Mohammad Ali Sahraian

Multiple sclerosis (MS) prevalence has increased to medium-to-high levels in several countries of the Middle East and North Africa (MENA), while Epstein–Barr virus (EBV) exposure as one of the most consistently reported MS risk factors is common throughout the region. This systematic review and meta-analysis descriptively mapped published estimates of MS prevalence and EBV seroprevalence alongside each other in MENA countries. We searched MEDLINE, PubMed, Embase, Scopus, and Web of Science until 18 June 2022. Observational studies reporting MS prevalence or EBV seroprevalence among healthy MENA populations were eligible. For quantitative EBV synthesis, we restricted analyses to studies reporting VCA-IgG seroprevalence to improve comparability across studies. Studies reporting the prevalence of specific type of MS or those assessing EBV infection by IgA antibodies or non-serological tissues or without a control arm representing general population were excluded. The Joanna Briggs Institute (JBI) critical appraisal tools were used to determine the quality of the studies. The heterogeneity of studies was evaluated using I² statistics. Results of the fix-effects meta-analysis were presented using prevalence and 95% confidence intervals. Of 4,323 records identified, 131 studies were included. The pooled MS prevalence was 51.02 per 100,000 population, and the pooled EBV VCA-IgG seroprevalence was 75%. In time-stratified analyses, pooled MS prevalence increased from 12 per 100,000 in studies conducted up to 2000 to 58 per 100,000 in studies conducted from 2011 to 2022. Pooled EBV seroprevalence increased from 71 to 75% across the same periods. These findings suggest that the prevalence of both MS and EBV has increased over time, with a more pronounced increase observed for MS. Between-study heterogeneity was extreme for both outcomes (I² = 100%), reflecting substantial variation in geography, populations, study methods, diagnostic approaches, and study periods. Published MS prevalence estimates and EBV VCA-IgG seroprevalence estimates showed co-occurring uprising patterns in the available MENA literature. Based on a probable ecological correlation between the increasing prevalence of MS and EBV, the preventive measures against EBV infection may potentially yield favorable outcomes in terms of MS prevention. Future longitudinal, population-based studies with standardized MS ascertainment and age-specific EBV serology are needed.

Background: Quantitative susceptibility mapping (QSM) characterizes multiple sclerosis (MS) lesions through susceptibility changes related to iron and myelin. However, the prevalence, regional distribution, and relationship of QSM‐defined lesion subtypes with choroid plexus alterations remain unclear. Purpose: To characterize MS lesion subtypes using QSM and evaluate their prevalence, regional distribution, and associations with choroid plexus volume changes. Study Type: Prospective longitudinal study. Population: Forty‐six patients with MS (44 relapsing–remitting, 2 primary progressive; mean age, 37 ± 8 years). QSM data were available for lesion classification in 32 participants. Field Strength/Sequence: 1.5T MRI with 3D T2‐FLAIR, 3D T1‐MPRAGE, and multi‐echo gradient‐echo sequences for QSM. Assessment: Following whole‐brain segmentation, white matter was parcellated into periventricular, juxtacortical, and deep regions. White matter lesions were segmented using the SAMSEG algorithm and classified on QSM as isointense, hypointense, hyperintense, or paramagnetic rim lesions. Lesion counts, lesion volumes, regional distribution, and associations with choroid plexus volume changes were evaluated. Statistical Tests: Normality was assessed using the Kolmogorov–Smirnov test. Spearman correlation and Mann–Whitney U tests were used. Bonferroni correction was applied for multiple comparisons. Results: Eight hundred ninety lesions were identified: 512 isointense (57.1%), 65 hypointense (7.2%), 291 hyperintense (32.4%), and 29 paramagnetic rim lesions (3.2%). Isointense and hypointense lesions, potentially reflecting different stages of tissue repair, accounted for 64.3% of all lesions. Juxtacortical white matter was most frequently affected (43.4%), followed by subcortical (40.4%) and periventricular (16.2%) regions. Hyperintense lesion volume correlated significantly with baseline lesion volume ( r = 0.71), follow‐up lesion volume ( r = 0.81), and total lesion volume ( r = 0.87). Total lesion volume was significantly associated with bilateral choroid plexus volume changes ( r = 0.515–0.595). Data Conclusion: QSM has the potential to characterize MS lesion heterogeneity by identifying distinct lesion susceptibility patterns. Total lesion volume was associated with choroid plexus volume changes. Evidence Level: 2. Technical Efficacy: Stage 2.

