In-silico approach to characterize the structure and function of a hypothetical protein of Monkeypox virus exploring Chordopox-A20R domain-containing protein activity.

Background: Monkeypox has emerged as a noteworthy worldwide issue due to its daily escalating case count. This illness presents diverse symptoms, including skin manifestations, which have the potential to spread through contact. The transmission of this infectious agent is intricate and readily transfers between individuals.Methods: The hypothetical protein MPXV-SI-2022V502225_00135 strain of monkeypox underwent structural and functional analysis using NCBI-CD Search, Pfam, and InterProScan. Quality assessment utilized PROCHECK, QMEAN, Verify3D, and ERRAT, followed by protein-ligand docking, visualization, and a 100-nanosecond simulation on Schrodinger Maestro.Results: Different physicochemical properties were estimated, indicating a stable molecular weight (49147.14) and theoretical pI (5.62) with functional annotation tools predicting the target protein to contain the domain of Chordopox_A20R domain. In secondary structure analysis, the helix coil was found to be predominant. The three-dimensional (3D) structure of the protein was obtained using a template protein (PDB ID: 6zyc.1), which became more stable after YASARA energy minimization and was validated by quality assessment tools like PROCHECK, QMEAN, Verify3D, and ERRAT. Protein-ligand docking was conducted using PyRx 9.0 software to examine the binding and interactions between a ligand and a hypothetical protein, focusing on various amino acids. The model structure, active site, and binding site were visualized using the CASTp server, FTsite, and PyMOL. A 100 nanosecond simulation was performed with ligand CID_16124688 to evaluate the efficiency of this protein.Conclusion: The analysis revealed significant binding interactions and enhanced stability, aiding in drug or vaccine design for effective antiviral treatment and patient management.

Meta-analysis investigating the impact of the LEPR rs1137101 (A>G) polymorphism on obesity risk in Asian and Caucasian ethnicities.

Obesity is a chronic condition which is identified by the buildup of excess body fat caused by a combination of various factors, including genetic predisposition and lifestyle choices. rs1137101 (A > G) polymorphism in the CHR1 domain of LEPR protein linked to different diseases including obesity. Nevertheless, the connection between this polymorphism and the likelihood of developing obesity has not been determined definitively. Therefore, a meta-analysis was conducted to assess the relationship between rs1137101 and the risk of obesity. The meta-analysis included all studies meeting pre-defined criteria, found through searching databases up until February 2023. A combined odds ratio with a 95% confidence interval was estimated as overall and in continent subgroups for homozygous, heterozygous, recessive, dominant and allelic models using the fixed or the random-effects model. The meta-analysis identified 39 eligible studies with cases and controls (6099 cases/6711 controls) in 38 articles under different ethnic backgrounds. The results indicated a significant relationship between rs1137101 and the likelihood of developing obesity in each of the genetic models [the homozygous model (GG vs. AA: 95% Confidence Interval = 1.12-1.73, Odds Ratio = 1.39, P value = 0.003); the heterozygous model (AG vs. AA: 95% Confidence Interval = 1.07-1.42, Odds Ratio = 1.23, P value = 0.005); the dominant model (AG/GG vs AA: 95% Confidence Interval = 1.10-1.49, Odds Ratio = 1.28, P value = 0.001); the recessive model (GG vs AA/AG: 95% Confidence Interval = 1.02-1.45, Odds Ratio = 1.21, P value = 0.03); and the allelic model (G vs A; 95% Confidence Interval = 1.07-1.33, Odds Ratio = 1.19, P value = 0.002)] tested. Additionally, with an FDR <0.05, all genotypic models demonstrated statistical significance. The association remained significant among subgroups of Asian and Caucasian populations, although analysis in some genetic models did not show a significant association. Begg's and Egger's tests did not show publication biases. In sensitivity analysis, one particular study was found to have an impact on the Recessive model's significance, but other models remained unaffected. The current meta-analysis found significant indications supporting the association between rs1137101 and obesity. To avail a deeper understanding of this association, future research should include large-scale studies conducted in diverse ethnic populations.

Evaluation of Inbred Maize (Zea mays L.) for Tolerance to Low Phosphorus at the Seedling Stage.

In underdeveloped nations where low-input agriculture is practiced, low phosphorus (LP) in the soil reduces the production of maize. In the present study, a total of 550 inbred maize lines were assessed for seedling traits under LP (2.5 × 10-6 mol L-1 of KH2PO4) and NP (2.5 × 10-4 mol L-1 of KH2PO4) hydroponic conditions. The purpose of this study was to quantify the amount of variation present in the measured traits, estimate the genetic involvement of these characteristics, examine the phenotypic correlation coefficients between traits, and to integrate this information to prepare a multi-trait selection index for LP tolerance in maize. A great deal of variability in the maize genotype panel was confirmed by descriptive statistics and analysis of variance (ANOVA). Estimated broad-sense heritability (h2) ranged from 0.7 to 0.91, indicating intermediate to high heritability values for the measured traits. A substantial connection between MSL and other root traits suggested that the direct selection of MSL (maximum shoot length) could be beneficial for the enhancement of other traits. The principal component analysis (PCA) of the first two main component axes explained approximately 81.27% of the variation between lines for the eight maize seedling variables. TDM (total dry matter), SDW (shoot dry weight), RDW (root dry weight), SFW (shoot fresh weight), RFW (root fresh weight), MRL (maximum root length), and MSL measurements accounted for the majority of the first principal component (59.35%). The multi-trait indices were calculated based on PCA using all the measured traits, and 30 genotypes were selected. These selected lines might be considered as the potential source for the improvement of LP tolerance in maize.

