Design of novel isoxazole derivatives as tubulin inhibitors using computer-aided techniques: QSAR modeling, in silico ADMETox, molecular docking, molecular dynamics, biological efficacy, and retrosynthesis.

In the current work, computational methods were used to investigate new isoxazole derivatives that could be used as tubulin inhibitors. The study aims to develop a reliable quantitative structure-activity relationship (QSAR) model, following the criteria set by Golbraikh, Tropsha, and Roy. As a result, seven candidate compounds were developed, all having higher activity than the well-established anticancer agent Cisplatin (Cisp). According to the ADMETox in silico test, the candidates Pr4, Pr5, and P6 can be toxic. As a result, we have chosen to focus our study on compounds Pr1, Pr2, and Pr3. Molecular docking analysis revealed that drug candidate Pr2 exhibits the highest stability within the oxidized quinone reductase 2 (PDB ID: 4zvm), target receptor (ΔG(Pr2) = ΔG(Pr3) = -10.4 < ΔG(Pr1) = -10.0 < ΔG(Cisp) = -7.3 kcal/mol). This finding aligns with the activity predictions made by the QSAR model. Furthermore, molecular dynamics simulations of the Pr2-4zvm complex over 100 ns confirm the ligand's robust stability within the receptor's active site, supporting the results obtained from molecular docking and the QSAR model predictions. The CaverDock software was utilized to identify the tunnels likely to be followed by ligands moving from the active site to the receptor surface. This analysis also helped in determining the biological efficacy of the target compounds. The results indicated that the Pr2 compound is more effective than the others. Finally, the computer-assisted retrosynthesis process of two high confidence sequences was used to synthesize drug candidates.Communicated by Ramaswamy H. Sarma.

3D-QSAR methods were used to design eight new compounds and anti-tubulin agents.3D-QSAR models were validated by Golbraikh­Tropsha and Roy methods.The toxicity and pharmacokinetics of the proposed compounds were identified by the Lipinski rule of five, Veber rules, and ADMETox.Pr2 and Pr3 had a reasonable affinity to the receptor protein (ID PDB: 4zvm) based on molecular docking, reactivity indices, and molecular dynamics simulation.Metadynamics was used to study ligand transport in the receptor (ID PDB:3zvm).

Multifunctional role of nanoparticles for the diagnosis and therapeutics of cardiovascular diseases.

The increasing burden of cardiovascular disease (CVD) remains responsible for morbidity and mortality worldwide; their effective diagnostic or treatment methods are of great interest to researchers. The use of NPs and nanocarriers in cardiology has drawn much interest. The present comprehensive review provides deep insights into the use of current and innovative approaches in CVD diagnostics to offer practical ways to utilize nanotechnological interventions and the critical elements in the CVD diagnosis, associated risk factors, and management strategies of patients with chronic CVDs. We proposed a decision tree-based solution by discussing the emerging applications of NPs for the higher number of rules to increase efficiency in treating CVDs. This review-based study explores the screening methods, tests, and toxicity to provide a unique way of creating a multi-parametric feature that includes cutting-edge techniques for identifying cardiovascular problems and their treatments. We discussed the benefits and drawbacks of various NPs in the context of cost, space, time and complexity that have been previously suggested in the literature for the diagnosis of CVDs risk factors. Also, we highlighted the advances in using NPs for targeted and improved drug delivery and discussed the evolution toward the nano-cardiovascular potential for medical science. Finally, we also examined the mixed-based diagnostic approaches crucial for treating cardiovascular disorders, broad applications and the potential future applications of nanotechnology in medical sciences.

In silico designing of multiepitope-based-peptide (MBP) vaccine against MAPK protein express for Alzheimer's disease in Zebrafish.

