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Abstract Machine learning (ML) offers precise predictions and could improve patient care, potentially replacing traditional scoring systems. A retrospective study at Emtiaz Hospital analyzed 3,180 traumatic brain injury (TBI) patients. Nineteen variables were assessed using ML algorithms to predict outcomes. Data preparation addressed missing values and balancing methods corrected imbalances. Model building involved training-test splits, survival analysis, and ML algorithms like Random Survival Forest (RSF) and Gradient Boosting. Feature importance was examined, with patient risk stratification guiding survival analysis. The best-performing model, RSF with ROS resampling, achieved the highest mean AUC of 0.80, the lowest IBS of 0.11, and IPCW c-index of 0.79, maintaining strong predictive ability over time. Top predictors for in-hospital mortality included age, GCS, pupil condition, PTT, IPH, and Rotterdam score, with high variations in predictive abilities over time. A risk stratification cut-off value of 63.34 separated patients into low and high-risk categories, with Kaplan–Meier curves showing significant survival differences. Our high-performing predictive model, built on first-day features, enables time-dependent risk assessment for tailored interventions and monitoring. Our study highlights the feasibility of AI tools in clinical settings, offering superior predictive accuracy and enhancing patient care for TBI cases.

Abstract 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.

Abstract Background Hypertension (HTN) is well-known as a major risk factor for various noncommunicable diseases. Evidence indicates a link between socioeconomic status and the likelihood of developing HTN. A thorough comprehension of the inequalities in HTN is crucial for implementing evidence-based interventions. This study aimed to assess the socioeconomic disparities in HTN among people aged 60 and older in Fasa County, located in southern Iran. Methods A total of 1,632 seniors, aged 60 and above, were included in the analysis, with data obtained from the Fasa Adults Cohort Study (FACS). Initially, we utilized both simple and multiple logistic regression models to investigate the associations between HTN and the determinant variables. The Blinder‒Oaxaca decomposition method was used to decompose the disparity between the impoverished and the wealthy. Results The research indicated that elderly individuals with a higher risk of developing HTN were notably linked to factors such as socioeconomic status, education level, a history of coronary artery disease, employment status, smoking habits, body mass index (BMI), waist‒hip ratio (WHR), and physical activity (p

Abstract Background Drug-drug interactions (DDIs) between antiretroviral therapy (ART) and commonly used co-medications in HIV patients, especially women, impact treatment efficacy and patient safety. Objective 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. Methods 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. Results 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). Conclusion 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.

Abstract Objectives To enhance the identification of individuals at risk of developing clinically significant kidney stones. Methods In this study, data from the Fasa Adults Cohort Study were analyzed to explore factors linked to symptomatic and clinically significant kidney stone disease. After cleaning, 10,128 participants with 103 variables were studied. One outcome variable (presence of symptomatic kidney stones) and 102 predictor variables from surveys and tests were assessed. Five Machine learning (ML) algorithms (SVM, RF, KNN, GBM, XGB) were applied to examine kidney stone factors, with performance comparisons made. Data balancing was done using SMOTE, and metrics like accuracy, precision, sensitivity, specificity, F1 score, and AUC were evaluated for each algorithm. Results The XGB model outperformed others with AUC of 0.60, while RF, GBM, SVC, and KNN had AUC values of 0.58, 0.57, 0.54, and 0.52. RF, GBM, and XGB showed good accuracy at 0.81, 0.81, and 0.77. Top predictors for kidney stones were serum creatinine, salt intake, hospitalization history, sleep duration, and BUN levels. Conclusions ML models show promise in evaluating an individual’s risk of developing painful kidney stones and recommending early lifestyle changes to reduce this risk. Further research can enhance predictive accuracy and tailor interventions for better prevention/management.

Oxidative stress contributes to the development of cardiometabolic diseases and cancers. Numerous studies have highlighted the adverse effects of high reactive oxygen species (ROS) levels in the progression of chronic noncommunicable diseases and also during infections. On the other hand, antioxidants play a crucial role in preventing oxidative stress or postponing cell damage via the direct scavenging of free radicals or indirectly via the Keap1/Nrf2/ARE pathway, among others. Dietary antioxidants can be obtained from various sources, mainly through a plant-based diet, including fruits and vegetables. The dietary antioxidant index (DAI) has been developed to assess total antioxidant intake from diet. This review delineated the performance of DAI in the risk assessment of different diseases. It is suggested that a high DAI score prevents obesity-related diseases, including diabetes mellitus, hyperuricemia, dyslipidemia, and metabolic (dysfunction)-associated steatotic liver disease (MASLD). Additionally, DAI is negatively associated with Helicobacter pylori and Human papillomavirus infection, thus reducing the risk of gastric and cervical cancer. Also, a high intake of antioxidants prevents the development of osteoporosis, miscarriage, infertility, and mental illnesses. However, further prospective observations and clinical trials are warranted to confirm the application of DAI in preventing diseases that have been studied.

BackgroundAndrogenic alopecia (AGA), the most prevalent hair loss type, causes major psychological distress and reduced quality of life. A definite and safe cure/prevention for this condition is still lacking. The role of oxidative stress and inflammation in AGA pathogenesis prompted us to investigate the association between dietary antioxidant index (DAI) and energy-adjusted dietary inflammatory index (E-DII) with AGA.MethodsThe investigation was designed based on data from 10,138 participants from the Fasa Adult Cohort Study (FACS). DAI and energy-adjusted DII (E-DII) were calculated utilizing a validated 125-item food frequency questionnaire (FFQ). A physician diagnosed AGA. Logistic regression models were utilized to evaluate the association of DAI and E-DII with AGA.ResultsAfter exclusion, 9,647 participants (44.0% men, mean age: 48.6 ± 9.5 years) consisting of 7,348 participants with AGA entered the analyses. Higher DAI was associated with 10% lower AGA odds, while higher E-DII showed 4% higher AGA odds after adjusting for various confounding variables. However, significant associations were found only among women, and adjusting for metabolic syndrome (MetS) made the E-DII-AGA association insignificant.ConclusionAntioxidant-rich diets protect against AGA, while pro-inflammatory diets increase the risk, likely through developing MetS. Patient nutrition is frequently overlooked in clinical practice, yet it plays a crucial role, especially for women genetically predisposed to androgenetic alopecia. Dietary changes, such as reducing pro-inflammatory foods (like trans and saturated fats) and increasing anti-inflammatory options (fruits and vegetables), can help prevent hair loss and mitigate its psychological impacts, ultimately lowering future treatment costs.

Abstract Objective To evaluate the frequency and significance of brain imaging findings in methanol poisoning patients and to propose a criterion for prioritizing brain imaging. Methods We retrospectively reviewed the data of 306 patients (286 men and 34 women, mean age 32.10 ± 9.9 years) with confirmed methanol poisoning who were admitted to two hospitals in Iran during the COVID‐19 pandemic. We analyzed their demographic, clinical, laboratory, and brain imaging data. Results The main brain computed tomography (CT) scan findings were hypodensity in the putamen (11.1%), cerebellar nuclei (8.2%), diffuse cerebral edema (7.5%), and intracranial hemorrhage (ICH; 1.6%). These findings were associated with blood pH, Glasgow Coma Scale (GCS), renal failure, bicarbonate, oxygen, carbon dioxide, potassium, and glucose levels (p