The current study aims to investigate the significance of N ⁶-methyladenosine (m⁶A) methylation-related genes in the clinical prognosis of hepatocellular carcinoma (HCC) using bioinformatics analyses based on The Cancer Genome Atlas (TCGA) database. Transcriptome data and corresponding clinical data on m⁶A methylation-related genes (including 15 genes) were obtained from TCGA database. Differential expression of 15 genes was identified. Survival curves of subgroups based on m⁶A methylation-related gene expression levels were plotted. We selected potential predictive genes and analyzed their prognostic values using bioinformatics methods. Eleven genes (METTL3, YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, FTO, KIAA1429, HNRNPC, HNRNPA2B1, and RBM15) were found to be overexpressed in HCC. Of these, five genes had worse survival (P < 0.05). There was a significant difference in the survival rate between subgroups with different expression levels of m⁶A. We selected five potential predictors (METTL3, KIAA1429, ZC3H13, YTHDF1, and YTHDF2) that met the independent predictive value. ZC3H13 was upregulated in patients with high cancer risk, whereas METTL3, KIAA1429, YTHDF1, and YTHDF2 were downregulated. In summary, we found that the expression levels of m⁶A methylation-related genes were different in patients with HCC and correlated with survival and prognosis. This implies that m⁶A methylation-related genes may be promising prognostic indicators or therapeutic targets for HCC. Graphical abstract

Background. The bile infection may already exist before the administration of an interventional procedure, despite no clinical manifestations of cholangitis detected. Blood cultures remained negative even in more than half of the febrile cases with cholangitis. Risk factors associated with bacterial growth in bile before the intervention are not well defined. To establish the bacterial profiles isolated from the bile samples and to identify risk factors for bacterial colonization in the bile system. Methods. Individuals who underwent endoscopic retrograde cholangiopancreatography (ERCP) interventions were enrolled. Bile samples were aspirated and were immediately transferred into a sterile tube for storage. Results. Positive bile cultures were detected in 363 (38.0%) of 956 patients, including 322 benign diseases and 41 malignances. Of 363 positive cases, 351 (96.7%) were monoinfection and 12 (3.3%) multi-infection. Escherichia coli were the most common Gram-negative bacteria (210, 56.0%), followed by Klebsiella pneumoniae (45, 12.0%). Enterococcus faecalis represented the most common Gram-positive microorganism (19, 5.07%), while Candida albicans (11, 2.93%) were the dominant fungi. Klebsiella pneumoniae were more frequently detected in malignant diseases (P=0.046). Age, previous ERCP history or OLT history, and CBD diameter were independent risk factors for positive cultures (P<0.05) while preoperative jaundice drug therapy was the protective factor for bile infection (P<0.05). Conclusion. Monomicrobial infection was dominant among all infections, and Klebsiella pneumoniae strains were more frequently isolated from patients with malignant diseases. To effectively manage patients who are at a high risk for bile infection, a detailed diagnosis and treatment plan for each case should be prepared.