Background and Objective: Reliable medical research depends on data integrity, yet clinical datasets often contain real and systematically missing values. This study aimed to develop a robust, clinically realistic imputation framework for a raw Multiple Sclerosis (MS) dataset affected by real-world missingness. Methods: We propose an innovative data-driven approach called the Sequential Multiple Imputation Bootstrapping (SMIB) model, which orders imputation based on feature correlation and incorporates bootstrapping to enhance stability and generalizability. Relevant features were identified using RF importance and Mutual Information scores and imputed using a hybrid machine learning framework that combines Random Forest (RF), Multilayer Perceptron (MLP), k-Nearest Neighbors (kNN), and a multiple imputation (MI) algorithm. The proposed method was evaluated using 15-fold cross-validation and a masking-based evaluation strategy. Model performance was assessed using accuracy, precision, recall, specificity, F1 score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and R². Results: RF-based SMIB achieved superior performance, with final imputation accuracy reaching up to 97% for categorical outcomes and strong numerical performance (R² up to 0.999; MSE as low as 2.48 × 10⁻⁵). Sequential ordering and weighted bootstrapping improved stability in imbalanced clinical data under real-world missingness. Compared with the widely adopted Multiple Imputation by Chained Equations (MICE) approach, the proposed SMIB framework consistently demonstrated superior predictive performance across all evaluated categorical and numerical outcomes. Conclusions: The SMIB framework provides a robust and clinically aligned strategy for handling real-world missing values in MS datasets, improving imputation accuracy while preserving feature dependencies and feature relationships. The method supports reliable predictive analytics in healthcare contexts.

Multiple sclerosis (MS) is a chronic, immune-mediated demyelinating disease of the central nervous system whose heterogeneous clinical, radiological, and biological course has long resisted precise individual-level prediction. The recent convergence of large longitudinal datasets, advanced computational methods, and increasingly informative biomarkers has created conditions in which artificial intelligence (AI) and machine learning (ML) can begin to address that problem substantively. This review surveys the current evidence for AI/ML applications across the MS care continuum, with particular focus on the literature from 2022 through early 2026. Nine domains are examined: automated MRI lesion segmentation and quantification, fluid biomarker interpretation, unsupervised disease subtyping, disability progression prediction, treatment response stratification, drug repurposing and molecular discovery, digital biomarker monitoring, mechanistic interpretability, and integrated clinical management protocols. Notable recent contributions include the SuStaIn-based identification of two biologically distinct MS trajectories distinguished by early versus late serum neurofilament light chain elevation, the MindGlide deep learning platform enabling longitudinal analysis of archived routine clinical MRI data, the T-cell morphological classifier predicting natalizumab treatment response before drug initiation, and the fenebrutinib Phase III program that produced the first Bruton’s tyrosine kinase inhibitor results meeting primary endpoints in both relapsing and primary progressive MS. A proposed AI-Enhanced Management Protocol (AMP-26) reflecting 2026 clinical standards is included as an appendix. Throughout, emphasis is placed on mechanistic interpretability: the distinction between models that correlate features with outcomes and models whose decision logic reflects established MS pathobiology is considered a prerequisite for clinical credibility and regulatory readiness.

