features such as seed match at first 2–8 nucleotides starting at the $5^\prime $ 5 ' end and counting toward the $3^\prime $ 3 ' end. Also, it was able to predict G-U wobble base pair in seed region. While validating on experimentally validated data, the model was able to predict on average 72 percent of miRNAs for specific mRNA and shows highest positive expression fold change of predicted targets on a microarray data generated using anti 25 miRNAs compare to other predicted tools. Codes are available at https://github.com/rajkumar1501/sequence-prediction-using-CNN-and-LSTMs .

The periodic ingestion of a protein-rich blood meal by adult female mosquitoes causes a drastic metabolic change in their innate physiological status, which is referred to as a 'metabolic switch'. While understanding the neural circuits for host-seeking is modestly attended, how the gut 'metabolic switch' modulates brain functions, and resilience to physiological homeostasis, remains unexplored. Here, through a comparative brain RNA-Seq study, we demonstrate that the protein-rich diet induces the expression of brain transcripts related to mitochondrial function and energy metabolism, possibly causing a shift in the brain's engagement to manage organismal homeostasis. A dynamic mRNA expression pattern of neuro-signaling and neuro-modulatory genes in both the gut and brain likely establishes an active gut-brain communication. The disruption of this communication through decapitation does not affect the modulation of the neuro-modulator receptor genes in the gut. In parallel, an unusual and paramount shift in the level of neurotransmitters (NTs), from the brain to the gut after blood feeding, further supports the idea of the gut's ability to serve as a 'second brain'. After blood-feeding, a moderate enrichment of the gut microbial population, and altered immunity in the gut of histamine receptor-silenced mosquitoes, provide initial evidence that the gut-microbiome plays a crucial role in gut-brain-axis communication. Finally, a comparative metagenomics evaluation of the gut microbiome highlighted that blood-feeding enriches the family members of the Morganellaceae and Pseudomonadaceae bacterial communities. The notable observation of a rapid proliferation of

Aquaculture is a sector that has seen unprecedented growth in recent years by acting as an alternative source for seafood production to fulfill global food demands and to compensate for the overexploitation of fishing. Various parameters such as environmental factors, production factors such as water quality, and biotic factors have to be monitored constantly to prevent losses in yield and increase efficiency. The Internet of Things (IoT) has transformed the aquaculture sector by providing real-time monitoring solutions remotely without much human intervention. Usually, the IoT technologies in aquaculture use a local server and store the huge amount of generated data in an Excel file or a database. This in turn exerts a lot of pressure on the Internet infrastructure. Cloud computing complements IoT by providing a virtual storage place and pathway for the vast amounts of data and programs to be stored and accessed. It not only increases the efficiency and scalability of the techniques but also allows a better collaboration between developers situated remotely. This chapter introduces the readers to the basic concepts of cloud computing and identifies the current area of its application in IoT. We will see how cloud computing can integrate with IoT technologies and offer more accountable solutions to various challenges. It also provides a discussion on future scope and trends for the same.

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.

As the scientific know-how of the people around the world is expanding, the requirement of new technologies is also growing rapidly. This is evident by the number of papers being published and the new discoveries of scientists that are changing the definition of impossibility day by day. This paper explains one such technology which has made possible not only the recognition of natural language (i.e., human language) by computers but generation of speech and text which is natural language processing (NLP). When machine learning came into picture for assaying large amount data (statistical), deriving meaning from data became easy. Statistical prediction could be made for data containing millions of data points. However, analysis and prediction from textual data still remained a challenge. In 1950s, Alan Turing’s publication—Computing Machinery and Intelligence, introduced NLP in computational field which dealt with conversion of human language to machine-readable form and generated written or spoken output. NLP can be further be applied in bioinformatics for deducing the structure and function of a protein from its primary chain sequence or deriving end products of functional genes from their basal sequences as many researchers have found the sequences to be similar to human language calling it the ‘language of life’. Current studies are based upon using the rules of NLP in analyzing gene and protein sequences. This article is aimed at exploring the various applications of natural language processing in the field of bioinformatics and medical informatics.