Pain characteristics of Parkinson's disease using validated Arabic versions of the King's Parkinson's Disease Pain Scale and Questionnaire: A Multicenter Egyptian Study.

Objective: Pain is one of the most common non-motor symptoms in Parkinson's disease (PD), with variable characteristics among populations. This multicenter Egyptian study aimed to translate and validate the King's Parkinson's Disease Pain Scale (KPPS) and questionnaire (KPPQ) into Arabic versions and to investigate the pain characteristics in Egyptian people with PD (PWP). Methods: 192 PWP and 100 sex and age-matched controls were evaluated by KPPS-Arabic and KPPQ-Arabic. Both tools were assessed for test-retest reliability, floor or ceiling effects, construct validity and convert validity. PWP were assessed also by MDS-UPDRS, Hoehn and Yahr, NMSS, PD Questionnaire-39, and the Non-Motor Fluctuation Assessment (NoMoFA). Results: KPPS-Arabic and KPPQ-Arabic showed inter and intra-rater consistency and high validity, with an acceptable ceiling effect. 188 PWP (97.9%) reported at least 1 type of pain, (p<0.001). The severity and prevalence of KPPS-Arabic domains were significantly higher in all pain domains among PWP compared to controls (p < 0.001). Fluctuation-related and musculoskeletal pains were the most common (81.3% and 80.7%, respectively). In the PD group, the total and domains of KPPS-Arabic were significantly correlated to the MDS-UPDRS total, parts I, II, III, PIGD, axial, and H &Y scores, but not age or age of onset. Predictors of KPPS-Arabic included the total MDS-UPDRS, part III-Off, disease duration, total NMSS, and NoMoFA. Conclusion: The current multicentre study provided a validated Arabic versions of KPPS and KPPQ, with high reliability and validity, and demonstrated a high prevalence and severity of pain within Egyptian PWP and characterized its determinants.

The effect of lacosamide on calcitonin gene-related peptide serum level in episodic migraine patients: a randomized, controlled trial.

BACKGROUND: Migraine affects 11-15% of people worldwide, and the calcitonin gene-related peptide (CGRP) is released during the migraine attack, producing pulsating pain of migraine. Also, lacosamide reacts with collapsin-response mediator protein 2, preventing its phosphorylation and leading to the inhibition of CGRP release in the trigeminal system. OBJECTIVE: The primary outcome was the difference in the serum level of CGRP-LI after three months of treatment with either lacosamide and ibuprofen or ibuprofen alone in episodic migraine patients. The secondary outcomes were assessing safety and efficacy of lacosamide in episodic migraine patients. METHODS: We conducted an open-label randomized controlled trial on episodic migraine patients aged 10-55 years diagnosed according to (ICHD-3) in Kafr El-Sheikh University Hospital, Egypt. We assessed serum levels of CGRP-LI before and three months after treatment in our two groups, the lacosamide, and the control groups. We also assessed the side effects of treatment in each group, the percentage of patients who achieved ≥ 50% reduction in the migraine monthly days (MMD) frequency and the percentage of patients who achieved pain freedom within 2 h in ≥ 4 of 5 attacks in each group. RESULTS: 200 episodic migraine patients completed the study. There was a statistically significantly higher reduction in the serum CGRP-LI level in the lacosamide group compared with the control group. In addition, lacosamide was well tolerated by patients. Also, the lacosamide group had statistically significant higher percentage of patients who achieved ≥ 50% reduction in the migraine monthly days (MMD) frequency and pain freedom within two hours in ≥ 4 of 5 attacks with P-values 0.002, 0.02 respectively. CONCLUSION: The daily use of lacosamide 50 mg Bid for three months in episodic migraine patients was associated with a significant reduction in serum CGRP-LI, better clinical outcomes regarding frequency and duration of migraine attacks, and was well tolerated by patients. These results were derived from an open-label pilot study that needed to be thoroughly investigated by a large-scale, randomized, double-blinded, placebo-controlled study. TRIAL REGISTRATION:  We registered our trial on ClinicalTrials.gov, named after "The Lacosamide's Effect on Calcitonin Gene-related Peptide in Migraine Patients," and with a clinical trial number (NCT05632133)-August 8, 2023.

Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome.

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.

Relevance of HLA-DP/DQ and INF-λ4 Polymorphisms to COVID-19 Outcomes.

Background: Single nucleotide polymorphisms provide information on individuals' potential reactions to environmental factors, infections, diseases, as well as various therapies. A study on SNPs that influence SARS-CoV-2 susceptibility and severity may provide a predictive tool for COVID-19 outcomes and improve the customized coronavirus treatment. Aim: To evaluate the role of human leukocyte antigens DP/DQ and IFNλ4 polymorphisms on COVID-19 outcomes among Egyptian patients. Participants and Methods: The study involved 80 patients with severe COVID-19, 80 patients with mild COVID-19, and 80 non-infected healthy volunteers. Genotyping and allelic discrimination of HLA-DPrs3077 (G/A), HLA-DQrs7453920 (A/G), and IFNλ4 rs73555604 (C/T) SNPs were performed using real-time PCR. Results: Ages were 47.9 ± 8, 44.1 ± 12.1, and 45.8 ± 10 years in severe, mild and non-infected persons. There was a statistically significant association between severe COVID-19 and male gender (p = 0.002). A statistically significant increase in the frequency of HLA-DPrs3077G, HLA-DQrs7453920A, and IFNλ4rs73555604C alleles among severe COVID-19 patients when compared with other groups (p < 0.001). Coexistence of these alleles in the same individual increases the susceptibility to severe COVID-19 by many folds (p < 0.001). Univariate and multivariate logistic regression analysis for the studied parameters showed that old age, male gender, non-vaccination, HLA-DQ rs7453920AG+AA, HLA-DPrs3077GA+GG, and IFNλ4rs73555604CT+CC genotypes are independent risk factors for severe COVID-19 among Egyptian patients. Conclusion: HLA-DQ rs7453920A, HLA-DPrs3077G, and IFNλ4rs73555604C alleles could be used as markers of COVID-19 severity.

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.

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.

Cochlear implants: Visual evoked potentials study.

Cochlear implants (CIs) are a successful alternative in cases with severe-to-profound HL. In these individuals, visual cross-modal re-organization can occur because of hearing loss where the visual cortex will recruit auditory cortical areas for visual processing. OBJECTIVES: This work is designed to study visual evoked potentials (VEPs) in children fitted with CIs in comparison to normal hearing children. METHOD: This work included 2 groups of children: Group I included 20 normal hearing children and study group included 25 children fitted with unilateral CIs. All cases were subjected to Thorough otological history. Check up on CIs performance using physical check and Aided sound field examination, ophthalmic examination and Pattern Visual Evoked Potentials (pVEPs). RESULTS: Both groups showed no significant difference as regard age or sex. And both had normal ophthalmic examinations. Children of the study groups showed satisfactory aided response. As regard pVEPs, the study group showed significant higher P100 amplitude in comparison to the control group. CONCLUSION: This study showed that deafness could induced cortical organization in the visual cortex and not limited to the auditory cortex only.