Study Design:Systematic review and meta-analysis of preclinical studies. Purpose:To evaluate the efficacy of biomaterial scaffolds incorporating neurotrophic growth factors in promoting locomotor recovery after traumatic spinal cord injury (SCI). Overview of Literature:SCI results in severe neurological deficits and limited regenerative capacity. Biomaterial scaffolds provide structural support and, when combined with neurotrophic growth factors, may enhance axonal regeneration and functional recovery. However, despite numerous preclinical studies, the pooled efficacy of these strategies has not been comprehensively quantified. Methods:Comprehensive searches of Medline, Embase, Scopus, and Web of Science were conducted through May 2025. Two independent researchers conducted the screening process and extracted data as per the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Eligible studies assessed functional recovery in rodent SCI models treated with scaffolds containing neurotrophic factors. A random-effects meta-analysis was performed to calculate standardized mean differences (SMDs) and 95% confidence intervals (95% CI). Risk of bias (ROB) was evaluated using SYRCLE's ROB tool. The certainty of evidence was reported using the GRADE framework. Results:Analysis of 56 articles revealed that brain-derived neurotrophic factor (BDNF) (SMD, 3.35; 95% CI, 1.71-5.00; p <0.001), nerve growth factor (NGF) (SMD, 4.51; 95% CI, 1.57-7.45; p <0.001), neurotrophin-3 (NT-3) (SMD, 2.57; 95% CI, 1.29-3.84; p <0.001), basic fibroblast growth factor (bFGF) (SMD, 3.23; 95% CI, 1.84-4.62; p <0.001), and insulin-like growth factor 1 (IGF-1) (SMD, 2.39; 95% CI, 1.35-3.43; p <0.001) were associated with significant improvements in locomotion. NGF demonstrated sex-specific differences favoring females (p =0.026), while bFGF was most effective in transection models (SMD, 4.60; p <0.001). Both bioactive and non-bioactive scaffolds effectively delivered BDNF and NGF. Conclusions:Growth factor-loaded scaffolds, especially those incorporating BDNF, NGF, and NT-3, demonstrate substantial potential for the treatment of SCI. These findings highlight the significance of selecting appropriate scaffold materials and delivery strategies tailored to specific injury types.

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality worldwide. Genetics factors play a significant role in the development of CVDs, which may result from monogenic or polygenic influences. These genetic contributions can lead to various life-threatening conditions such as cardiomyopathies, aortopathies, dyslipidemias, and arrhythmias, many of which are associated with sudden cardiac death (SCD) in adults. Early genetic screening and diagnosis enable timely intervention, not only for affected individuals but also for at-risk family members. Advances in molecular genetics and pharmacogenomic are transforming the management of cardiovascular diseases. By identifying genetic variants that influence drug metabolism, clinicians can tailor drug selection and dosing to individual patients, paving the way for personalized treatment strategies. This review will explore the role of genetics and pharmacogenetics in cardiovascular medicine, with a focus on how genetic insights inform risk stratification and guide therapy. In particular, the review will examine the pharmacogenetic considerations in the use of anticoagulant, antiplatelet agents, lipid-lowering therapies, direct-acting vasodilators, antiarrhythmics, renin-angiotensin system inhibitors, and diuretics ultimately supporting the implementation of personalized clinical interventions to achieve optimal patient care outcomes.

Cardiovascular diseases remain the leading cause of mortality worldwide, with elderly patients experiencing the worst prognosis following ST-elevation myocardial infarction (STEMI). Traditional risk stratification models demonstrate suboptimal performance in geriatric patients due to complex risk profiles involving frailty and multiple comorbidities. To develop machine learning-based predictive models for one-year major adverse cardiovascular events (MACE) in elderly patients (≥65 years) undergoing percutaneous coronary intervention (PCI) for STEMI. This retrospective cohort study analyzed 1,358 elderly patients who underwent PCI between 2015 and 2021. MACE included cardiovascular death, myocardial infarction, stroke, and revascularization within one year. Eight machine learning algorithms were evaluated: XGradient Boosting (XGB), Random Forest (RF), Logistic Regression, Neural Networks, Support Vector Machines, K-Nearest Neighbors, Decision Trees, and Naive Bayes. The Synthetic Minority Oversampling Technique (SMOTE) was applied to address class imbalance. Model performance was evaluated using various metrics, and SHAP (Shapley Additive Explanations) was used to enhance model interpretability and support clinical decision-making by identifying key risk factors driving predictions. Among the patients (mean age 74.1 ± 6.7 years, 31.8

An Umbrella Review and Meta-analysis. This umbrella review and meta-analysis aims to evaluate the efficacy of stem cell therapy for locomotion recovery and neuropathic pain alleviation in rodent models of spinal cord injury (SCI). A comprehensive literature search was conducted in Medline, Embase, Scopus, and Web of Science until May 2024 to identify systematic reviews/meta-analyses on stem cell therapy for SCI. Original studies from these reviews were screened based on the predefined inclusion criteria. Data on locomotion, thermal hyperalgesia, and mechanical allodynia were extracted. Standardized mean differences (SMD) with 95

