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The World Health Organization (WHO) has declared the monkeypox outbreak a public health emergency, as there is no specific therapeutics for monkeypox virus (MPXV) disease. This study focused on docking various commercial drugs and plant-derived compounds against the E8 envelope protein crucial for MPXV attachment and pathogenesis. The target protein structure was modeled based on the vaccinia virus D8L protein. Notably, maraviroc and punicalagin emerged as potential ligands, with punicalagin exhibiting higher binding affinity (− 9.1 kcal/mol) than maraviroc (− 7.8 kcal/mol). Validation through 100 ns molecular dynamics (MD) simulations demonstrated increased stability of the E8-punicalagin complex, with lower RMSD, RMSF, and Rg compared to maraviroc. Enhanced hydrogen bonding, lower solvent accessibility, and compact motions also attributed to higher binding affinity and stability of the complex. MM-PBSA calculations revealed van der Waals, electrostatic, and non-polar solvation as principal stabilizing energies. The binding energy decomposition per residue favored stable interactions between punicalagin and the protein’s active site residues (Arg20, Phe56, Glu228, Tyr232) compared to maraviroc. Overall study suggests that punicalagin can act as a potent inhibitor against MPXV. Further research and experimental investigations are warranted to validate its efficacy and safety.

Triple-negative breast cancer (TNBC), is diagnosed as the most lethal molecular subtype of breast cancer (BC) preceded by an extremely poor prognosis. For enabling effective TNBC therapy, the identification of novel druggable biomarkers is an earnest need. Multigene paneling and genomewide association studies identify multiple genes with high-to-moderate penetrance in TNBC. Modern computer-aided drug designing techniques, thus aim to design more cost-effective natural small molecule inhibitors for TNBC prevention and diagnosis. Here Amygdalin, a natural glycosidic inhibitor is docked and simulated against three such high-to-moderate penetrance genes identified in TNBC, BARD1, RAD51, and PALB2. The preliminary result of the analysis, reports a highest, intermediate, and least binding energy score of − 6.69 kcal/mol, − 5.09 kcal/mol, and − 4.89 kcal/mol in BARD1, RAD51, and PALB2, respectively. The best-docked protein–ligand complex (BARD1-Amygdalin) was then simulated and compared with an approved drug for TNBC treatment, Olaparib. A comparable binding energy score of − 8.53 kcal/mol was obtained by docking olaparib with BARD1. A 100 ns MD simulation revealed, Amygdalin forms more H-bonds, providing more stable and compact protein–ligand complex with BARD1 than compared to Olaparib. The result was also supported by calculation of solvent accessible surface area and analysis of radius of gyration. Thus, our findings suggest that role of Amygdalin can further be studied in details for TNBC therapeutics, which was found to target the BRCT domain of the BARD1 receptor in stable manner. Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary. Name and affiliations are correctly identified.

Triple-negative breast cancer (TNBC) is the most invasive molecular subtype of breast cancer (BC), accounting for about nearly 15% of all BC cases reported annually. The absence of the three major BC hormone receptors, Estrogen (ER), Progesterone (PR), and Human Epidermal Growth Factor 2 (HER2) receptor, accounts for the characteristic “Triple negative” phraseology. The absence of these marked receptors makes this cancer insensitive to classical endocrine therapeutic approaches. Hence, the available treatment options remain solemnly limited to only conventional realms of chemotherapy and radiation therapy. Moreover, these therapeutic regimes are often accompanied by numerous treatment side-effects that account for early distant metastasis, relapse, and shorter overall survival in TNBC patients. The rigorous ongoing research in the field of clinical oncology has identified certain gene-based selective tumor-targeting susceptibilities, which are known to account for the molecular fallacies and mutation-based genetic alterations that develop the progression of TNBC. One such promising approach is synthetic lethality, which identifies novel drug targets of cancer, from undruggable oncogenes or tumor-suppressor genes, which cannot be otherwise clasped by the conventional approaches of mutational analysis. Herein, a holistic scientific review is presented, to undermine the mechanisms of synthetic lethal (SL) interactions in TNBC, the epigenetic crosstalks encountered, the role of Poly (ADP-ribose) polymerase inhibitors (PARPi) in inducing SL interactions, and the limitations faced by the lethal interactors. Thus, the future predicament of synthetic lethal interactions in the advancement of modern translational TNBC research is assessed with specific emphasis on patient-specific personalized medicine.

