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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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Jia, Yang, Li, Chen, Wu, Li, Wang, Wang, Liu and Wu.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: To establish and validate a predictive formula for calculating the possibility of developing delayed neurological sequelae (DNS) after acute carbon monoxide (CO) poisoning to facilitate better decision-making about treatment strategies., Methods: This study retrospectively enrolled 605 consecutive patients who had been newly diagnosed with CO poisoning from the Central Hospital of Enshi Prefecture between January 1, 2015 and December 31, 2020. The cohort was randomly divided into two subgroups: the development cohort (n = 104) and validation cohort (n = 44). Univariate analysis and backward elimination of multivariate logistic regression were used to identify predictive factors, and a predictive formula was established. The performance was assessed using the area under the curve (AUC), the mean AUC of five-fold cross-validation, and calibration plots., Results: The formula included four commonly available predictors: initial GCS score, duration of exposure, CK, and abnormal findings on MRI. We next created a formula to calculate the risk score for developing DNS: Risk score = -4.54 + 3.35 * (Abnormal findings on MRI = yes) - 0.51 * (Initial GCS score) + 0.65 * (Duration of exposure) + 0.01 * (CK). Then, the probability of developing DNS could be calculated: Probability of DNS = 1/(1 + e Risk score ). The model revealed good discrimination with AUC, and mean AUC of fivefold cross-validation in two cohort, and the calibration plots showed good calibration., Conclusions: This study established a prediction predictive formula for predicting developing of DNS, which could facilitate better decision-making about treatment strategies., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2021. Published by Elsevier Inc.)

Background: The diagnosis and treatment of lung cancer necessitate a variety of supportive care needs. To our knowledge, no studies have been conducted that target specifically the supportive care needs of patients with lung cancer in Mainland China. Cross-cultural studies indicate that supportive care needs vary by cultural background. Thus, it is necessary to investigate the supportive care needs of patients with lung cancer in the cultural context of China., Purpose: This study aimed to describe the level of supportive care required by patients with lung cancer in China and to examine the relationships between supportive care needs and demographic factors and between supportive care needs and treatment variables., Methods: A cross-sectional descriptive study design was adopted. Five hundred fifty-four patients with lung cancer were recruited using a convenience sampling method from inpatient departments in four tertiary teaching hospitals that are affiliated with a medical university in Anhui Province, China. The Nursing Professional Social Support Needs Scale and background information list were used as the data collection instruments. A Wilcoxon rank sum test and a Kruskal-Wallis rank sum test were conducted to examine the differences among the professional supportive care needs of patients of different demographic characteristics and under different treatment conditions., Results: Participants self-reported the highest scores in the domain of informational needs (M = 3.67, interquartile range = 1.25). The most common supportive care need was "to be cared for by nurses with skilled venipuncture techniques." There were significant differences in needs across different genders, age groups, educational levels, and income levels (p < .05). Patients with metastasis and other illnesses had greater supportive care needs in terms of total and subscale scores in Stages III and IV (p < .05)., Conclusions: Patients with serious diseases and heavy socioeconomic burdens have greater supportive care needs. Therefore, healthcare providers should improve their awareness and expertise to identify the needs of their patients and to provide supportive care to patients with lung cancer. In addition, patients with high supportive care needs should be identified.

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., (© 2019 Nordic Association for the Publication of BCPT (former Nordic Pharmacological Society).)