Pharmacoinformatics and molecular dynamics simulation approach to identify anti-diarrheal potentials of Centella asiatica (L.) Urb. against Vibrio cholerae.

Vibrio cholerae, the etiological agent of cholera, causes dehydration and severe diarrhea with the production of cholera toxin. Due to the acquired antibiotic resistance, V. cholerae has drawn attention to the establishment of novel medications to counteract the virulence and viability of the pathogen. Centella asiatica is a medicinal herb native to Bangladesh that has a wide range of medicinal and ethnobotanical applications including anti-bacterial properties. In the present investigation, a total of 25 bioactive phytochemicals of C. asiatica have been screened virtually through molecular docking, ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analyses, and molecular dynamics simulation. Our results revealed four lead compounds as Viridiflorol (-8.7 Kcal/mol), Luteolin (-8.1 Kcal/mol), Quercetin (-8.0 Kcal/mol) and, Geranyl acetate (-7.1 Kcal/mol) against V. cholerae Toxin co-regulated pilus virulence regulatory protein (ToxT). All the lead compounds have been found to possess favorable pharmacokinetic, pharmacodynamics, and molecular dynamics properties. Toxicity analysis revealed satisfactory results with no major side effects. Molecular dynamics simulation was performed for 100 ns that revealed noteworthy conformational stability and structural compactness for all the lead compounds, especially for Quercetin. Target class prediction unveiled enzymes in most of the cases and some experimental and investigational drugs were found as structurally similar analogs of the lead compounds. These findings could aid in the development of novel therapeutics targeting Cholera disease and we strongly recommend in vitro trials of our experimental findings.Communicated by Ramaswamy H. Sarma.

Virtual screening reveals liquiritigenin as a broad-spectrum inhibitor of SARS-CoV-2 variants of concern: an in silico study.

The SARS-CoV-2 has severely impacted the lives of people worldwide. Global concern is on the rise due to a large number of unexpected mutations in the viral genome, resulting in new variants. Nature-based bioactive phytochemicals hold great promise as inhibitors against pathogenic viruses. The current study was aimed at evaluating some bioactive antiviral phytochemicals against SARS-CoV-2 variants of concern. A total of 46 phytochemicals were screened against the pathogenic spike protein of Alpha, Beta, Delta, Gamma, and Omicron variants. In addition to molecular docking, screening for favorable pharmacokinetic and pharmacodynamic properties such as absorption, distribution, metabolism, excretion, and toxicity was undertaken. For each of the aforementioned five SARS-CoV-2 variants of concern, a 100 ns molecular dynamics simulation was run to assess the stability of the complexes between their respective spike protein receptor-binding domain and the best-selected compound. From our current investigation, the natural compound liquiritigenin turned out to be the most promising potential lead compound against almost all the variants. These findings could pave the way for the development of effective medications against SARS-CoV-2 variants. However, in vivo trials in future studies are necessary for further validation of our results.Communicated by Ramaswamy H. Sarma.

Systems biology models to identify the influence of SARS-CoV-2 infections to the progression of human autoimmune diseases.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been circulating since 2019, and its global dominance is rising. Evidences suggest the respiratory illness SARS-CoV-2 has a sensitive affect on causing organ damage and other complications to the patients with autoimmune diseases (AD), posing a significant risk factor. The genetic interrelationships and molecular appearances between SARS-CoV-2 and AD are yet unknown. We carried out the transcriptomic analytical framework to delve into the SARS-CoV-2 impacts on AD progression. We analyzed both gene expression microarray and RNA-Seq datasets from SARS-CoV-2 and AD affected tissues. With neighborhood-based benchmarks and multilevel network topology, we obtained dysfunctional signaling and ontological pathways, gene disease (diseasesome) association network and protein-protein interaction network (PPIN), uncovered essential shared infection recurrence connectivities with biological insights underlying between SARS-CoV-2 and AD. We found a total of 77, 21, 9, 54 common DEGs for SARS-CoV-2 and inflammatory bowel disorder (IBD), SARS-CoV-2 and rheumatoid arthritis (RA), SARS-CoV-2 and systemic lupus erythematosus (SLE) and SARS-CoV-2 and type 1 diabetes (T1D). The enclosure of these common DEGs with bimolecular networks revealed 10 hub proteins (FYN, VEGFA, CTNNB1, KDR, STAT1, B2M, CD3G, ITGAV, TGFB3). Drugs such as amlodipine besylate, vorinostat, methylprednisolone, and disulfiram have been identified as a common ground between SARS-CoV-2 and AD from drug repurposing investigation which will stimulate the optimal selection of medications in the battle against this ongoing pandemic triggered by COVID-19.

