Early identification of Parkinson's disease with anxiety based on combined clinical and MRI features.

Objective: To identify cortical and subcortical volume, thickness and cortical area features and the networks they constituted related to anxiety in Parkinson's disease (PD) using structural magnetic resonance imaging (sMRI), and to integrate multimodal features based on machine learning to identify PD-related anxiety. Methods: A total of 219 patients with PD were retrospectively enrolled in the study. 291 sMRI features including cortical volume, subcortical volume, cortical thickness, and cortical area, as well as 17 clinical features, were extracted. Graph theory analysis was used to explore structural networks. A support vector machine (SVM) combination model, which used both sMRI and clinical features to identify participants with PD-related anxiety, was developed and evaluated. The performance of SVM models were evaluated. The mean impact value (MIV) of the feature importance evaluation algorithm was used to rank the relative importance of sMRI features and clinical features within the model. Results: 17 significant sMRI variables associated with PD-related anxiety was used to build a brain structural network. And seven sMRI and 5 clinical features with statistically significant differences were incorporated into the SVM model. The comprehensive model achieved higher performance than clinical features or sMRI features did alone, with an accuracy of 0.88, a precision of 0.86, a sensitivity of 0.81, an F1-Score of 0.83, a macro-average of 0.85, a weighted-average of 0.92, an AUC of 0.88, and a result of 10-fold cross-validation of 0.91 in test set. The sMRI feature right medialorbitofrontal thickness had the highest impact on the prediction model. Conclusion: We identified the brain structural features and networks related to anxiety in PD, and developed and internally validated a comprehensive model with multimodal features in identifying.

Early prediction of drug-resistant epilepsy using clinical and EEG features based on convolutional neural network.

OBJECTIVE: Machine learning utilization in electroencephalogram (EEG) analysis and epilepsy care is fast evolving. Thus, we aim to develop and validate two one-dimensional convolutional neural network (CNN) algorithms for predicting drug-resistant epilepsy (DRE) in patients with newly-diagnosed epilepsy based on EEG and clinical features. METHODS: We included a total of 1010 EEG signal epochs and 15 clinical features from 101 patients with epilepsy. Each patient had 10 epochs of EEG signal data, with each signal recorded for 90 s. The ratio of development set and validation set was 80:20, and ten-fold cross validation was performed. First, a CNN algorithm was used to extract EEG features automatically. Then, Two one-dimensional CNNs were crafted.. Accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, mean square error (MSE) and area under the curve (AUC) were calculated to evaluate the classifiers performance. RESULTS: The clinical-EEG model showed good performance and clinical practical value, with the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.72, 0.82, 0.96, 0.89, 0.83, 32.00, 0.81, respectively, and the accuracy in validation set was 0.84. In the EEG model, the accuracy, specificity, precision, sensitivity, F1-score, kappa statistics, best MSE and AUC in test set were 0.99, 0.59, 0.82, 0.90, 0.86, 0.72, 181.76, 0.76, respectively, and the accuracy in validation set was 0.81. CONCLUSION: We constructed a clinical-EEG model showed good potential for predicting DRE in patients with newly-diagnosed epilepsy, which could help identify patients at high risk of developing DRE at earlier stages.

Involvement of p38 mitogen-activated protein kinase in altered expressions of AQP1 and AQP4 after carbon monoxide poisoning in rat astrocytes.

Cerebral oedema is a major pathological change of acute carbon monoxide (CO) poisoning, the pathogenesis of which is still unclear. In the aquaporin (AQP) water channel family, AQP1 and AQP4 play critical roles in the progress of cerebral oedema of various neuropathological events. However, their functions in CO poisoning have not been demonstrated. In this study, we investigated the expressions of AQPs and associated mechanisms of brain injury in an acute CO poisoning rat model. Compared with the control injected intraperitoneally with equal volume of air, the dry weight/wet weight (DW/WW) ratio of brain water content, levels of AQP1, AQP4, phosph-p38 mitogen-activated protein kinase (p-p38 MAPK) and astrocyte marker, glial fibrillary acidic protein (GFAP) in the frontal cortex and hippocampal CA1 of acute CO poisoning group significantly increased at 6, 12, 24 hours after CO injection. Intracerebroventricular injection with a p38 MAPK inhibitor, SB203580 (200 µmol/L/kg/d), before CO injection reduced water content in the brain tissues and significantly decreased levels of AQP1, AQP4, p-p38 MAPK and GFAP. Therefore, our study showed that the overexpressions of AQP1 and AQP4 were involved in the development of CO poisoning-induced cerebral oedema, which could be attenuated by inhibition of p-p38 MAPK signalling. Manipulation of AQPs and p38 MAPK may be a new therapeutic strategy for acute CO poisoning.

Monitoring and Treatment of Acute Kidney Injury in Children with Acute Lymphoblastic Leukemia After High Dose Methotrexate Chemotherapy.

To investigate acute kidney injury (AKI) in children with acute lymphoblastic leukemia (ALL) who received high dose methotrexate (MTX) chemotherapy and explore the corresponding treatment. Methods 180 children who received high dose MTX chemotherapy were observed with serum MTX concentration and serum creatinine. Patients with AKI of stage 3 or poor response to conventional treatment were performed on hemodialysis and assessed the treatment outcome. Results 9 patients (5%) have appeared AKI, including 7 cases of AKI of stage 3. However, there were not any significant correlation between age, gender, serum MTX concentration and AKI, respectively. Compared with normal serum MTX concentration, the patients with high serum MTX concentration easily were developed to AKI, the MTX and serum creatinine concentration had been significantly decreased in 9 patients after hemodialysis. Conclusion AKI has appeared in some children with ALL who receive high dose MTX chemotherapy, and this may due to increase of serum MTX concentration. The monitoring of serum MTX concentration and AKI index could help to find out AKI, and even to prevent the occurrence of it. Furthermore, once AKI is present, those patients with AKI stage 3 or poor response to conventional treatment should be performed on hemodialysis treatment.