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Background Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are extensively used in the management of type 2 diabetes mellitus (T2DM) and obesity. While these medications offer glycemic control and cardiovascular benefits, the risks have increased because of their potential impact on cancer risk, particularly colorectal cancer (CRC). This meta-analysis aimed to evaluate the association between GLP-1 RAs and CRC risk in patients receiving GLP-1 RAs. Methods This study was conducted the PRISMA guidelines. Electronic databases (PubMed, Embase, Cochrane Library Web of Science, and ClinicalTrials.gov) were searched from inception to December 2024. The inclusion criteria encompassed Studies analyzing the effects of GLP-1 RA on CRC risk in patients with T2DM. The Newcastle-Ottawa Scale was used for the quality assessment of the included cohort studies. Random-effects models were employed for the pooled analysis, and heterogeneity was evaluated using the I2 statistic. Results Seven retrospective cohort studies involving 5,066,681 patients were included. The pooled analysis revealed a significantly increased risk of CRC among patients receiving GLP-1 RAs (RR, 2.31; 95% CI, 1.82–2.93; I2 = 36%; p  < 0.0001). However, the incidence of CRC was not significantly associated with GLP-1 RA use compared with other drugs (OR, 1.73; 95% CI: 0.21–14.18, p  = 0.61; I2 = 100%). Quality assessment indicated a low-to-moderate risk of bias across the included studies. Conclusion Overall, this study suggests a significantly increased risk of colorectal cancer associated with GLP-1 RA use in patients receiving GLP-1 RAs. However, the incidence of CRC is not considerably high. These findings highlight the need for further long-term, large-scale clinical trials to elucidate the relationship between GLP-1 RAs and cancer risk. Clinicians should consider these results when prescribing GLP-1 RAs, particularly in patients with CRC risk factors. Graphical Abstract

Soil salinity is a major factor affecting rice growth and productivity worldwide especially at seedling stage. Many genes for salt tolerance have been identified and applied to rice breeding, but the actual mechanism of salt tolerance remains unclear. In this study, seedlings of 664 cultivated rice varieties from the 3000 Rice Genome Project (3K-RG) were cultivated by hydroponic culture with 0.9% salt solution for trait identification. A genome-wide association study (GWAS) of salt tolerance was performed using different models of analysis. Twenty-one QTLs were identified and two candidate genes named OsSTL1 ( Oryza sativa salt tolerance level 1) and OsSTL2 ( Oryza sativa salt tolerance level 2) were confirmed using sequence analysis. Haplotype and sequence analysis revealed that gene OsSTL1 was a homolog of salt tolerance gene SRP1 (Stress associated RNA-binding protein 1) in Arabidopsis. The hap1 of OsSTL1 was identified as the superior haplotype and a non-synonymous SNP was most likely to be the functional site. We also determined that the level of salt tolerance was improved by combining haplotypes of different genes. Our study provides a foundation for molecular breeding and functional analysis of salt tolerance in rice seedlings.

Background: Breast cancer is a major global health burden, with hereditary factors such as BRCA1/2 mutations significantly increasing the lifetime risk. This meta-analysis aimed to evaluate the outcomes of selective estrogen receptor modulators (SERMs), tamoxifen, and raloxifene as chemopreventive agents for breast cancer risk reduction in BRCA1/2 mutation carriers. Methods: A meta-analysis was conducted according to the PRISMA guidelines. PubMed, Cochrane Library, and MEDLINE databases were searched for relevant studies published between 2000 and 2024. Case-control studies and observational cohort studies examining the use of tamoxifen/raloxifene in BRCA1/2 carriers were included. Data on the incidence and risk ratios of breast cancer were also extracted. Quality was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects meta-analysis was performed using Review Manager (version 5.4.0). Results: Nine studies (13,676 women) were included. Two studies had low risk, and the remaining seven studies had moderate risk, as assessed by the NOS checklist. Pooled analysis showed tamoxifen/raloxifene decreased breast cancer risk compared to controls (RR 0.80, 95% CI 0.72–0.88, p  = 0.04). The risk ratio of breast cancer incidence among BRCA1/2 carriers was reduced after tamoxifen use (RR 1.82, 95% CI 1.48–2.23, p  < 0.00001). Subgroup analysis revealed reduced breast cancer risk with SERM use in both BRCA1 (RR 1.51, 95% CI 1.48–1.51) and BRCA2 carriers (RR 1.48, 95% CI 1.40–1.58). The heterogeneity ranged from 51 to 85%, representing high significance and variation in true effect sizes underlying the different included studies. Whereas the heterogeneity among subgroups BRCA1 and BRCA2 was 98%, and the difference was 0%, showing no difference in response to SERM for risk reduction of breast cancer. Conclusion: This meta-analysis provides evidence that tamoxifen and raloxifene significantly reduce the breast cancer risk in women with BRCA1/2 mutations. Chemoprevention efficacy was similar for both BRCA1 and BRCA2 carriers. Further research is needed to validate these findings and to optimize their use in high-risk populations.