Early Stroke Prediction Methods for Prevention of Strokes.

The emergence of the latest technologies gives rise to the usage of noninvasive techniques for assisting health-care systems. Amongst the four major cardiovascular diseases, stroke is one of the most dangerous and life-threatening disease, but the life of a patient can be saved if the stroke is detected during early stage. The literature reveals that the patients always experience ministrokes which are also known as transient ischemic attacks (TIA) before experiencing the actual attack of the stroke. Most of the literature work is based on the MRI and CT scan images for classifying the cardiovascular diseases including a stroke which is an expensive approach for diagnosis of early strokes. In India where cases of strokes are rising, there is a need to explore noninvasive cheap methods for the diagnosis of early strokes. Hence, this problem has motivated us to conduct the study presented in this paper. A noninvasive approach for the early diagnosis of the strokes is proposed. The cascaded prediction algorithms are time-consuming in producing the results and cannot work on the raw data and without making use of the properties of EEG. Therefore, the objective of this paper is to devise mechanisms to forecast strokes on the basis of processed EEG data. This paper is proposing time series-based approaches such as LSTM, biLSTM, GRU, and FFNN that can handle time series-based predictions to make useful decisions. The experimental research outcome reveals that all the algorithms taken up for the research study perform well on the prediction problem of early stroke detection, but GRU performs the best with 95.6% accuracy, whereas biLSTM gives 91% accuracy and LSTM gives 87% accuracy and FFNN gives 83% accuracy. The experimental outcome is able to measure the brain waves to predict the signs of strokes. The findings can certainly assist the physicians to detect the stroke at early stages to save the lives of the patients.

Psychological Reactions to Different Types of Gender-Based Violence in Women Survivors of Violence in the Context of a Developing Country.

Literature on the psychological effects on women survivors of violence (WSV) suggests there may be a relationship between the specific type of gender-based violence and patterns in the development of mental health consequences. Understanding these relationships would support early targeted (or early specialist) intervention. Since violence against women in families is a common social health problem in developing countries, the study attempted to explore the abuse specific reaction patterns within such a context. A total of 600 WSV (mean age = 26.86, SD = 7.47) were recruited from different social service organizations working for WSV in Bangladesh. To identify the type of gender-based violence (i.e., physical, sexual, emotional, psychological, and economic violence) experienced and psychiatric sequelae (i.e., post-traumatic stress disorder (PTSD), anxiety, depression, suicidal ideation, and substance abuse) in WSV, multiple reliable and valid measures were used. The results of the hierarchical multiple regression analysis showed that amongst the various different types of violence, only psychological, sexual, and economic violence have a significant independent predictive ability on PTSD, anxiety, and depression in the Bangladeshi WSV. Violence related factors such as witnessing violence in childhood among parents and history of childhood abuse had the unique predictive ability on suicidal ideation and substance abuse, respectively. Our result suggested that accepting physical and emotional abuse at any stage of life is very common for Bangladeshi women. This study suggested that non-physical forms of violence have the most significant independent predictive ability in the development of psychiatric symptoms. It is suggested that to develop appropriate support services for WSV within this socio-cultural context, further research is required which focuses on the psychological impact of non-physical forms of violence.

Adiponectin gene polymorphisms associated with diabetes mellitus: A descriptive review.

Diabetes is currently a growing concern of the age. Prevention and treatment of diabetes is a global health priority. Adiponectin is an adipocyte derived protein hormone that enhances insulin sensitivity and ameliorates diabetes by enhancing fatty acid oxidation and glucose uptake in skeletal muscle and reducing glucose production in the liver. Low serum adiponectin concentrations are associated with diabetes, central obesity, insulin resistance and metabolic syndrome. Adiponectin gene is located on chromosome 3q27, where a locus of susceptibility to diabetes was mapped. Several cross-sectional studies showed that single nucleotide polymorphisms (SNPs) in adiponectin gene (ADIPOQ) were associated with diabetes. SNPs in ADIPOQ help in assessing the association of common variants with levels of adiponectin and the risk of diabetes. Two common SNPs, rs2241766 and rs1501299, have been linked significantly to type 1 diabetes mellitus which endow the world with a block of haplotypes. Experimental evidences also suggest that rs1501299, rs2241766, rs266729, rs17366743, rs17300539, rs182052, rs822396, rs17846866, rs3774261 and rs822393 are significantly associated with type 2 diabetes mellitus which is the predominant form of the disease. In addition, rs2241766 and rs266729 are extensively associated with gestational diabetes, a condition that develops in women during pregnancy. Therefore not a particular single mutation but a number of SNPs in adiponectin gene could be a risk factor for developing diabetes among the individuals worldwide. This study firmly suggests that adiponectin plays a crucial role in the pathogenesis of type 1, type 2 and gestational diabetes mellitus.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.