Understanding the role of the mitogen-activated protein kinases (MAPKs) signalling pathway is essential in advancing treatments for neurodegenerative disorders like Alzheimer's. In this study, we investigate in-silico techniques involving computer-based methods to extract the MAPK1 sequence. Our applied methods enable us to analyze the protein's structure, evaluate its properties, establish its evolutionary relationships, and assess its prevalence in populations. We also predict epitopes, assess their ability to trigger immune responses, and check for allergenicity using advanced computational tools to understand their immunological properties comprehensively. We apply virtual screening, docking, and structure modelling to identify promising drug candidates, analyze their interactions, and enhance drug design processes. We identified a total of 30 cell-targeting molecules against the MAPK1 protein, where we selected top 10 CTL epitopes (PAGGGPNPG, GGGPNPGSG, SAPAGGGPN, AVSAPAGGG, AGGGPNPGS, ATAAVSAPA, TAAVSAPAG, ENIIGINDI, INDIIRTPT, and NDIIRTPTI) for further evaluation to determine their potential efficacy, safety, and suitability for vaccine design based on strong binding potential. The potential to cover a large portion of the world's population with these vaccines is substantial-88.5 % for one type and 99.99 % for another. In exploring the molecular docking analyses, we examined a library of compounds from the ZINC database. Among them, we identified twelve compounds with the lowest binding energy. Critical residues in the MAPK1 protein, such as VAL48, LYS63, CYS175, ASP176, LYS160, ALA61, LEU165, TYR45, SER162, ARG33, PRO365, PHE363, ILE40, ASN163, and GLU42, are pivotal for interactions with these compounds. Our result suggests that these compounds could influence the protein's behaviour. Moreover, our docking analyses revealed that the predicted peptides have a strong affinity for the MAPK1 protein. These peptides form stable complexes, indicating their potential as potent inhibitors. This study contributes to the identification of new drug compounds and the screening of their desired properties. These compounds could potentially help reduce the excessive activity of MAPK1, which is linked to Alzheimer's disease.

In silico and in vitro study of bioactive compounds of Nigella sativa for targeting neuropilins in breast cancer.

Introduction: Breast cancer poses a significant global challenge, prompting researchers to explore novel approaches for potential treatments. Material and Methods: For in vitro study we used thin layer chromatography (TAC) for phytochemical screening, total antioxidant capacity (TLC) assay for antioxidant capacity, and hemolytic activity test for toxicity of Neuropilins (NRPs). We performed bioinformatic analyses to predict protein structures, molecular docking, pharmacophore modeling, and virtual screening to reveal interactions with oncogenes. We conducted 200 ns Molecular Dynamics (MD) simulations and MMGBSA calculations to assess the complex dynamics and stability. Results: We identified phytochemical constituents in Nigella sativa leaves, including tannins, saponins, steroids, and cardiac glycosides, while phlobatannins and terpenoids were absent. The leaves contained 9.4% ± 0.04% alkaloids and 1.9% ± 0.05% saponins. Methanol extract exhibited the highest yield and antioxidant capacity, with Total Flavonoid Content at 127.51 ± 0.76 mg/100 g and Total Phenolic Content at 134.39 ± 0.589 mg GAE/100 g. Hemolysis testing showed varying degrees of hemolysis for different extracts. In-silico analysis indicated stable Neuropilin complexes with key signaling pathways relevant for anti-cancer therapy. Molecular docking scores at different possesses (0, C-50, C -80, C-120,C -150, C -200 ns) revealed strong hydrogen bonding in the complexes and showed -12.9, -11.6, and -11.2 binding Affinities (kcal/mol) to support their stability. Our MD simulations analysis at 200ns confirmed the stability of Neuropilin complexes with the signaling pathways protein PI3K. The calculated binding free energies using MMGBSA provided valuable quantitative information on ligand potency on different time steps. These findings highlight the potential health benefits of N. sativa leaves and their possible role in anti-cancer treatments targeting angiogenesis. Conclusion: Nigella sativa leaves have shown significant medical potential due to their bioactive compounds, which exhibit strong properties in supporting organogenic processes related to cancer. Furthermore, studies have highlighted the promising role of neuropilins in anticancer treatment, demonstrating stable interactions and potential as targeted therapy specifically for breast cancer.

Exploring the multifunctional roles of quantum dots for unlocking the future of biology and medicine.

With recent advancements in nanomedicines and their associated research with biological fields, their translation into clinically-applicable products is still below promises. Quantum dots (QDs) have received immense research attention and investment in the four decades since their discovery. We explored the extensive biomedical applications of QDs, viz. Bio-imaging, drug research, drug delivery, immune assays, biosensors, gene therapy, diagnostics, their toxic effects, and bio-compatibility. We unravelled the possibility of using emerging data-driven methodologies (bigdata, artificial intelligence, machine learning, high-throughput experimentation, computational automation) as excellent sources for time, space, and complexity optimization. We also discussed ongoing clinical trials, related challenges, and the technical aspects that should be considered to improve the clinical fate of QDs and promising future research directions.

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations.

The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic.

Multifunctional analysis and antimicrobial activity of Adhatoda vasica: a traditional medicinal plant.