Skin cancer is a growing global health concern, marked by high incidence and significant mortality, particularly in aggressive melanoma subtypes. In this study, we employed an integrative network pharmacology and molecular docking approach to evaluate the anticancer potential of Nigella sativa (black seed) against skin cancer. Initially, 13 active compounds were identified from N. sativa based on stringent pharmacokinetic criteria. Target prediction using SwissTargetPrediction, integrated with 9697 skin cancer-associated genes from GeneCards and DisGeNET, revealed 303 overlapping targets implicated in critical oncogenic processes. Subsequent Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses via the DAVID database identified 780 biological processes, 87 cellular components, and 278 molecular functions. Enriched pathways included the positive regulation of the MAPK cascade, EGFR signaling, angiogenesis, and several other pathways central to skin cancer pathogenesis. The compound-target network further underscored the polypharmacological nature of N. sativa, highlighting hub genes. Molecular docking studies were conducted to validate the interactions of select bioactive compounds with key receptors (AR, CDK4, EGFR, MAPK1, and MAPK3). Among the compounds, Gramisterol (CID: 5283640) demonstrated the strongest binding affinities, with energies of − 9.1 kcal/mol for both EGFR and MAPK3, − 9.0 kcal/mol for CDK4, − 8.2 kcal/mol for MAPK1, and − 7.4 kcal/mol for AR. Cycloeucalenol (CID: 101690), Obtusifoliol (CID: 65225) and Lophenol (CID: 160482) also exhibited potent interactions, particularly with EGFR, MAPK1, MAPK3 and CDK4, supporting their potential to disrupt tumor proliferation and survival signaling. Collectively, these findings indicate that N. sativa ’s bioactive compounds can modulate multiple cancer-related pathways, offering a promising multi-target strategy for skin cancer therapy. This computational study lays a robust foundation for subsequent in vitro and in vivo validations and paves the way for the development of novel, less toxic therapeutic regimens against skin cancer. Although the computational discoveries offer a solid conceptual foundation, evidence of their clinical significance is still pending. The next crucial stage of this investigation is to conduct extensive in vitro and in vivo verification investigations in order to close this gap. These initiatives will be crucial to converting our research into practical skin cancer treatment plans.

Background Despite breakthroughs in treatment, metastatic castration-resistant prostate cancer (mCRPC) continues to pose a substantial problem. This meta-analysis sought to assess the efficacy and safety of immunotherapy-chemotherapy combinations in mCRPC. Methods A thorough search of ClinicalTrials.gov, Embase, PubMed, SCOPUS, and Web of Science was performed to retrieve randomised controlled trials (RCTs) published between January 2000 and July 2024. The primary outcomes included overall survival (OS), progression-free survival (PFS), PSA response rate, time to PSA progression, and severe adverse events (SAEs). Data were aggregated using fixed-effect or random-effects models dependent on heterogeneity. Results Four RCTs involving 2,289 participants were included. The pooled results showed no statistically significant advantage of immunotherapy-chemotherapy combinations over placebo or docetaxel alone for OS (HR = 0.95; 95%CI: 0.79–1.14; P  = 0.56), PFS (HR = 0.93; 95%CI: 0.80–1.07; P  = 0.32), PSA response rate (RR = 0.99; 95%CI: 0.66–1.49; P  = 0.96), time to PSA progression (HR = 1.01; 95%CI: 0.90–1.14; P  = 0.85). The risk of SAEs was also not significantly different between the intervention and control groups (RR = 0.95; 95%CI: 0.71–1.29; P  = 0.76). Conclusion Existing findings do not suggest a significant advantage of immunotherapy-chemotherapy combos over chemotherapy alone in mCRPC. However, the small number of trials and study heterogeneity call for caution in interpretation. Further high-quality RCTs are required to determine the role of these combinations in mCRPC treatment.

