Context: Vitamin D deficiency is highly prevalent in chronic liver disease. Although international societies recommend vitamin D supplementation in cases of proven deficiency, the impact of vitamin D on chronic liver disease remains uncertain. Objective: Our aim was to evaluate the effects of vitamin D supplementation in patients with chronic liver disease by conducting a systematic review and meta-analysis of randomized controlled trials (RCTs). Data Sources: We systematically searched PubMed, EMBASE and the Cochrane Library on July 2, 2024. Data Extraction: Our primary outcomes involved survival, controlled attenuation parameter (CAP), liver stiffness measurement (LSM), and effects on changes in liver enzymes. Secondary outcomes included lipid profile and homeostasis model assessment of insulin resistance (HOMA-IR), among others. The pooled risk ratio (RR), mean difference (MD), and corresponding 95% CIs were calculated using the random-effects model. Data Analysis: Forty-six RCTs were included, comprising 4084 patients. When we compared the vitamin D group with the control, the RR for overall survival was 1.14 (95% CI, 0.85-1.54; 4 RCTs) at 6 months and 0.99 (95% CI, 0.83-1.17; 4 RCTs) at the 12-month follow-up. Vitamin D supplementation did not result in a lower CAP (MD, −23.50 dB/m; 95% CI, −81.72 to 34.72; 3 RCTs) and LSM (MD, −0.65 kPa; 95% CI, −1.98 to 0.68; 3 RCTs). A significant reduction in HOMA-IR was observed in the vitamin D group (MD, −0.31; 95% CI, −0.62 to −0.01; 15 RCTs). Alanine aminotransferase (ALT) (MD, −4.98 IU/L; 95% CI, −8.28 to −1.68; 24 RCTs), aspartate aminotransferase (AST) (MD, −3.33 IU/L; 95% CI, −6.25 to −0.40; 23 RCTs), gamma-glutamyl transferase (GGT) (MD, −5.14 IU/L; −6.40; −3.88; 11 RCTs), triglycerides (MD, −7.59 mg/dL; 95% CI, −15.09 to −0.81), and insulin (MD −0.79 μIU/L; 95% CI, −1.36 to −0.21) were significantly reduced in the patients with vitamin D supplementation. Conclusion: Our results showed significantly reduced ALT, AST, GGT, triglycerides, insulin, and HOMA-IR in the vitamin D–supplemented group; however, the effect was modest. In addition, there were no differences in survival, CAP, or LSM. Further RCTs with adequate power are warranted to clarify the results. Systematic Review Registration: PROSPERO registration No. CRD42022370312

Background/Objectives: Incidence of malnutrition varies greatly among gastrointestinal (GI) cancer patients and has a major impact on prognosis. We performed a meta-analysis to identify risk factors for malnutrition risk, malnutrition diagnosis, and cachexia in patients with GI cancer. Methods: A systematic search was performed on 31 October 2025 on the PubMed (Medline), Embase, and Cochrane Library databases. Eligible studies reported on risk factors for malnutrition risk, malnutrition diagnosis, malnutrition-related complication risk and cachexia in adult patients with GI cancer. Articles on neuroendocrine tumours, primary cancer outside the GI tract, and the paediatric population were excluded. The random-effects model yielded the pooled odds ratios (ORs) and 95% confidence intervals (CIs) for the investigated risk factors. Results: A total of 37,624 records were identified. Data from 262,525 patients from 578 articles were included in the analysis. Older age (≥65) was associated with higher odds for malnutrition risk across all GI cancers. In gastric cancer, males had a lower odds for malnutrition risk (OR 0.84; 95% CI 0.75–0.95); however, the sex difference across other cancer types was heterogeneous, and mostly not significant. Tumour location influenced the odds for malnutrition-related complication risk in pancreatic ductal adenocarcinoma (head vs. body/tail—OR 1.48; 95% CI 0.98–2.23) and colorectal cancer (colon vs. rectal—OR 1.39; 95% CI 1.07–1.81; right-sided vs. left-sided—OR 1.54; 95% CI 1.34–1.77). Increased C-reactive protein alone indicated higher odds for malnutrition risk at baseline. Conclusions: Inflammatory biomarkers and tumour characteristics may indicate malnutrition risk in GI cancer at baseline. There is a great need for standardised and harmonised approaches in nutritional status assessment in GI cancer.

Introduction: Oral potentially malignant disorders (OPMDs) can lead to oral cancer, which is one of the most common cancers worldwide. Prevention is crucial in the avoidance of malignant transformations of OPMDs. Artificial intelligence (AI) provides a new and noninvasive tool for analyzing medical data, such as patient data, radiologic images, and clinical photographs. These AI-based tools can help in the decision-making process. However, histological examination is still the gold standard for diagnosing OPMDs. Objectives: This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of artificial intelligence on intraoral photographs of patients with OPMDs. Methods: A systematic search was conducted on 5 major databases (MEDLINE, Embase, Cochrane Library, Scopus, and Web of Science) on November 10, 2023. Included studies compared AI methods to histology examination as the reference. A quantitative analysis was carried out to assess sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), diagnostic odds ratio (DOR), positive likelihood ratio (LR ⁺ ), and negative likelihood ratio (LR ⁻ ) calculated with 95% confidence intervals (CIs). Results: Six eligible articles were included, with 898 images out of 4,046 tested using AI-based architectures. Five studies investigated at least 2 AI models. The overall sensitivity, specificity, DOR, LR ⁺ , and LR ⁻ were 0.94 (95% CI, 0.88 to 0.95), 0.95 (95% CI, 0.85 to 0.98), 212.39 (95% CI, 56.39 to 800.00), 16.89 (95% CI, 5.72 to 48.68), and 0.08 (95% CI, 0.05 to 0.13) for the best-performing AI-based architectures in terms of sensitivity, respectively. Conclusion: AI-based diagnostic tools have high negative predictive value that could help identify OPMD lesions using intraoral photographs. Knowledge Transfer Statement: This systematic review on AI-based methods to diagnose oral potentially malignant disorders showed that although their high negative predictive value could reduce unnecessary specialist consultations, clinical judgment remains paramount. Further prospective studies are needed to evaluate the integration of AI diagnostics into routine care and screening and policies to enhance efficiency and support early detection and prevention of oral cancer.