The impact of flaxseed oil on lipid profiles, weight loss, and inflammatory markers in hemodialysis patients: A systematic review and meta-analysis of randomized controlled trials.

Studies investigating the effects of flaxseed oil on lipid profiles, weight loss, and inflammatory markers have produced inconsistent results. This systematic review and meta-analysis of randomized controlled trials (RCTs) aimed to explore the impact of flaxseed oil on these parameters in hemodialysis patients. The study protocol was registered online (PROSPERO number: CRD42023484076). The meta-analyses showed a significant decrease in triglyceride (TG) levels (WMD = -85.78 mg/dL, 95% CI: -155.24 to -16.32, I2 = 98.32%) and C-reactive protein (CRP) levels (WMD = -2.66 mg/L, 95% CI: -4.07 to -1.24, I2 = 92.26%) following consumption of flaxseed oil. Subgroup analyses revealed significant changes in LDL-C, HDL-C, and TC levels only in trials utilizing a dosage higher than 10 g per day and using ground flaxseed oil. Based on the results, flaxseed oil improves CRP and TG levels, and higher doses positively affect lipid profiles. However, it has no significant effect on anthropometric measures.

Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up.

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.

Comparing Quality of Life: Discectomy Surgery versus Epidural Corticosteroid Injection for Lumbar Disc Herniation.

BACKGROUND: Selecting an efficient treatment for patients with radiculopathy caused by lumbar disc herniation (LDH) unresponsive to conservative management remains a challenging task under investigation, yielding varying results. This study aims to compare the outcomes of the 2 most prevalent invasive treatments. METHODS: In this retrospective longitudinal study, we enrolled patients with confirmed refractory symptomatic LDH who sought treatment at Valiasr Hospital and underwent either discectomy surgery or epidural steroid injection (ESI) between 2019 and 2022. The outcome measures included pain intensity using the Visual Analog Scale (VAS) and quality of life (QoL) using the SF-36 questionnaire. RESULTS: A total of 202 individuals (112 in the discectomy group and 90 in the ESI group) consisting of 90 males and 112 females, with a mean age of 50.9 ± 13.5 years, underwent analysis. In the univariate analysis, QoL scores were significantly higher in the discectomy group (57.4 ± 2.4) compared to the ESI group (44.2 ± 2.4) (P < 0.01). Furthermore, the surgery group exhibited a significantly greater reduction in the Leg VAS score after treatment compared to the ESI group (mean difference: -2.59, 95% confidence interval (CI): -3.45 to -1.70, P < 0.01). After adjusting for the most important confounding variables using multiple linear regression analysis, the association between surgery and higher QoL scores remained statistically significant (Unstandardized Coefficients B = 7.65, 95% CI: 0.55, 14.76, P = 0.03). CONCLUSIONS: Our findings indicate that discectomy surgery has a more pronounced effect on patient outcomes and is a preferable treatment option for LDH patients.

Methanol poisoning during the COVID-19 pandemic in Iran: A retrospective cross-sectional study of clinical, laboratory, and brain imaging characteristics and outcomes.

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 < 0.05). Poor prognosis was related to blindness, opium addiction, chronic alcohol use, hyperglycemia, and abnormal CT scans (p < 0.001 for all). The most predictive brain imaging findings for poor prognosis were hypodensity in the cerebellar nuclei, diffuse cerebral edema, and ICH. Conclusion: Brain imaging can provide valuable information for the diagnosis and management of methanol poisoning patients. We suggest that patients with severe acidosis, low GCS, low pH, low oxygen saturation, and high glucose levels should undergo brain CT scan as a priority.