OBJECTIVES: Antibiotic resistance is rising, prompting innovative strategies for eradicating the epidemic. This study investigated the antibacterial properties of the leaves of a widely used medicinal plant, Adhatoda vasica. METHODS: The plant's polar (water, methanol) and non-polar (hexane) extracts were tested against several different bacterial strains using the disc diffusion technique. RESULTS: In a study, it was found that the water extract had the greatest inhibitory effect on Staphylococcus simulans and Staphylococcus aureus, with minimum inhibitory concentrations of 16.444 and 19.315 g/mL, respectively. Gram-negative strains were more susceptible to plant extracts than Gram-positive strains. The phytochemical analysis indicated the presence of secondary metabolites such as alkaloids, saponins, flavonoids, tannins, and steroids, where absorbance was recorded at 415 nm. The water extract had the highest amount of phenolics, with a total phenolic content of 53.92 0.47 mg and a total flavonoid content of 7.25 0.08 mg. Results suggest that the extract may have potential therapeutic applications for antimicrobial properties. CONCLUSIONS: The study concluded that the extract's phenolic group of secondary metabolites were responsible for its antibacterial activity. The study highlights A. vasica as a promising source for discovering new and effective antibacterial compounds.

Elucidation of novel compounds and epitope-based peptide vaccine design against C30 endopeptidase regions of SARS-CoV-2 using immunoinformatics approaches.

There has been progressive improvement in immunoinformatics approaches for epitope-based peptide design. Computational-based immune-informatics approaches were applied to identify the epitopes of SARS-CoV-2 to develop vaccines. The accessibility of the SARS-CoV-2 protein surface was analyzed, and hexa-peptide sequences (KTPKYK) were observed having a maximum score of 8.254, located between amino acids 97 and 102, whereas the FSVLAC at amino acids 112 to 117 showed the lowest score of 0.114. The surface flexibility of the target protein ranged from 0.864 to 1.099 having amino acid ranges of 159 to 165 and 118 to 124, respectively, harboring the FCYMHHM and YNGSPSG hepta-peptide sequences. The surface flexibility was predicted, and a 0.864 score was observed from amino acids 159 to 165 with the hepta-peptide (FCYMHHM) sequence. Moreover, the highest score of 1.099 was observed between amino acids 118 and 124 against YNGSPSG. B-cell epitopes and cytotoxic T-lymphocyte (CTL) epitopes were also identified against SARS-CoV-2. In molecular docking analyses, -0.54 to -26.21 kcal/mol global energy was observed against the selected CTL epitopes, exhibiting binding solid energies of -3.33 to -26.36 kcal/mol. Based on optimization, eight epitopes (SEDMLNPNY, GSVGFNIDY, LLEDEFTPF, DYDCVSFCY, GTDLEGNFY, QTFSVLACY, TVNVLAWLY, and TANPKTPKY) showed reliable findings. The study calculated the associated HLA alleles with MHC-I and MHC-II and found that MHC-I epitopes had higher population coverage (0.9019% and 0.5639%) than MHC-II epitopes, which ranged from 58.49% to 34.71% in Italy and China, respectively. The CTL epitopes were docked with antigenic sites and analyzed with MHC-I HLA protein. In addition, virtual screening was conducted using the ZINC database library, which contained 3,447 compounds. The 10 top-ranked scrutinized molecules (ZINC222731806, ZINC077293241, ZINC014880001, ZINC003830427, ZINC030731133, ZINC003932831, ZINC003816514, ZINC004245650, ZINC000057255, and ZINC011592639) exhibited the least binding energy (-8.8 to -7.5 kcal/mol). The molecular dynamics (MD) and immune simulation data suggest that these epitopes could be used to design an effective SARS-CoV-2 vaccine in the form of a peptide-based vaccine. Our identified CTL epitopes have the potential to inhibit SARS-CoV-2 replication.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function.

The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.

Amomum subulatum: A treasure trove of anti-cancer compounds targeting TP53 protein using in vitro and in silico techniques.

