Seyyed Kiarash Sadat Rafiei

Global and regional trends of population aging spotlight major public health concerns. As one of the most common adverse prognostic factors, advanced age is associated with a remarkable incidence risk of many non-communicable diseases, affecting major organ systems of the human body. Age-dependent factors and molecular processes can change the nervous system's normal function and lead to neurodegenerative disorders. Oxidative stress results from of a shift toward reactive oxygen species (ROS) production in the equilibrium between ROS generation and the antioxidant defense system. Oxidative stress and neuroinflammation caused by Amyloid-ß protein deposition in the human brain are the most likely pathogenesis of Alzheimer's disease (AD). Walnut extracts could reduce Amyloid-ß fibrillation and aggregation, indicating their beneficial effects on memory and cognition. Walnut can also improve movement disabilities in Parkinson's disease due to their antioxidant and neuroprotective effect by reducing ROS and nitric oxide (NO) generation and suppressing oxidative stress. It is noteworthy that Walnut compounds have potential antiproliferative effects on Glioblastoma (the most aggressive primary cerebral neoplasm). This effective therapeutic agent can stimulate apoptosis of glioma cells in response to oxidative stress, concurrent with preventing angiogenesis and migration of tumor cells, improving the quality of life and life expectancy of patients with glioblastoma. Antioxidant Phenolic compounds of the Walnut kernel could explain the significant anti-convulsion ability of Walnut to provide good prevention and treatment for epileptic seizures. Moreover, the anti-inflammatory effect of Walnut oil could be beneficial in treating multiple sclerosis. In this study, we review the pharmaceutical properties of Walnut in age-related neurological disorders.

Background: Sleep quality is defined as an individual’s consent to sleep experience. Poor sleep quality has important adverse health outcomes. There are drugs to treat sleep disorders but consumption of these drugs is accompanied by adverse effects whereas herbal treatments have fewer side effects. Saffron is spice obtained from Crocus sativus flower. Several articles have been done on its effects on the quality of sleep and its safety. This review for the first time critically evaluates effect of saffron on sleep quality improvement. Method: The search technique aims to get all related published data-based up to 2022 articles. PubMed, Central, Google Scholar, and Scopus were examined. Only full reports were evaluated (abstracts were excluded). The first screening was done by title and abstract. Then full text of articles was read and irrelevant articles were removed. Duplicate articles were also removed by Endnote. By using Cochrane risk of bias tool assessment, a quality score based on probability of bias was given. Methodological characteristics were also evaluated using the criteria of Stevinson and Ernst. Result: In the systematic review, 5 randomized clinical trials with 379 participants from 3 countries were identified. In placebo-comparison trials, saffron contains a large treatment. Conclusion: It seems that saffron has a beneficial influence on duration and quality of sleep. Saffron, crocin, and safranal induce hypnotic effects by increasing the duration of sleep. Research conducted so far provides initial support and safety for use of saffron to improve sleep quality.

BACKGROUND: In recent years, artificial intelligence and machine learning algorithms have been used more extensively to diagnose diabetic retinopathy and other diseases. Still, the effectiveness of these methods has not been thoroughly investigated. This study aimed to evaluate the performance and limitations of machine learning and deep learning algorithms in detecting diabetic retinopathy. METHODS: This study was conducted based on the PRISMA checklist. We searched online databases, including PubMed, Scopus, and Google Scholar, for relevant articles up to September 30, 2023. After the title, abstract, and full-text screening, data extraction and quality assessment were done for the included studies. Finally, a meta-analysis was performed. RESULTS: We included 76 studies with a total of 1,371,517 retinal images, of which 51 were used for meta-analysis. Our meta-analysis showed a significant sensitivity and specificity with a percentage of 90.54 (95%CI [90.42, 90.66], P < 0.001) and 78.33% (95%CI [78.21, 78.45], P < 0.001). However, the AUC (area under curvature) did not statistically differ across studies, but had a significant figure of 0.94 (95% CI [- 46.71, 48.60], P = 1). CONCLUSIONS: Although machine learning and deep learning algorithms can properly diagnose diabetic retinopathy, their discriminating capacity is limited. However, they could simplify the diagnosing process. Further studies are required to improve algorithms.

Background Accurate preoperative grading of meningiomas is crucial for selecting the most suitable treatment strategies and predicting patient outcomes. Traditional MRI-based assessments are often insufficient to distinguish between low- and high-grade meningiomas reliably. Deep learning (DL) models have emerged as promising tools for automated histopathological grading using imaging data. This systematic review and meta-analysis aimed to comprehensively evaluate the diagnostic performance of deep learning (DL) models for meningioma grading. Methods This study was conducted in accordance with the PRISMA-DTA guidelines and was prospectively registered on the Open Science Framework. A systematic search of PubMed, Scopus, and Web of Science was performed up to March 2025. Studies using DL models to classify meningiomas based on imaging data were included. A random-effects meta-analysis was used to pool sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). A bivariate random-effects model was used to fit the summary receiver operating characteristic (SROC) curve. Study quality was assessed using the Newcastle-Ottawa Scale, and publication bias was evaluated using Egger's test. Results Twenty-seven studies involving 13,130 patients were included. The pooled sensitivity was 92.31% (95% CI: 92.1–92.52%), specificity 95.3% (95% CI: 95.11–95.48%), and accuracy 97.97% (95% CI: 97.35–97.98%), with an AUC of 0.97 (95% CI: 0.96–0.98). The bivariate SROC curve demonstrated excellent diagnostic performance, characterized by a relatively narrow 95% confidence interval despite moderate to high heterogeneity (I 2 = 79.7%, p &amp;lt; 0.001). Conclusion DL models demonstrate high diagnostic accuracy for automatic meningioma grading and could serve as valuable clinical decision-support tools. Systematic review registration DOI: 10.17605/OSF.IO/RXEBM

BACKGROUND: Postoperative cognitive dysfunction (POCD) significantly affects recovery, hospitalization duration, and quality of life following cardiac surgery. Identifying reliable biomarkers for predicting POCD could improve patient outcomes and perioperative care. Among these, S100 calcium-binding protein beta (S100β) and neuron-specific enolase (NSE) have emerged as promising indicators of cerebral injury and neurocognitive dysfunction. OBJECTIVES: This systematic review and meta-analysis aimed to assess within-subject perioperative changes in S100β and NSE levels among patients who developed POCD after cardiac surgery, to evaluate whether these biomarkers consistently rise in association with POCD. METHODS: Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science up to October 2024. Studies included peer-reviewed articles evaluating S100β and NSE levels in relation to POCD in cardiac surgery patients. Two reviewers independently extracted data and assessed the quality using the ROBINS-I tool. Meta-analyses were conducted using a random-effects model. RESULTS: = 88.7%) for NSE, indicating large effect sizes. Sensitivity analyses confirmed the robustness of these findings despite substantial heterogeneity. CONCLUSIONS: Among patients who developed POCD, S100β and NSE levels significantly increased from preoperative to postoperative measurements, indicating a potential association with cerebral injury. However, as non-POCD patients were not analyzed for the same biomarker changes, causality or specificity to POCD cannot be confirmed and future research should be directed toward between group changes.