Non-coding RNAs (ncRNAs) play a crucial role in breast cancer progression, necessitating advanced computational approaches for precise disease classification. This study introduces a Deep Reinforcement Learning (DRL)-based framework for predicting ncRNA–disease associations in metaplastic breast cancer (MBC) using a multi-dimensional descriptor system (ncRNADS) integrating 550 sequence-based features and 1,150 target gene descriptors (miRDB score ≥ 90). The model achieved 96.20% accuracy, 96.48% precision, 96.10% recall, and a 96.29% F1-score, outperforming traditional classifiers such as support vector machines (SVM) and neural networks. Feature selection and optimization reduced dimensionality by 42.5% (4,430 to 2,545 features) while maintaining high accuracy, demonstrating computational efficiency. External validation confirmed model specificity to breast cancer subtypes (87–96.5% accuracy) and minimal cross-reactivity with unrelated diseases like Alzheimer’s (8–9% accuracy), ensuring robustness. SHAP analysis identified key sequence motifs (e.g., \"UUG\") and structural free energy (ΔG = − 12.3 kcal/mol) as critical predictors, validated by PCA (82% variance) and t-SNE clustering. Survival analysis using TCGA data revealed prognostic significance for MALAT1, HOTAIR, and NEAT1 (associated with poor survival, HR = 1.76–2.71) and GAS5 (protective effect, HR = 0.60). The DRL model demonstrated rapid training (0.08 s/epoch) and cloud deployment compatibility, underscoring its scalability for large-scale applications. These findings establish ncRNA-driven classification as a cornerstone for precision oncology, enabling patient stratification, survival prediction, and therapeutic target identification in MBC.

MoHrip2 Magnaporthe oryzae hypersensitive protein 2 is an elicitor protein of rice blast fungus M. oryzae. Rice seedlings treated with MoHrip2 have shown an induced resistance to rice blast. To elucidate the mechanism underlying this MoHrip2 elicitation in rice, we used differential-display 2-D gel electrophoresis and qRT-PCR to assess the differential expression among the total proteins extracted from rice leaves at 24 h after treatment with MoHrip2 and buffer as a control. Among ~1000 protein spots detected on each gel, 10 proteins were newly induced, 4 were up-regulated, and 3 were down-regulated in MoHrip2-treated samples compared with the buffer control. Seventeen differentially expressed proteins were detected using MS/MS analysis and categorized into six groups according to their putative function: defense-related transcriptional factors, signal transduction-related proteins, reactive oxygen species (ROS) production, programmed cell death (PCD), defense-related proteins, and photosynthesis and energy-related proteins. The qPCR results (relative expression level of genes) further supported the differential expression of proteins in MoHrip2-treated rice leaves identified with 2D-gel, suggesting that MoHrip2 triggers an early defense response in rice leaves via stress-related pathways, and the results provide evidence for elicitor-induced resistance at the protein level.