Cancer is a primary global health concern, and researchers seek innovative approaches to combat the disease. Clinical bioinformatics and high-throughput proteomics technologies provide powerful tools to explore cancer biology. Medicinal plants are considered effective therapeutic agents, and computer-aided drug design (CAAD) is used to identify novel drug candidates from plant extracts. The tumour suppressor protein TP53 is an attractive target for drug development, given its crucial role in cancer pathogenesis. This study used a dried extract of Amomum subulatum seeds to identify phytocompounds targeting TP53 in cancer. We apply qualitative tests to determine its phytochemicals (Alkaloid, Tannin, Saponin, Phlobatinin, and Cardic glycoside), and found that alkaloid composed of 9.4% ± 0.04% and Saponin 1.9% ± 0.05% crude chemical constituent. In the results of DPPH Analysis Amomum subulatum Seeds founded antioxidant activity, and then we verified via observing methanol extract (79.82%), BHT (81.73%), and n-hexane extract (51.31%) found to be positive. For Inhibition of oxidation, we observe BHT is 90.25%, and Methanol (83.42%) has the most significant proportion of linoleic acid oxidation suppression. We used diverse bioinformatics approaches to evaluate the effect of A. subulatum seeds and their natural components on TP53. Compound-1 had the best pharmacophore match value (53.92), with others ranging from 50.75 to 53.92. Our docking result shows the top three natural compounds had the highest binding energies (-11.10 to -10.3 kcal/mol). The highest binding energies (-10.9 to -9.2 kcal/mol) compound bonded to significant sections in the target protein's active domains with TP53. Based on virtual screening, we select top phytocompounds for targets which highly fit based on pharmacophore score and observe these compounds exhibited potent antioxidant activity and inhibited cancer cell inflammation in the TP53 pathway. Molecular Dynamics (MD) simulations indicated that the ligand was bound to the protein with some significant conformational changes in the protein structure. This study provides novel insights into the development of innovative drugs for the treatment of cancer disorders.

Profiling the structural determinants of pyrrolidine derivative as gelatinases (MMP-2 and MMP-9) inhibitors using in silico approaches.

Quantitative structure activity relationship (QSAR) studies on pyrrolidine derivatives have been established using CoMFA, CoMSIA, and Hologram QSAR analysis to estimate the values (pIC50) of gelatinase inhibitors. When the CoMFA cross-validation value, Q², was 0.625, the training set coefficient of determination, R² was 0.981. In CoMSIA, Q² was 0.749 and R² was 0.988. In the HQSAR, Q² was 0.84 and R² was 0.946. Visualization of these models was performed by contour maps showing favorable and unfavorable regions for activity, while visualization of HQSAR model was performed by a colored atomic contribution graph. Based on the results obtained of external validation, the CoMSIA model was statistically more significant and robust and was selected as the best model to predict new, more active inhibitors. To study the modes of interactions of the predicted compounds in the active site of MMP-2 and MMP-9, a simulation of molecular docking was realized. A combined study of MD simulations and calculation of free binding energy, were also carried out to validate the results obtained on the best predicted and most active compound in dataset and the compound NNGH as control compound. The results confirm the molecular docking results and indicate that the predicted ligands were stable in the binding site of MMP-2 and MMP-9.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture.

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer.

Machine learning (ML) can enhance a dermatologist's work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Synthesis and characterization of copper oxide nanoparticles: its influence on corn (Z. mays) and wheat (Triticum aestivum) plants by inoculation of Bacillus subtilis.

Nanotechnology is now playing an emerging role in green synthesis in agriculture as nanoparticles (NPs) are used for various applications in plant growth and development. Copper is a plant micronutrient; the amount of copper oxide nanoparticles (CuONPs) in the soil determines whether it has positive or adverse effects. CuONPs can be used to grow corn and wheat plants by combining Bacillus subtilis. In this research, CuONPs were synthesized by precipitation method using different precursors such as sodium hydroxide (0.1 M) and copper nitrate (Cu(NO3)2) having 0.1 M concentration with a post-annealing method. The NPs were characterized through X-ray diffraction (XRD), scanning electron microscope (SEM), and ultraviolet (UV) visible spectroscopy. Bacillus subtilis is used as a potential growth promoter for microbial inoculation due to its prototrophic nature. The JAR experiment was conducted, and the growth parameter of corn (Z. mays) and wheat (Triticum aestivum) was recorded after 5 days. The lab assay evaluated the germination in JARs with and without microbial inoculation under CuONP stress at different concentrations (25 and 50 mg). The present study aimed to synthesize CuONPs and systematically investigate the particle size effects of copper (II) oxide (CuONPs) (< 50 nm) on Triticum aestivum and Z. mays. In our results, the XRD pattern of CuONPs at 500 °C calcination temperature with monoclinic phase is observed, with XRD peak intensity slightly increasing. The XRD patterns showed that the prepared CuONPs were extremely natural, crystal-like, and nano-shaped. We used Scherrer's formula to calculate the average size of the particle, indicated as 23 nm. The X-ray diffraction spectrum of synthesized materials and SEM analysis show that the particles of CuONPs were spherical in nature. The results revealed that the synthesized CuONPs combined with Bacillus subtilis used in a field study provided an excellent result, where growth parameters of Z. Mays and Triticum aestivum such as root length, shoot length, and plant biomass was improved as compared to the control group.

