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: 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.
Objective: The aim of this study was to detect changes in white matter in patients with Parkinson's disease applied by diffusion tensor imaging to predict cognitive impairment. Methods: Montreal cognitive assessment was applied to 50 Parkinson's disease patients to confirm cognitive decline (M: F = 41:9; age: 62.72±9.07 years) and to 20 Parkinson's disease patients with no cognitive impairment as a control (M: F =13:7; age 58.95±11.22). All patients underwent disease severity testing by using Modified Hoehn and Yahr Scale, Unified Parkinson disease rating scale and Diffusion tensor imaging (DTI) for the corpus callosum and cingulum including their involved parts to define affected tracts. Results: In PD with cognitive impairment subjects, the cognitive affection correlated with abnormal DTI parameters of the corpus callosum and cingulum. There were FA or MD differences in both the corpus callosum and cingulum pathways. These findings were independent of age, sex and total white matter volume. Conclusion: Patients with Parkinson's disease associated with cognition decline are detected by tractography changes of the corpus callosum and cingulum.
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