Background: In this article, we review the most recent research on probiotics effects on diarrhea in both human and animal models of the condition along with the therapeutic potential of these compounds based on their findings.Recent Findings: Nearly 50%–80% of cancer patients experience chemotherapy‐induced diarrhea (CID), serious gastrointestinal toxicity of chemotherapeutic and radiation regimens that leads to prolonged hospitalizations, cardiovascular problems, electrolyte imbalances, disruptions in cancer treatment, poor cancer prognosis, and death. CID is typically categorized as osmotic diarrhea. The depletion of colonic crypts and villi by radiotherapy and chemotherapy agents interferes with the absorptive function of the intestine, thereby decreasing the absorption of chloride and releasing water into the intestinal lumen. Probiotic supplements have been found to be able to reverse the intestinal damage caused by chemo‐radiation therapy by promoting the growth of crypt and villi and reducing inflammatory pathways. In addition, they support the modulation of immunological and angiogenesis responses in the gut as well as the metabolism of certain digestive enzymes by altering the gut microbiota.Conclusion: Beyond the benefits of probiotics, additional clinical research is required to clarify the most effective strain combinations and dosages for preventing chemotherapy and radiotherapy diarrhea.

Background: The coronavirus disease 2019, also known as COVID-19, imposed various challenges to healthcare and became a pandemic accompanied by a high rate of mortality. This infection has many manifestations and affects nearly all body systems. The circulatory and coagulation systems also seem to be affected. Studies show elevated rates of thrombotic events within COVID-19 patients such as disseminated intravascular coagulation (DIC), deep vein thrombosis (DVT), and pulmonary embolism (PE). Incidences of such coagulopathies were correlated to poor patient prognosis and mortality. Given the importance, complication, and mortality caused by thrombotic events (TEs) in COVID-19 patients, the goals of this study are to collect and analyze data on coagulopathy in COVID-19 patients and the pathophysiology and molecular events behind it. We also aim to bring attention to the role of d-dimer in COVID-19 infection by presenting the most recent information available from research studies evaluating d-dimer as a potential biomarker for disease severity, as well as mortality in COVID-19 patients. Main body: Various mechanisms are described for COVID-19 coagulopathies such as endothelial cell dysfunction, fibrinolysis inhibitor overexpression, immuno-thrombosis, and imbalance between pro- and anticoagulants, to name a few. d-dimer which is a degradation product of fibrin is a helpful diagnostic tool for the assessment of clots and thrombosis. Given the pro-thrombotic nature of COVID-19 infection, within the current narrative review, we studied the diagnostic value of d-dimer for PE prediction. Several studies utilized d-dimer as a predictive tool for detecting PE, and the results were varied. Different cutoff points are proposed ranging from 0.5 up to over 4 mg/L with varying sensitivity and specificity. Although CT pulmonary angiography (CTPA) is the standard model for the prediction of PE, radiation exposure, contrast nephropathy, higher cost, and lack of adequate access can shift our diagnosis into models based on d-dimer. Short conclusion: In summary, various coagulopathies have been associated with COVID-19 infection, and a safe and early diagnosis is needed. d-dimer showed various successes in PE prediction and can be a good candidate for further research and diagnostic model and algorithm development.