The genome contributes to the uniqueness of an individual breed, and enables distinctive characteristics to be passed from one generation to the next. The allelic heterogeneity of a certain breed results in a different response to a pathogen with different genomic expression. Disease resistance in chicken is a polygenic trait that involves different genes that confer resistance against pathogens. Such resistance also involves major histocompatibility (MHC) molecules, immunoglobulins, cytokines, interleukins, T and B cells, and CD4+ and CD8+ T lymphocytes, which are involved in host protection. The MHC is associated with antigen presentation, antibody production, and cytokine stimulation, which highlight its role in disease resistance. The natural resistance-associated macrophage protein 1 (Nramp-1), interferon (IFN), myxovirus-resistance gene, myeloid differentiation primary response 88 (MyD88), receptor-interacting serine/threonine kinase 2 (RIP2), and heterophile cells are involved in disease resistance and susceptibility of chicken. Studies related to disease resistance genetics, epigenetics, and quantitative trait loci would enable the identification of resistance markers and the development of disease resistance breeds. Microbial infections are responsible for significant outbreaks and have blighted the poultry industry. Breeding disease-resistant chicken strains may be helpful in tackling pathogens and increasing the current understanding on host genetics in the fight against communicable diseases. Advanced technologies, such as the CRISPR/Cas9 system, whole genome sequencing, RNA sequencing, and high-density single nucleotide polymorphism (SNP) genotyping, aid the development of resistant breeds, which would significantly decrease the use of antibiotics and vaccination in poultry. In this review, we aimed to reveal the recent genetic basis of infection and genomic modification that increase resistance against different pathogens in chickens.

The grain number per panicle (GNP) is an important yield component. Identifying naturally favorable variations in GNP will benefit high-yield rice breeding. Here, we performed a genome-wide association study using a mini-core collection of 266 cultivated rice accessions with deep sequencing data and investigated the phenotype for three years. Three genes, i.e., TOTOU1 ( TUT1 ), Grain height date 7 ( Ghd7 ), and Days to heading 7 / Grain height date 7.1 / Pseudo-Response Regulator37 ( DTH7/Ghd7.1 / OsPRR37 ), which regulate GNP, were found in the quantitative trait loci (QTL) identified in this study. A stable QTL, qGNP1.3 , which showed a strong correlation with variations in GNP, was repeatedly detected. After functional and transgenic phenotype analysis, we identified a novel gene, regulator of grain number 1a ( RGN1a ), which codes for protein kinase, controlling GNP in rice. The RGN1a mutation caused 37.2%, 27.8%, 51.2%, and 25.5% decreases in grain number, primary branch number per panicle, secondary branch number per panicle, and panicle length, respectively. Furthermore, breeding utilization analysis revealed that the additive effects of the dominant allelic variants of RGN1a and DTH7 played a significant role in increasing the grain number per panicle in japonica rice. Our findings enrich the gene pool and provide an effective strategy for the genetic improvement of grain numbers.

Background DNA repair mechanisms, particularly RAD51 -mediated homologous recombination repair, play a crucial role in breast cancer development, with the rs1801320 (135G > C) polymorphism showing conflicting associations across studies. This meta-analysis aimed to assess the relationship between RAD51 rs1801320 polymorphism and breast cancer susceptibility. Method We systematically searched PubMed and Web of Science databases through August 15, 2024, and included 16 case–control studies comprising 4743 breast cancer cases and 4448 controls, analyzing various genetic models using R Studio. Results Our results revealed significant associations in several genetic models: the allele contrast model (C vs. G) showed an increased risk (OR = 1.37, 95% CI: 1.04–1.80, p = 0.0249. The recessive model (CC vs. CG + GG) demonstrated a strong risk association (OR = 2.68, 95% CI: 1.55–4.61, p = 0.00038), while the dominant model (CC + CG vs. GG) showed no significant association (OR = 1.12, 95% CI: 0.98–1.28, p = 0.1037). Pairwise comparisons revealed the CC genotype as a substantial risk factor, particularly in CC vs. GG (OR = 2.31, 95% CI: 1.58–3.37, p = 0.00001) and CC vs. CG (OR = 2.97, 95% CI: 1.53–5.77, p = 0.00128) comparisons. Most models showed moderate to high heterogeneity (I 2  = 30–93%), though publication bias was detected in some analyses. Conclusion This comprehensive meta-analysis is larger than previous studies and provides robust evidence that the RAD51 rs1801320 CC genotype significantly increases breast cancer risk, particularly in recessive and homozygous comparison models, suggesting potential implications for cancer risk assessment and therapeutic strategies targeting DNA repair mechanisms.