Mohammad-Amin Abdollahifar

Trigeminal neuralgia (TN) is a debilitating condition characterized by severe, episodic facial pain, often caused by neurovascular conflict (NVC) at the trigeminal nerve root. Early and accurate diagnosis of TN is crucial for effective management, with microvascular decompression (MVD) being a common surgical treatment. While MRI plays a key role in detecting NVC, its subjective nature limits diagnostic precision. Recent advances in artificial intelligence (AI) offer promising tools for enhancing diagnostic accuracy. This study evaluates the potential of deep learning (DL) and machine learning (ML) models in improving TN diagnosis through automated segmentation and NVC detection in MRI scans. A comprehensive search was conducted across PubMed, Scopus, Embase, Web of Science, and the Cochrane Library to identify studies utilizing AI models in TN diagnosis. Studies assessing sensitivity, specificity, accuracy, and AUC were included in the meta-analysis. A total of 5 studies met the inclusion criteria, encompassing 577 patients with confirmed TN. The primary focus was on evaluating the performance of AI models in segmenting the trigeminal nerve and identifying neurovascular conflicts. The reference standard or target criterion used to define correct model performance was also extracted and considered, including expert imaging-based annotation, clinical-radiological diagnosis, and intraoperative confirmation where available. The pooled sensitivity of AI models in detecting TN was 71% (95% CI: 65–76%), with a specificity of 92% (95% CI: 88–94%). The positive diagnostic likelihood ratio (DLR) was 8.5 (95% CI: 5.75–12.56), and the negative DLR was 0.32 (95% CI: 0.26–0.38). The diagnostic odds ratio (DOR) was 26.71 (95% CI: 16.38–43.56), and the area under the curve (AUC) reached 0.91 (95% CI: 0.88–0.93). These findings demonstrate the high diagnostic accuracy of AI models, particularly in identifying NVC and improving preoperative planning for TN patients. AI models, particularly DL-based approaches, show promising diagnostic performance in the segmentation of the trigeminal nerve and detection of NVC. The high specificity and diagnostic odds ratio suggest that AI can play a critical role in enhancing clinical decision-making and improving the accuracy of TN diagnosis, ultimately aiding in better treatment planning for patients.

This study aimed to evaluate the predictive value of inferior vena cava (IVC) reflux and the right ventricle to left ventricle ratio (RV/LV ratio) in risk stratification of acute pulmonary thromboembolism (APTE) patients. In this retrospective, cross-sectional study, eighty-six patients diagnosed with APTE underwent computed tomography pulmonary angiography (CTPA). The pulmonary arterial obstruction index (Qanadli score) was evaluated by an experienced radiologist, along with the RV/LV ratio and IVC reflux grading (1-6) based on CTPA. Correlations between the Qanadli score, IVC reflux grade, and RV/LV ratio were analyzed. Linear regression was performed after confirming regression assumptions. Additionally, IVC reflux grade and Qanadli score were compared between patients with RV/LV ratios below and above 0.9. The mean Qanadli score was 7.20 ± 8.04. A significant correlation was found between the Qanadli score and IVC reflux grade (r = 0.601, p < 0.001), whereas no significant correlation was observed between the Qanadli score and RV/LV ratio (r = 0.131, p = 0.230). Further analysis demonstrated a significant linear association between the Qanadli score and IVC reflux grade, with a regression coefficient of 3.98 ± 0.55 (p < 0.001) and an adjusted R² of 0.38. However, patients with an RV/LV ratio > 0.9 did not show a significant difference in IVC reflux grade (p = 0.176) or Qanadli score (p = 0.439). Our study highlights IVC reflux as a potential valuable imaging marker for assessing APTE severity, while also pointing out the limitations of relying solely on the RV/LV ratio for risk stratification.

Objective: Tramadol is a centrally acting analgesic with a modest opioid effect similar to morphine and codeine, but less potent; it is mainly prescribed for the treatment of mild to moderate pain. Tramadol exhibits both opioid and non-opioid properties, primarily affecting the central nervous system. Accordingly, the present study was designed to investigate the effects of tramadol on female reproductive function and ovarian toxicity, as well as to examine oocyte survival and follicular development in mice exposed to tramadol.Methods: Mice were treated with tramadol at 50 mg/kg daily for 3 weeks. Blood levels of the hormones estrogen and progesterone were measured. The ovaries of the mice were subjected to histological, immunohistochemical, and molecular studies.Results: Our results revealed that tramadol provoked ovarian atrophy by inducing oxidative stress, while also decreasing oocyte survival and impairing follicular development.Conclusion: Although further research is necessary, the findings indicate that tramadol could reduce fertility in female mice.

