Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients’ health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman’s correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disease with a heterogeneous nature. Many molecular pathways are engaged in PD pathogenesis. Granzyme B (GrB) is an enzyme released by CTLs and has roles in neuroinflammation, axonal degeneration, demyelination, and neuronal ischemic death. However, little is reported about its role in PD pathogenesis and deterioration. To evaluate the role of GrB in PD. Method: A total of 94 participants were recruited from outpatient neurology clinics (47 PD patients and 47 healthy controls). Serum GrB levels were measured using ELISA. Results: Among PD patients, the age of onset was 57.30 ± 4.92 years, and the duration of illness was 7.13 ± 4.20 years. Sociodemographic data revealed statistically significant associations between the development of PD and HCV infection (0.001*), family history of neuropsychiatric illness (0.004*), and/or PD (0.028*). MoCA TS showed a significant reduction in cognitive performance among PD patients even after correction (p < 0.001*). H_Y score showed that >50% of PD patients were clustered in stages 1 and 2, and motor scores showed a substantial reduction. GrB levels were markedly increased among PD patients compared with the control group (1721.4 ± 588.8 pg/mL vs. 418.4 ± 131.3 pg/mL) (p < 0.001*). This indicates a strong association between elevated granzyme B and PD. However, no statistically significant correlations were observed between GrB levels and the parameters studied. Conclusion: GrB levels are significantly elevated among PD patients. This reflects underlying immune activation or inflammatory processes associated with the progression of PD. GrB showed a promising diagnostic association with PD, but was not correlated with disease severity, activity, or duration in this cohort. Formal evaluation of GrB’s diagnostic accuracy in an independent, disease-control-inclusive cohort is warranted before it can be considered a diagnostic biomarker.

Background Erectile Dysfunction (ED) is a highly prevalent non-motor symptom in Parkinson’s Disease (PD), yet its vascular and hormonal factors require objective clarification. This study aimed to systematically assess ED prevalence, its association with clinical severity, cognition and quality of life (QoL), and its correlation with objective hormonal and penile hemodynamic parameters in male PD patients. Methods This cross-sectional study included 74 male PD patients. ED was quantified using the International Index of Erectile Function- 5 items (IIEF-5). Disease severity was measured by the Movement Disorder Society - Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) and Hoehn and Yahr (H&Y) staging. QoL was assessed using the PDQ-39 (Parkinson’s Disease Questionnaire-39 items). Cognition was assessed using the MoCA (Montreal Cognitive Assessment). Correlations were analyzed with hormonal parameters (total/free testosterone, prolactin) and penile duplex ultrasound metrics. Results A total of 74 men suffering from PD (average age of 61 ± 8 years). ED seems to be highly prevalent, with 90.5% reporting some degree of dysfunction (mean IIEF-5: 13 ± 5). Correlations with erectile function are seen in hormonal components and duplex penile parameters, excluding prolactin and total testosterone. Older age and age at disease onset also significantly correlated with greater ED severity (p < 0.001). There was a correlation of levodopa use with diminished erectile function and lower free testosterone (all p < 0.05). There was a negative correlation between ED and quality of life. A significant association was also found between ED severity and cognitive impairment. There are negative correlations between IIEF-5 scores and UPDRS subscales and the Hoehn & Yahr stage; these scores reflect the overall burden of both motor and non-motor disease on sexual function. Conclusion ED occurs with considerable frequency in men with PD, exhibiting robust correlations with motor severity, non-motor symptom burden, hormonal dysregulation, vascular dysfunction, and diminished health-related quality of life. Contributory effects are magnified in individuals receiving levodopa therapy and in patients with later-onset disease. Collectively, these observations substantiate the heterogeneous pathophysiology of ED in the Parkinson’s cohort and accentuate the imperative for systematic sexual health screening and integrative therapeutic intervention in clinical care.

Background: Migraine is manifested by recurrent headache attacks, with variable characteristics and associated symptoms. Oral beta blockers are widely used to prevent and reduce the frequency these attacks. However, the use of topical beta blockers for acute treatment is still controversial. Aims: In this randomized double – armed study, we aim to compare the efficacy of Timolol 0.5% eyedrops compared to other over-the-counter (OTC) medications used for acute treatment of migraine. Methods: Fifty-seven patients were randomly allocated to group A (treatment group: Timolol 0.5% eyedrops), and group B (control group: OTC drugs including Non-steroidal anti-inflammatory drugs (NSAIDs), caffeine, aspirin, and acetaminophen). The primary outcome was pain reduction. Secondary outcome measures included reducing nausea, vomiting, photophobia, and phonophobia. Results: Median pain reduction for the OTC group was higher than for timolol (6, 4 prospectively) with a significant U-test (p

Background: Migraine and epilepsy are common neurological disorders that share several pathophysiological mechanisms, including cortical hyperexcitability. Migraine is more frequent in patients with epilepsy than in the general population. This study aimed to determine the prevalence of interictal migraine among adults with epilepsy and evaluate its association with epilepsy characteristics and demographic factors. Results: This cross-sectional study included 100 adult patients with epilepsy attending the neurology outpatient clinic at Kafrelsheikh university hospitals. Clinical evaluation, Electroencephalogram (EEG), brain MRI, and migraine assessment according to ICHD-3 criteria were performed. Only interictal migraine was included. Migraine was identified in 25% of patients, with migraine without aura being the most common subtype (76%). Migraine was significantly associated with female sex, earlier age at epilepsy onset, higher seizure frequency, longer epilepsy duration, drug-resistant epilepsy, multiple antiepileptic drug use, and focal epileptiform EEG abnormalities (all p < 0.05). Logistic regression identified female sex, earlier age at epilepsy onset, prolonged epilepsy duration, and drug-resistant epilepsy as independent predictors of migraine. Conclusion: Interictal migraine is a frequent comorbidity in epilepsy, particularly among patients with more severe disease. Early recognition of migraine may improve the comprehensive management and quality of life of patients with epilepsy.