Androgenetic alopecia (AGA), the most prevalent hair loss type, significantly affects life quality. While androgen excess has been linked to metabolic dysfunction–associated steatotic liver disease (MASLD), their direct association remains underexplored. The cross-sectional investigation enrolled 7,993 adults (35–70 years old) from the FACS cohort (49.1% male, mean age 48.6 ± 9.63). Participants with extreme caloric intake, AGA-relevant comorbidities such as anemia, thyroid disorders, cancer, and pregnancy, or confounding conditions were excluded. MASLD was defined using the Fatty Liver Index (FLI) and adult cardiometabolic criteria. Multivariate logistic regression assessed the connection between MASLD and AGA, adjusting for key literature-based confounders. AGA was present in 6,004 participants. MASLD was linked to 31% elevated odds of AGA (OR = 1.31;95%CI:1.13–1.52;p < 0.001), with a stronger effect in females (OR = 1.62;95%CI:1.30–2.03;p < 0.001). Elevated risk was also observed among those with high carbohydrate intake (OR = 1.45), low physical activity (OR = 1.48), and no lipid-lowering therapy (OR = 1.30). No significant association was found in individuals with healthier lifestyle profiles. The MASLD–AGA relationship remained independent of calorie, lipid, protein, and fiber intake, insulin resistance, and inflammatory status. MASLD is significantly associated with AGA, particularly in women and individuals with specific lifestyle patterns. These findings highlight the importance of metabolic evaluation in AGA patients.

Drug-drug interactions (DDIs) between antiretroviral therapy (ART) and commonly used co-medications in HIV patients, especially women, impact treatment efficacy and patient safety. This study aimed to study the prevalence and types of drug-drug interactions (DDIs) between antiretroviral therapy drugs (ARTs) and comedications among a female population with HIV. Additionally, the study investigates the association of these DDIs with ART medication changes and treatment adherence. This cross-sectional study included 632 adult women living with HIV (WLHIV). Data was retrospectively extracted from patient files. Drug.com interaction checker website was used to assess DDIs between ART and non-ART medications. Changes to the ART regimen previously attributed to ART side effects or patient non-adherence were considered drug changes. A total of 429 WLHIV (mean age: 44.05 ± 9.50) were eligible. The prevalence of DDIs between ART and non-ART medications was 21.4%, with 4.7% minor, 18.4% moderate, and 8.9% major interactions. The highest prevalence of DDI was among cardiovascular medication users (71.7%), followed by central nervous system drugs (69.2%). Changing medications resulted in a decrease in DDIs, with significant reductions in total and minor interactions. Participants without DDIs had better adherence to ART. DDI between ART and non-ART medications was significantly associated with ART drug change, even after accounting for side effects attributed to ARTs, indicating an independent twofold association (OR = 1.99, CI 1.04–3.77). Moreover, further adjustments for HIV viral load and CD4 + cell count did not change the significance of the association (OR = 2.01, CI 1.03–3.92). DDIs in WLHIV impact adherence to ART. Altering ART may not be directly related to ART side effects, but rather primarily due to interactions with non-ART medications. Modifying non-ART drug regimens can reduce the likelihood of DDIs.

Background: Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area. Methods: This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35–70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP). Results: Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth. Conclusion: ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.

Post-Liver transplantation (LT) survival rates stagnate, with biliary complications (BC) as a major cause of death. We analyzed longitudinal data with a median 19-month follow-up. BC was diagnosed with ultrasounds and MRCP. Missing data was imputed using mean and median. Data preprocessing involved feature scaling and one-hot encoding. Survival analysis used filter (Cox-P, Cox-c) and embedded (RSF, LASSO) feature selection methods. Seven survival machine learning algorithms were used: LASSO, Ridge, RSF, E-NET, GBS, C-GBS, and FS-SVM. Model development employed 5-fold cross-validation, random oversampling, and hyperparameter tuning. Random oversampling addressed data imbalance. Optimal hyperparameters were determined based on average C-index. Features importance was assessed using standardized regression coefficients and permutation importance for top models. Stability was evaluated using 5-fold cross-validation standard deviation. Finally, 1799 observations with 40 outcome predictors were included. RSF with Ridge achieved the highest performance (C-index: 0.699) for BC prediction, while RSF with RSF had the highest performance (C-index: 0.784) for mortality prediction. Top BC predictors were LT graft types, IBD in recipients, recipient’s BMI, recipient’s history of PVT, and previous LT history. For mortality, they were post-transplant AST, creatinine, recipient’s age, post-transplant ALT, and tacrolimus consumption. We identified BC and mortality risk factors, improving decision-making and outcomes.

Background: Functional recovery after traumatic brain injury (TBI) is heterogeneous, and reliance on single time-point outcomes may obscure meaningful post-discharge change. Objective: To identify early clinical, laboratory, and radiologic factors associated with functional change between hospital discharge and six-month follow-up using the Glasgow Outcome Scale-Extended (GOSE). Methods: In this longitudinal study, 2,948 TBI survivors aged 12 years or older admitted to a tertiary trauma center underwent GOSE assessment at discharge and six months post-discharge. Functional trajectories were classified as recovering, stable, or regressing. Multinomial logistic regression evaluated associations between early variables and functional trajectories. Analyses were additionally stratified by discharge GOSE to assess statistical effects. Results: Substantial bidirectional functional change occurred after discharge. Several variables, including age, Glasgow Coma Scale, subarachnoid or intraventricular hemorrhage, hemoglobin, and coagulation indices, were associated with both recovery and regression. Discharge GOSE strongly predicted subsequent change but increased the odds of both improvement and decline. Subgroup analyses indicated many unexpected associations reflected ceiling and floor effects and regression to the mean. Nevertheless, some predictors showed consistent directional effects: in non-severe TBI, age 40–74 years, low admission blood glucose, and higher platelet counts; and in severe TBI, age > 40 years, low admission blood glucose, pneumocephalus, and bilateral fixed pupils. Conclusions: Post-TBI functional outcomes follow complex, often unpredictable trajectories. These findings highlight limitations of static early prognostication and support a trajectory-oriented approach to post-discharge TBI care.