Background: Gadolinium-based contrast agents (GBCAs) have recognized limitations for accurate delineation of brain tumor margins and perfusion assessment in neuro-oncology. Nanoparticle contrast agents (NP-CAs), particularly ultrasmall superparamagnetic iron oxides (USPIOs), may overcome these limitations by providing delayed uptake and tissue characterization. Methods: We systematically searched PubMed, Embase, Scopus, Web of Science, and trial registries on July 2025 for human neuro-oncology studies using NP-CAs. The primary outcome was the change in relative cerebral blood volume (ΔrCBV) compared with that of GBCA imaging at prespecified time points. The secondary outcomes were the contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), reader-rated margin delineation, and safety. Two reviewers independently extracted the data and assessed the risk of bias via tools from the Joanna Briggs Institute. Random effects meta-analysis was performed when ≥3 comparable datasets were available; otherwise, the results were synthesized narratively. Results: Six studies met the inclusion criteria. Agents included ferumoxtran-10, ferumoxytol, and ferumoxides, with intravenous doses ranging from 0.56–7.00 mg Fe/kg. The pooled common-effect mean dose was 4.65 mg/kg. Across heterogeneous designs, NP-CAs consistently enhanced margin delineation and perfusion metrics: ferumoxtran-10 produced sharp, persistent T2/T2* rims beyond T1-GBCA enhancement, and ferumoxytol-based DSC (Dynamic Susceptibility Contrast) yielded a higher rCBV with reduced leakage effects. No serious adverse events were reported; infusion reactions were rare and inconsistently defined. Conclusions: Compared with GBCA, NP-CAs, particularly USPIOs, improve brain tumor margin visualization and perfusion assessment. However, methodological heterogeneity and small sample sizes limit certainty. Standardized protocols for dosing, acquisition, and safety monitoring, alongside biopsy-validated prospective trials, are needed before clinical adoption.

Study design: Systematic Review. Objective: To describe applications of AI for traumatic SCI management with focus on diagnostics, prognostication, and therapeutic interventions. Methods: PubMed, Scopus and Cochrane libraries were searched (March 2025). Studies published in English between January 1 ^st , 2020, and March 18, 2025, dealing with clinical aspects in the acute, post-injury rehabilitative and first year phases of SCI were included. Studies on brain computer interface, robotics and non-neurologic aspects of SCI were excluded. Extracted were country of study, study design, focus of study, total participants, American Spinal Injury Association (ASIA) Impairment Scale (AIS), machine learning (ML) models, inputs, outcomes and performance metrices. Results: A total of 23 studies with 120,931 individuals were identified. Classical Machine Learning Models, Ensemble Learning Models and Deep Learning Models were the most used ML families. Age, AIS, neurologic level of injury, sex, mechanism of injury and motor score were the most common inputs. Predictions of neurologic status, functionality status, Hospital/ICU utilizations, complications, survival, discharge destination and results of image segmentation and patient grouping were the outputs of interest. The performance metrices were satisfactory in most and higher than humans in some studies. Conclusion: AI can facilitate personalized approach to diagnosis of SCI, prediction of outcomes like neurological improvement, complications, functionality indicators like walking, selfcare and independence, re-admissions, prolonged length of stays, discharge destination and mortality after injury. It was also useful to suggest specific MAP goals and time of surgical intervention. These functions complement clinical judgement.

Diabetes mellitus (DM) is a global health issue that affects many people around the world. The use of antioxidant and anti-inflammatory agents such as elderberry (EB) can have beneficial effects on the CNS following DM. In this research, streptozotocin was administered to rats at a dosage of 45 mg/kg to induce diabetes mellitus. The rats were treated with a 2% EB diet for 30 days. Behavioral assessments were conducted using Y-maze, elevated plus maze, open field, Morris water maze, and tail suspension tests. Electrophysiological recordings were also performed. Immunohistochemistry against GFAP as an astrocyte marker and Voronoi tessellation for neuronal spatial arrangement was done. We also performed a western blot against BDNF and REST. Our results showed that EB reduced blood sugar following DM induction. On the other hand, the body weight of rats in the DM and EB groups decreased compared to the control group. However, treatment with the EB diet improved memory and learning and reduced anxiety and depression following DM. The data from the electrophysiological recording revealed that the EB diet increased long-term potentiation (LTP) more than the DM group. Our results also showed that the EB diet reduced astrogliosis and reduced astrocyte reactivity. On the other hand, the EB diet improved the spatial arrangement of neurons in the hippocampus. Moreover, EB increased the expression of BDNF and decreased the expression of REST in the hippocampus of DM rats. EB prevents cognitive impairment and hippocampal damage following DM induction. However, in future studies, we need to clarify the mechanism by which EB ameliorate DM effects on the memory by focusing on REST/ BDNF changes and hippocampal LTP related neuronal circuits.

Introduction: Coronavirus spread rapidly around the world, posing a major challenge to medical science for controlling and treating its complications. It is well known that this disease can have devastating effects on various body systems, including the male reproductive system. This study aimed to evaluate the impact of COVID-19 on male ...