Background: Cardiac involvement in sarcoidosis is associated with high mortality but is often underrecognized due to diagnostic challenges. Advanced imaging modalities like Cardiac Magnetic Resonance (CMR) and Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) are highly sensitive for detecting myocardial inflammation and scarring, aiding in the diagnosis and management of cardiac sarcoidosis. The objective of this study was to characterize the imaging features of cardiac sarcoidosis in Iranian patients using these advanced cardiac imaging modalities. Materials and Methods: This multicenter prospective study included 42 Iranian patients with biopsy-proven extracardiac sarcoidosis who met the Japanese Circulation Society criteria for cardiac involvement. All patients underwent CMR to evaluate myocardial function, edema, scarring, and strain. 28 patients also underwent FDG-PET/CT to assess active myocardial inflammation. Results: In our study of 42 cardiac sarcoidosis patients (50% male, mean age 47.14±14.33 years), CMR revealed reduced left ventricular ejection fraction (34.2±13.7%) in 83.3% of patients, with late gadolinium enhancement (LGE) present in 88.1%. LGE was most frequent in the basal anteroseptal and mid inferoseptal/anteroseptal segments, with midwall (35.5%) and subepicardial (23.7%) patterns predominating. Global strain analysis showed impaired values: longitudinal -10.08±4.14%, radial 15.38±8.55%, and circumferential -10.79±4.63%. Mean T1 and T2 values were 1054.60±50.98 ms and 52.59±4.53 ms, respectively. FDG-PET demonstrated active disease in 67.9% of cases, predominantly involving the apical septum, basal inferolateral, and mid inferolateral segments. The left anterior descending artery territory showed the highest involvement in both active inflammation (44.3% of affected segments) and scarring (39.2% of affected segments). Conclusion: CMR and FDG-PET provided comprehensive assessment of cardiac involvement in this Iranian cardiac sarcoidosis cohort, with predominant basal and lateral wall involvement. Regional differences highlight the importance of population-specific studies.

BACKGROUND AND AIMS:Non-alcoholic fatty liver disease (NAFLD) and metabolic-associated fatty liver disease (MAFLD) affect over 30 % of the global population. Extrahepatic manifestations, including colorectal malignancies, represent the second leading cause of death in these patients. This study evaluates the association between NAFLD/MAFLD and colorectal polyps, neoplasia, and cancer. METHODS:A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar through June 2025. Observational studies reporting odds ratios (ORs) or hazard ratios for colorectal pathologies in NAFLD/MAFLD patients were included. Quality assessment was performed using Joanna Briggs Institute tools. Random-effects meta-analysis calculated pooled effect sizes with subgroup analyses to explore heterogeneity. RESULTS:Forty-eight studies encompassing 56,175,279 participants were analyzed. NAFLD/MAFLD was associated with significantly increased risk of colorectal polyps (OR 1.86, 95 % CI: 1.51-2.30), adenomas (OR 1.81, 95 % CI: 1.62-2.02), CRC (OR 1.37, 95 % CI: 1.30-1.45), overall neoplasia (OR 1.50, 95 % CI: 1.24-1.81), hyperplastic polyps (OR 1.60, 95 % CI: 1.34-1.91), and multiple adenomas (OR 1.49, 95 % CI: 1.16-1.91). Associations were confirmed in both imaging-based and biopsy-proven studies, with adjusted analyses supporting these findings. However, no significant association was found with advanced or large adenomas. Lean patients (BMI <25 kg/m²) showed stronger associations with adenoma risk than those with BMI ≥25 kg/m² (p = 0.027). Sensitivity analyses confirmed the robustness of the results. CONCLUSION:NAFLD/MAFLD significantly increases colorectal polyp risk, particularly adenomas, hyperplastic polyps, overall neoplasia, and CRC, emphasizing the need for targeted colorectal screening. Future research should focus on prospective studies and mechanistic insights to enhance preventive strategies.

To evaluate metformin’s efficacy in locomotion recovery, alleviating neuropathic pain, and modulating underlying molecular mechanisms in Spinal Cord Injury (SCI) rodent models through a systematic review and meta-analysis. We conducted a comprehensive literature search across Medline, Embase, Scopus, and Web of Science from inception to May 2024. We included studies that utilized rodent models of traumatic SCI treated with metformin versus untreated controls. Data on locomotor recovery, neuropathic pain, and molecular mechanisms related to secondary injury were extracted. Standardized mean differences (SMDs) were synthesized as the pooled effect sizes. Twenty-three studies comprising 1,567 animals met the inclusion criteria. Metformin significantly enhanced locomotor function (SMD = 2.23, 95

Introduction: Traumatic Brain Injury (TBI) is one of the leading causes of mortality and severe disability worldwide. This study aimed to develop and optimize machine learning (ML) algorithms to predict abnormal brain computed tomography (CT) scans in patients with mild TBI. Methods: In this retrospective analyses, the outcome was dichotomized into normal or abnormal CT scans, and univariate analyses were employed for feature selection. Then SMOTE was applied to address class imbalance. The dataset was split 80:20 for training/testing, and multiple ML algorithms were evaluated using accuracy, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). SHAP analysis was used to interpret feature contributions. Results: The data included 424 patients with an average age of 40.3 +/- 19.1 years (76.65% male). Abnormal brain CT scan findings were more common in older males, patients with lower Glasgow Coma Scale (GCS) scores, suspected fractures, hematomas, and visible injuries above the clavicle. Among the ML models, XGBoost performed best (AUC 0.9611, accuracy 0.8937), followed by Random Forest, while Naive Bayes showed high recall but poor specificity. SHAP analysis highlighted that lower GCS scores, decreased SpO2 levels, and tachypnea were strong predictors of abnormal brain CT findings. Conclusion: XGBoost and Random Forest achieved high predictive accuracy, sensitivity, and specificity. GCS, SpO2, and respiratory rate were key predictors. These models may reduce unnecessary CT scans and optimize resource use. Further multicenter validation is needed to confirm their clinical utility.