A comprehensive genomic study, mutation screening, phylogenetic and statistical analysis of SARS-CoV-2 and its variant omicron among different countries.

BACKGROUND: With the rapid development of the genomic sequence data for the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants Delta (B.1.617.2) and Omicron (B.1.1.529), it is vital to successfully identify mutations within the genome. OBJECTIVE: The main objective of the study is to investigate the full-length genome mutation analysis of 157 SARS-CoV-2 and its variant Delta and Omicron isolates. This study also provides possible effects at the structural level to understand the role of mutations and new insights into the evolution of COVID-19 and evaluates the differential level analysis in viral genome sequence among different nations. We have also tried to offer a mutation snapshot for these differences that could help in vaccine formulation. This study utilizes a unique and efficient method of targeting the stable genes for the drug discovery approach. METHODS: Complete genome sequence information of SARS-CoV-2, Delta, and Omicron from online resources were used to predict structure domain identification, data mining, and screening; employing different bioinformatics tools. BioEdit software was used to perform their genomic alignments across countries and a phylogenetic tree as per the confidence of 500 bootstrapping values was constructed. Heterozygosity ratios were determined in-silico. A minimum spanning network (MSN) of selected populations was determined by Bruvo's distance role-based framework. RESULTS: Out of all 157 different strains of SARS-CoV-2 and its variants, and their complete genome sequences from different countries, Corona nucleoca and DUF5515 were observed to be the most conserved domains. All genomes obtained changes in comparison to the Wuhan-Hu-1 strain, mainly in the TRS region (CUAAAC or ACGAAC). We discovered 596 mutations in all genes, with the highest number (321) found in ORF1ab (QHD43415.1), or TRS site mutations found only in ORF7a (1) and ORF10 (2). The Omicron variant has 30 mutations in the Spike protein and has a higher alpha-helix shape (23.46%) than the Delta version (22.03%). T478 was also discovered to be a prevalent polymorphism in Delta and Omicron variations, as well as genomic gaps ranging from 45 to 65aa. All 157 sequences contained variations and conformed to Nei's Genetic distance. We discovered heterozygosity (Hs) 0.01, mean anticipated Hs 0.32, the genetic diversity index (GDI) 0.01943989, and GD within population 0.01266951. The Hedrick value was 0.52324978, the GD coefficient was 0.52324978, the average Hs was 0.01371452, and the GD coefficient was 0.52324978. Among other countries, Brazil has the highest standard error (SE) rate (1.398), whereas Japan has the highest ratio of Nei's gene diversity (0.01). CONCLUSIONS: The study's findings will assist in comprehending the shape and kind of complete genome, their streaming genomic sequences, and mutations in various additions of SARS-CoV-2, as well as its different variant strains like Omicron. These results will provide a scientific basis to design the vaccines and understand the genomic study of these viruses.

Structural, genomic information and computational analysis of emerging coronavirus (SARS-CoV-2).

Background: The emerging viral pandemic worldwide is associated with a novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). This virus is said to emerge from its epidemic center in Wuhan, China, in 2019. Coronaviruses (CoVs) are single-stranded, giant, enveloped RNA viruses that come under the family of coronaviridae and order Nidovirales which are the crucial pathogens for humans and other vertebrates. Main body: Coronaviruses are divided into several subfamilies and genera based on the genomic structure and phylogenetic relationship. The name corona is raised due to the presence of spike protein on the envelope of the virus. The structural and genomic study revealed that the total genome size of SARS-CoV-2 is from 29.8 kb to 29.9 kb. The spike protein (S) is a glycoprotein that attaches to the receptor of host cells for entry into the host cell, followed by the attachment of virus RNA to the host ribosome for translation. The phylogenetic analysis of SARS-CoV-2 revealed the similarity (75-88%) with bat SARS-like coronavirus. Conclusion: The sign and symptoms of novel severe acute respiratory syndrome coronavirus 2 are also discussed in this paper. The worldwide outbreak and prevention from severe acute respiratory syndrome coronavirus 2 are overviewed in the present article. The latest variant of coronavirus and the status of vaccines are also overviewed in the present article.

