Clinical Characteristics and Gene Mutation Analysis of Poststroke Epilepsy.

Epilepsy is one of the most common brain disorders worldwide. Poststroke epilepsy (PSE) affects functional retrieval after stroke and brings considerable social values. A stroke occurs when the blood circulation to the brain fails, causing speech difficulties, memory loss, and paralysis. An electroencephalogram (EEG) is a tool that may detect anomalies in brain electrical activity, including those induced by a stroke. Using EEG data to determine the electrical action in the brains of stroke patients is an effort to measure therapy. Hence in this paper, deep learning assisted gene mutation analysis (DL-GMA) was utilized for classifying poststroke epilepsy in patients. This study suggested a model categorizing poststroke patients based on EEG signals that utilized wavelet, long short-term memory (LSTM), and convolutional neural networks (CNN). Gene mutation analysis can help determine the cause of an individual's epilepsy, leading to an accurate diagnosis and the best probable medical management. The test outcomes show the viability of noninvasive approaches that quickly evaluate brain waves to monitor and detect daily stroke diseases. The simulation outcomes demonstrate that the proposed GL-GMA achieves a high accuracy ratio of 98.3%, a prediction ratio of 97.8%, a precision ratio of 96.5%, and a recall ratio of 95.6% and decreases the error rate 10.3% compared to other existing methods.

CXCL13 chemokine as a promising biomarker to diagnose neurosyphilis in HIV-negative patients.

BACKGROUND: Chemokine ligand 13 (CXCL13) is believed to play a role in the recruitment of B cells in the central nervous system during neuroinflammation. Neurosyphilis is a group of clinical syndromes of the central nervous system caused by Treponema pallidum (T. pallidum) infection. The relationship between CXCL13 and neurosyphilis still needs further study. In our study, CSF and serum CXCL13 concentrations were detected among 40 neurosyphilis patients, 31 syphilis/non-neurosyphilis patients, 26 non-syphilis/other central nervous system diseases patients. Serum CXCL13 concentrations were detected in 49 healthy persons. All enrolled persons were HIV-negative. Receiver operating characteristic (ROC) analysis was performed to determine the threshold value that could distinguish neurosyphilis from syphilis. RESULTS: We found that the CSF CXCL13 concentrations and CXCL13 quotient (QCXCL13) were significantly increased in neurosyphilis patients compared to syphilis/non-neurosyphilis (χ(2) = 21.802, P < 0.001) and non-syphilis patients (χ(2) = 7.677, P = 0.002). ROC curve analyses revealed that CSF CXCL13 concentrations and QCXCL13 could serve as valuable biomarkers for differentiating neurosyphilis from non-neurosyphilis/syphilis. CONCLUSIONS: The CSF CXCL13 and QCXCL13 could serve as valuable biomarkers for differentiating neurosyphilis from non-neurosyphilis/syphilis in HIV-negative patients.