In plants, pathogen resistance is brought about by the binding of certain transcription factor (TF) proteins to the cis -elements of certain target genes. These cis -elements are present upstream in the motif of the promoters of each gene. This ensures the binding of a specific TF to a specific promoter, therefore regulating the expression of that gene. Therefore, the study of each promoter sequence of all the rice genes would help identify the target genes of a specific TF. Rice 1 kb upstream promoter sequences of 55,986 annotated genes were analyzed using the Perl program algorithm to detect WRKY13 binding motifs (bm). The resulting genes were grouped using Gene Ontology and gene set enrichment analysis. A gene with more than 4 TF bm in their promoter was selected. Ten genes reported to have a role in rice disease resistance were selected for further analysis. Cis -acting regulatory element analysis was carried out to find the cis -elements and confirm the presence of the corresponding motifs in the promoter sequences of these genes. The 3D structure of WRKY13 TF and the corresponding ten genes were built, and the interacting residues were determined. The binding capacity of WRKY13 to the promoter of these selected genes was analyzed using docking studies. WRKY 13 was considered for docking analysis based on the prior reports of autoregulation. Molecular dynamic simulations provided more details regarding the interactions. Expression data revealed the expression of the genes that helped provide the mechanism of interaction. Further co-expression network helped to characterize the interaction of these selected disease resistance-related genes with the WRKY13 TF protein. This study suggests downstream target genes that are regulated by the WRKY13 TF. The molecular mechanism involving the gene network regulated by WRKY13 TF in disease resistance against rice fungal pathogens is explored.

In plants, pathogen resistance is brought about due to the binding of certain transcription factors (TF) proteins to the cis-elements of certain target genes. These cis-elements are present up-stream in the motif of the promoters of each gene. This ensures the binding of a specific transcription factor to a specific promoter, therefore regulating the expression of that gene. Therefore, the study of each promoter sequence of all the rice genes would help identify the target genes of a specific transcription factor. Rice 1kb upstream promoter sequences of 55,986 annotated genes were analyzed using the Perl program algorithm to detect WRKY13 binding motifs (bm). The resulting genes were grouped using gene ontology and gene set enrichment analysis. Gene with more than 4 TFbm in their promoter was selected. Nine genes reported to have a role in rice disease resistance were selected for further analysis. Cis-acting regulatory element analysis was carried out to find the cis-elements and to confirm the presence of the corresponding motifs in the promoter sequences of these genes. The 3D structure of WRKY13 TF and the corresponding nine genes were built and the interacting residues were determined. The binding capacity of WRKY13 to the promoter of these selected genes was analyzed using docking studies. WRKY13 was also considered for docking analysis based on the prior reports of autoregulation. Molecular dynamic simulations provided more details regarding the interactions. Expression data revealed the expression of the genes that helped to provide the mechanism of interaction. Further co-expression network drew light on the interaction of these selected diseases resistance-related genes with WRKY13 TF protein. This study suggests the target downstream genes that are regulated by WRKY13 TF. The molecular mechanism involving the gene network regulated by WRKY13 TF in disease resistance against rice fungal pathogens is explored.Competing Interest StatementThe authors have declared no competing interest.

IntroductionNon-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease, with type 2 diabetes mellitus (T2DM) as a major predictor. Insulin resistance and chronic inflammation are key pathways in the pathogenesis of T2DM leading to NAFLD and vice versa, with the synergistic effect of NAFLD and T2DM increasing morbidity and mortality risks. This meta-analysis aims to quantify the prevalence of NAFLD and the prevalence of clinically significant and advanced fibrosis in people with T2DM.MethodsMEDLINE and Embase databases were searched from inception until 13 February 2023. The primary outcomes were the prevalence of NAFLD, non-alcoholic steatohepatitis (NASH) and fibrosis in people with T2DM. A generalised linear mixed model with Clopper-Pearson intervals was used for the analysis of proportions with sensitivity analysis conducted to explore heterogeneity between studies.Results156 studies met the inclusion criteria, and a pooled analysis of 1 832 125 patients determined that the prevalence rates of NAFLD and NASH in T2DM were 65.04% (95% CI 61.79% to 68.15%, I2=99.90%) and 31.55% (95% CI 17.12% to 50.70%, I2=97.70%), respectively. 35.54% (95% CI 19.56% to 55.56%, I2=100.00%) of individuals with T2DM with NAFLD had clinically significant fibrosis (F2–F4), while 14.95% (95% CI 11.03% to 19.95%, I2=99.00%) had advanced fibrosis (F3–F4).ConclusionThis study determined a high prevalence of NAFLD, NASH and fibrosis in people with T2DM. Increased efforts are required to prevent T2DM to combat the rising burden of NAFLD.PROSPERO registration numberCRD42022360251.