Genome wide identification, phylogeny, and synteny analysis of sox gene family in common carp (Cyprinus carpio).

Common carp (Cyprinus carpio) is a commercial fish species valuable for nutritious components and plays a vital role in human healthy nutrition. The SOX (SRY-related genes systematically characterized by a high-mobility group HMG-box) encoded important gene regulatory proteins, a family of transcription factors found in a broad range of animal taxa and extensively known for its contribution in multiple developmental processes including contribution in sex determination across phyla. In our current study, we initially accomplished a genome-wide analysis to report the SOX gene family in common carp fish based on available genomic sequences of zebrafish retrieved from gene repository databases, we focused on the global identification of the Sox gene family in Common carp among wide range of vertebrates and teleosts based on bioinformatics tools and techniques and explore the evolutionary relationships. In our results, a total of 27 SOX (high-mobility group HMG-box) domain genes were identified in the C. carp genome. The full length sequences of SOX genes ranging from 3496 (SOX6) to 924bp (SOX17b) which coded with putative proteins series from 307 to 509 amino acids and all gene having exon number expect SOX9 and SOX13. All the SOX proteins contained at least one conserved DNA-binding HMG-box domain and two (SOX7 and SOX18) were found C terminal. The Gene ontology revealed SOX proteins maximum involvement is in metabolic process 49.796 %, average in biological regulation 45.188 %, biosynthetic process (19.992 %), regulation of cellular process 39.68, 45.508 % organic substance metabolic process, multicellular organismal process 23.23 %,developmental process 21.74 %, system development 16.59 %, gene expression 16.05 % and 14.337 % of RNA metabolic process. Chromosomal location and syntanic analysis show all SOX gene are located on different chromosomes and apparently does not fallow the unique pattern. The maximum linkage of chromosome is (2) on Unplaced Scaffold region. Finally, our results provide important genomic suggestion for upcoming studies of biochemical, physiological, and phylogenetic understanding on SOX genes among teleost.

Colon Cancer Metastatic to the Thyroid Gland in the Setting of Pathologically Diagnosed Papillary Thyroid Cancer: A Review and Report of a Case.

Colon cancer metastases to the thyroid gland are a particularly rare occurrence. Despite the relative amenability of the gland to clinical, radiologic, and pathologic assessment, preoperative distinction between primary and secondary thyroid neoplastic processes remains difficult. Here we describe a case of a patient with a known history of stage IV colon cancer with multiple pulmonary metastases, presenting with a thyroid lesion initially diagnosed as papillary thyroid cancer on fine-needle aspiration biopsy but found to be metastatic colonic adenocarcinoma on post-thyroidectomy pathologic evaluation utilizing immunohistochemical techniques. A review of the literature is also included.

Genome-Wide identification of doublesex and Mab-3-Related transcription factor (DMRT) genes in nile tilapia (oreochromis niloticus).

Doublesex and Mab-3-related transcription factor (DMRT) gene family is extensively known for its contribution in sex determination and differentiation across phyla. Here we report the identification of five DM (doublesex and mab-3) domain genes in the Nile tilapia which includes DMRT1, DMRTa2, DMRT2a, DMRT2b and DMRT3a. The full-length sequence of DMRT genes ranges from 3526 (DMRTA2) to 1471bp (DMRT1) which encode putative proteins series from 469 to 372 amino acids. All the DMRT proteins contained at least one conserved DNA-binding DM domain. Sub-cellular localization and gene ontology revealed DMRT1 protein is maximum localized in nuclear region and gene ontology analysis showed the molecular function of 48.2%, biological process 43.6% and cellular component 25%. Chromosomal location and synteny analysis displayed that DMRT genes mostly cluster linkage group 12. Altogether, our findings provide vital genomic information for future studies of biochemical, physiological, and phylogenetic studies on DMRT genes in teleost.

Catalytic intramolecular decarbonylative coupling of 3-aminocyclobutenones and alkenes: a unique approach to [3.1.0] bicycles.

Here we describe a rhodium-catalyzed intramolecular decarbonylative coupling between 3-aminocyclobutenones and alkenes for synthesis of substituted [3.1.0] bicycles. This transformation represents a formal cyclopropanation reaction, in which the cyclobutenones serve as a one-carbon-unit synthon.