Colorectal cancer (CRC) is one of the most common malignancies worldwide. Unlike endothelium‑dependent vasculature, vasculogenic mimicry (VM) is an alternative type of blood supply in tumors that is frequently associated with poor patient outcome. Nectin‑4 serves a vital role in the formation and maintenance of adherens junctions; integrin β‑1 (ITGB1) promotes tumor invasion, metastasis and VM formation. In the present study, the analysis of nectin‑4 mRNA expression in a database of The Cancer Genome Atlas (TCGA) was combined with that of another non‑overlapping cohort of 68 patients with CRC. TCGA data were used to examine nectin‑4 mRNA expression in CRC and its correlation with the clinicopathological features of patients. Data from the non‑overlapping cohort of patients was used to determine nectin‑4 and ITGB1 protein expression in CRC by immunohistochemical (IHC) staining. Cluster of differentiation 34/periodic acid‑Schiff double staining was performed to validate the presence of VM formation. The association with, and significance of combining nectin‑4, ITGB1 protein expression and VM formation for predicting patient prognosis was evaluated. The TCGA dataset demonstrated that nectin‑4 mRNA was upregulated in CRC, which was significantly relate to lymph node metastasis (P=0.0017), distant metastasis (P=0.0045), and tumor‑node‑metastasis (TNM) stage (P=0.0015). Of the 68 patients analyzed by IHC staining, 48 (70.6%) were positive for nectin‑4, 46 (67.6%) for ITGB1 and 17 (25%) for VM formation. Nectin‑4 protein expression was associated with ITGB1 protein expression (P<0.01) and VM formation (P<0.05). Nectin‑4, ITGB1 expression and VM formation were associated with distant metastasis stage (P<0.05) and TNM stage (P<0.05). Based on these findings it was concluded that nectin‑4 was upregulated in CRC tissues compared with normal mucosal tissues, and was associated with ITGB1 expression and VM formation. Furthermore, nectin‑4 and ITGB1 protein expression, together with VM formation may be used to predict poor prognosis in CRC.

Objective: To assess the efficacy and safety of peripherally acting mu-opioid receptor antagonists (PAMORAs) for the treatment of opioid-induced constipation (OIC). Methods: Randomized controlled trials (RCTs) were searched for OIC therapy comparing PAMORAs with placebo. Both a pairwise and network meta-analysis were performed. The surface under the cumulative ranking area (SUCRA) was used to determine the efficacy and safety of OIC treatment using different PAMORAs. Results: The primary target outcome was a response that achieves an average of three or more bowel movements (BMs) per week. In the network meta-analysis, four PAMORAs (naldemedine, naloxone, methylnaltrexone, and alvimopan) showed a better BM response than the placebo. Naldemedine was ranked first (odds ratio [OR] = 2.8, 95% credible interval [CrI] = 2–4.5, SUCRA = 89.42%), followed by naloxone (OR = 2.9, 95% CrI = 1.6–5.3, SUCRA = 87.44%), alvimopan (OR = 2.2, 95% CrI = 1.3–3.5, SUCRA = 68.02%), and methylnaltrexone (OR = 1.7, 95% CrI = 1.0–2.8, SUCRA = 46.09%). There were no significant differences in safety found between the PAMORAs and the placebo. Conclusions: We found that PAMORAs are effective and can be safely used for the treatment of OIC. In network meta-analysis, naldemedine and naloxone appear to be the most effective PAMORAs for the treatment of OIC.

Background: Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is linked to significant alterations in gut microbiota. Conventional diagnostic approaches frequently rely on invasive procedures, contributing to patient discomfort; hence, non-invasive diagnostic models present a valuable clinical alternative.Methods: Metagenomic and amplicon sequencing data were collected from fecal samples of patients with IBD and healthy individuals across diverse geographic regions. Diagnostic models were developed using Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), and Feedforward Neural Network (FFNN), complemented by an ensemble model via a voting mechanism. Five-fold cross-validation facilitated the differentiation between normal controls (NC) and IBD, as well as between CD and UC.Results: K-mer-based methods leveraging metagenomic sequencing data demonstrated robust diagnostic performance, yielding ROC AUCs of 0.966 for IBD vs. NC and 0.955 for CD vs. UC. Similarly, models based on amplicon sequencing achieved ROC AUCs of 0.831 for IBD vs. NC and 0.903 for CD vs. UC. In comparison, k-mer-based approaches outperformed traditional microbiota-based models, which produced lower ROC AUCs of 0.868 for IBD vs. NC and 0.810 for CD vs. UC. Across all machine learning frameworks, the FFNN consistently attained the highest ROC AUC, underscoring its superior diagnostic performance.Conclusion: The integration of k-mer-based feature extraction with machine learning offers a non-invasive, highly accurate approach for IBD diagnosis, surpassing traditional microbiota-based models. This method holds considerable potential for clinical use, offering an effective alternative to invasive diagnostics and enhancing patient comfort.