Background/Aims: Multi-society experts proposed the adoption of new terminology, metabolic dysfunctionassociated steatotic liver disease (MASLD) and steatotic liver disease (SLD). We studied the current prevalence of SLD and its subcategories in the US.Methods: Using the recent National Health and Nutrition Examination Survey from 2017 to 2023, we analyzed data from 12,199 participants (≥18 years) who completed transient elastography. SLD and its subcategories, including MASLD, metabolic and alcohol-related liver disease (MetALD), and alcohol-related liver disease (ALD), were categorized according to consensus nomenclature.Results: The age-adjusted prevalence of SLD (cut-off: 285 dB/m) was 35.0% (95% confidence interval [CI] 33.4–36.7). Within this category, the age-adjusted prevalence for MASLD was 31.9% (95% CI 30.4–33.4), MetALD 2.2% (95% CI 1.8–2.6), and ALD 0.8% (95% CI 0.6–1.1). The prevalence of SLD and MASLD showed a statistically insignificant decrease during COVID-19, while ALD increased without significance. In contrast, the prevalence of advanced fibrosis in SLD was significantly higher during the COVID-19 era, at 9.8% for 285 dB/m and 7.8% for 263 dB/m, compared to 7.4% (P=0.039) and 6% (P=0.041) in the pre-COVID-19 era. The proportion of advanced fibrosis and cirrhosis in individuals with ALD was two-fold higher than MASLD and MetALD, largely due to increases during the COVID-19 era.Conclusions: While the prevalence of SLD and its subcategories remained stable, there was a significant increase in advanced fibrosis among SLD individuals during the COVID-19 era, with ALD having a proportion of advanced fibrosis and cirrhosis that was twice as high as MASLD and MetALD.

Importance Emerging data suggest that the incidence of early-onset cancers, defined as cancers diagnosed in people younger than 50 years, is increasing, but updated data are limited. Objective To characterize the patterns in the incidence of early-onset cancers in the US from 2010 to 2019 and provide granular data on the cancers with the fastest-growing incidence rates. Design, Setting, and Participants This population-based cohort study analyzed data from 17 National Cancer Institute Surveillance, Epidemiology, and End Results registries from January 1, 2010, to December 31, 2019. Age-standardized incidence rates per 100 000 people were extracted for early-onset cancers, with rates age adjusted to the US standard population. A total of 562 145 patients with early-onset cancer between 2010 and 2019 were identified and included. Data were analyzed from October 16, 2022, to May 23, 2023. Main Outcomes and Measures Primary outcomes were incidence rates and descriptive epidemiological data for people younger than 50 years with cancer. The annual percentage change (APC) of the age-standardized incidence rate was estimated using the Joinpoint regression program. Results Among 562 145 patients (324 138 [57.7%] aged 40-49 years; 351 120 [62.5%] female) with early-onset cancer, 4565 (0.8%) were American Indian or Alaska Native, 54 876 (9.8%) were Asian or Pacific Islander, 61 048 (10.9%) were Black, 118 099 (21.0%) were Hispanic, 314 610 (56.0%) were White, and 8947 (1.6%) were of unknown race and/or ethnicity. From 2010 to 2019, the age-standardized incidence rate of early-onset cancers increased overall (APC, 0.28%; 95% CI, 0.09%-0.47%;P = .01) and in female individuals (APC, 0.67%; 95% CI, 0.39%-0.94%;P = .001) but decreased in male individuals (APC, −0.37%; 95% CI, −0.51% to −0.22%;P < .001). In contrast, the age-standardized incidence rate of cancers in individuals aged 50 years and older decreased over the study period (APC, −0.87%; 95% CI, −1.06% to −0.67%;P < .001). In 2019, the highest number of incident cases of early-onset cancer were in the breast (n = 12 649). From 2010 to 2019, gastrointestinal cancers had the fastest-growing incidence rates among all early-onset cancer groups (APC, 2.16%; 95% CI, 1.66%-2.67%;P < .001). Among gastrointestinal cancers, those with the fastest-growing incidence rates were in the appendix (APC, 15.61%; 95% CI, 9.21%-22.38%;P < .001), intrahepatic bile duct (APC, 8.12%; 95% CI, 4.94%-11.39%;P < .001), and pancreas (APC, 2.53%; 95% CI, 1.69%-3.38%;P < .001). Conclusions and Relevance In this cohort study, the incidence rates of early-onset cancer increased from 2010 to 2019. Although breast cancer had the highest number of incident cases, gastrointestinal cancers had the fastest-growing incidence rates among all early-onset cancers. These data may be useful for the development of surveillance strategies and funding priorities.

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