The role of diet in multiple sclerosis (MS) development is still a matter of debate and its impact on MS course is not well understood. We tried to investigate the possible role of adolescent diet in late-onset multiple sclerosis (LOMS) odds. LOMS patients were obtained from the National MS Registry of Iran. Controls were sex and age matched with no history of neurological disorders. We assessed dietary factors using a questionnaire based on multinational studies. Food consumption levels were classified into low, medium, and high for each item. Logistic regression models were used to evaluate diet's impact on LOMS odds. We included 83 LOMS cases and 207 matched healthy controls. The mean age for LOMS patients was 61.14, compared to 61.51 years for controls. The results showed that higher consumption of dairy as in the third tertile associated with 79% decline in LOMS odds (AOR: 0.21; 95%CI: 0.09-0.47). Higher seafood consumption, in the third tertile was associated with a decrease in the odds of LOMS (AOR: 0.32; 95%CI: 0.14-0.72). Furthermore, increased nut consumption, in the third tertile (AOR: 0.37; 95%CI: 0.18-0.77), decreased the odds of LOMS. Additionally, higher consumption of fruits (AOR: 0.22; 95%CI: 0.07-0.63) and vegetables (AOR: 0.26; 95%CI: 0.12-0.55) was linked to a reduced odds of LOMS. This research highlights the advantageous impact of dairy products, seafood, nuts, fruits, and vegetables in lowering the LOMS odds. Hence, advocating for nutrition role in development of LOMS could represent a preventive measure for people susceptible to MS.

Epstein–Barr virus (EBV) is a significant global health concern due to its association with malignancies and autoimmune diseases like multiple sclerosis. Rapid, accessible detection methods are essential, especially in resource‐limited settings. Although polymerase chain reaction (PCR) remains the gold standard, its high cost and complexity underscore the need for simpler alternatives. This study presents a visual, user‐friendly biosensor using gold nanoparticles (AuNPs) for point‐of‐care detection of EBV DNA. A specific DNA probe was synthesized and conjugated to ∼30 nm spherical AuNPs via thiol linkages. Target EBV DNA induced nanoparticle aggregation, and subsequent HCl addition caused a distinct color change from red to blue, enabling visual detection. The biosensor achieved a detection limit of 200 pmol and produced results within 5 min. Nanoparticle uniformity and stability were confirmed through characterization. Compared to PCR, the biosensor demonstrated comparable specificity in cross‐validation experiments, while offering superior speed, cost‐effectiveness, and operational simplicity. This AuNP‐based biosensor represents a promising, low‐cost diagnostic tool for rapid EBV screening, particularly in low‐resource environments. Further validation with diverse clinical samples is recommended to enhance reliability and support broader application.

Background and Aims: Suicidal ideation (SI), attempts, and death have been frequently described and investigated in chronic neurological disorders such as Multiple Sclerosis (MS). This systematic review and meta‐analysis estimated the global prevalence of SI in patients with MS and demonstrated associated risk factors. This may help the physician to reduce the rate of suicidal attempts and improve public health. Methods: A comprehensive systematic search was performed on PubMed, Scopus, Embase, and Web of Science to find studies that reported the prevalence of SI or its related factors in MS patients. Studies were screened by the title/abstract and then by their full text. Data extraction was performed for relevant studies. A point prevalence with a 95% confidence interval was estimated. Random‐effect meta‐analysis was conducted to assess the pooled prevalence of SI. Results: From the 1246 total studies that were found, we included 18 final eligible studies for systematic review and 16 studies for meta‐analysis. The prevalence of SI in multiple sclerosis patients was estimated at 20% (95% CI [15–25]), based on random‐effect meta‐analysis. Among the included studies, higher rates of depression and anxiety, lower family and social support, longer duration of illness, and higher levels of disability were related risk factors for SI in patients with MS. Conclusion: Given the higher prevalence of SI in people with MS than in the general population, it should be considered during routine clinical visits and psychological evaluations. Proper counseling, psychological support, and psychiatric interventions may reduce the lifetime risks and help patients overcome their suicidal thoughts.