Diabetes is often associated with dyslipidemia, increasing the risk of cardiovascular complications. Studies have shown that Vitamin E, as a potent antioxidant, may improve serum lipid profile by reducing oxidative stress and inflammation. However, findings on its effects on diabetic patients remain inconsistent. To address this gap, this meta-analysis aims to evaluate the impact of vitamin E on serum lipid parameters in individuals with diabetes. A search was conducted on databases from inception to June 2025 to identify relevant randomized controlled trials (RCTs). Pooled effect sizes were estimated using weighted mean differences (WMDs) with 95% confidence intervals (CIs), applying a random-effects model. All statistical analyses were performed using STATA (V. 11.2). The pooled analysis of 28 RCTs found that overall vitamin E administration in diabetic patients significantly reduced total cholesterol (TC) (WMD: - 5.20 mg/dL, 95% CI: [- 7.60, - 2.80], p < 0.001) and low-density lipoprotein cholesterol (LDL-C) (WMD: - 4.21 mg/dL, 95% CI: [- 7.32, - 1.09], p = 0.008). While no significant change was observed in triglyceride (TG) (WMD: - 6.19 mg/dL, 95% CI: [- 13.13, 0.75], p = 0.081) and high-density lipoprotein cholesterol (HDL-C) serum levels (WMD: 0.57 mg/dL, 95%CI: [- 0.11, 1.24], p = 0.99). Subgroup analysis showed that vitamin E reduced TG with longer durations, lowered TC and LDL-C in participants with high baseline levels, higher doses, or longer interventions, and increased HDL-C only in studies lasting over 8 weeks. Linear regression analysis found no significant associations between vitamin E dose or duration and serum lipids. In contrast, non-linear dose-response analysis showed a significant inverse relationship between vitamin E dose and TC levels. Vitamin E administration may improve lipid profiles in diabetic patients, with significant reductions in TC and LDL-C, while effects on TG and HDL-C were not statistically significant but showed potential clinical relevance. Subgroup analysis highlighted greater benefits with higher doses (> 400 IU/day) and longer intervention durations (> 8 weeks), particularly in individuals with elevated baseline lipid levels. Further large-scale studies are needed to confirm these findings and establish optimal dosing strategies for clinical application.

This study examined the anti-inflammatory and antioxidant properties of Capparis spinosa L. (caper) in order to determine its medicinal potential in the treatment of acute colitis. Sixty male rats were divided into six groups. After the experimental period, distal colonic extension was collected for determination of colonic damage, oxidative stress markers, along with antioxidant markers. The impact of altered levels of inflammatory cytokines in colon tissues on the underlying mechanisms examined. The results showed that administering different doses of caper led to significant decreases in TNF-α and IL-6 levels when compared to the control colitis group (p < 0.001). Caper treatment effectively lowered elevated oxidative stress factors (MDA, NO, and MPO) compared to the control colitis group (p < 0.001). Caper treatment resulted in a significant increase in antioxidant factors (CAT, SOD, and GSH) compared with the control colitis group (p < 0.001). Significant improvements in tissue repair were observed in caper-treated groups compared to positives and control colitis (p < 0.001). The study highlights caper may be useful in the treatment of acute colitis due to its ameliorative effects on inflammation, oxidative stress, and tissue repair.

Background and purpose: Peritoneal metastasis significantly impacts prognosis and treatment strategies in ovarian cancer. Traditional imaging techniques have limited sensitivity in preoperative detection. Radiomics-based machine learning models offer a promising non-invasive approach to improve diagnostic accuracy. This study systematically reviews and meta-analyzes their predictive performance. Materials and methods: A systematic search was conducted up to June 2025 for studies that developed and validated machine learning models based on radiomic features for the prediction of peritoneal metastasis in ovarian cancer. Quality of included studies was evaluated using the METRICS and QUADAS-2 tool. Pooled sensitivity, specificity, and area under the curve (AUC) were calculated via bivariate random-effects meta-analysis. Heterogeneity, sensitivity, and publication bias analyses were also conducted. Results: Six studies were included in the systematic review and qualitative synthesis, with five studies comprising 448 individual participants derived exclusively from validation cohorts meeting criteria for meta-analysis. Radiomics models yielded a pooled AUC of 0.81 (95% CI: 0.71–0.88), accompanied by a sensitivity of 73% and specificity of 77%. Clinical models demonstrated a pooled AUC of 0.82 (95% CI: 0.79–0.85). The pooled AUC for the combined clinical–radiomics models was 0.87 (95% CI: 0.83–0.89). Methodological quality assessed via METRICS ranged from 64.5% to 86.3%, with a mean score of 77.6%. The QUADAS-2 assessment indicated a low overall risk of bias, with minor concerns noted in the patient selection and index test domains. No significant heterogeneity, publication bias, or outliers were detected. Conclusion: Radiomics-based machine learning models demonstrate potential as tools for the prediction of peritoneal metastasis in ovarian cancer and may assist in preoperative risk stratification. Further large-scale, multicenter prospective studies with standardized methodologies and external validation are necessary to confirm clinical applicability.