Monkeypox: A Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications.

Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Hub genes, key miRNAs and interaction analyses in type 2 diabetes mellitus: an integrative in silico approach.

Diabetes is a rising global metabolic disorder and leads to long-term consequences. As a multifactorial disease, the gene-associated mechanisms are important to know. This study applied a bioinformatics approach to explore the molecular underpinning of type 2 diabetes mellitus through differential gene expression analysis. We used microarray datasets GSE16415 and GSE29226 to identify differentially expressed genes between type 2 diabetes and normal samples using R software. Following that, using the STRING database, the protein-protein interaction network was constructed and further analyzed by Cytoscape software. The EnrichR database was used for Gene Ontology and pathway enrichment analysis to explore key pathways and functional annotations of hub genes. We also used miRTarBase and TargetScan databases to predict miRNAs targeting hub genes. We identified 21 hub genes in type 2 diabetes, some showing more significant changes in the PPI network. Our results revealed that GLUL, SLC32A1, PC, MAPK10, MAPT, and POSTN genes are more important in the PPI network and can be experimentally investigated as therapeutic targets. Hsa-miR-492 and hsa-miR-16-5p are suggested for diagnosis and prognosis by targeting GLUL, SLC32A1, PC, MAPK10, and MAPT genes involved in the insulin signaling pathway. Insight: Type 2 diabetes, as a rising global and multifactorial disorder, is important to know the gene-associated mechanisms. In an integrative bioinformatics analysis, we integrated different finding datasets to put together and find valuable diagnostic and prognostic hub genes and miRNAs. In contrast, genes, RNAs, and enzymes interact systematically in pathways. Using multiple databases and software, we identified differential expression between hub genes of diabetes and normal samples. We explored different protein-protein interaction networks, gene ontology, key pathway analysis, and predicted miRNAs that target hub genes. This study reported 21 significant hub genes and some miRNAs in the insulin signaling pathway for innovative and potential diagnostic and therapeutic purposes.