Disrupted Brain Structural Network Connection in de novo Parkinson's Disease With Rapid Eye Movement Sleep Behavior Disorder

ObjectiveTo explore alterations in white matter network topology in de novo Parkinson's disease (PD) patients with rapid eye movement sleep behavior disorder (RBD).Materials and MethodsThis study included 171 de novo PD patients and 73 healthy controls (HC) recruited from the Parkinson's Progression Markers Initiative (PPMI) database. The patients were divided into two groups, PD with probable RBD (PD-pRBD, n = 74) and PD without probable RBD (PD-npRBD, N = 97), according to the RBD screening questionnaire (RBDSQ). Individual structural network of brain was constructed based on deterministic fiber tracking and analyses were performed using graph theory. Differences in global and nodal topological properties were analyzed among the three groups. After that, post hoc analyses were performed to explore further differences. Finally, correlations between significant different properties and RBDSQ scores were analyzed in PD-pRBD group.ResultsAll three groups presented small-world organization. PD-pRBD patients exhibited diminished global efficiency and increased shortest path length compared with PD-npRBD patients and HCs. In nodal property analyses, compared with HCs, the brain regions of the PD-pRBD group with changed nodal efficiency (Ne) were widely distributed mainly in neocortical and paralimbic regions. While compared with PD-npRBD group, only increased Ne in right insula, left middle frontal gyrus, and decreased Ne in left temporal pole were discovered. In addition, significant correlations between Ne in related brain regions and RDBSQ scores were detected in PD-pRBD patients.ConclusionsPD-pRBD patients showed disrupted topological organization of white matter in the whole brain. The altered Ne of right insula, left temporal pole and left middle frontal gyrus may play a key role in the pathogenesis of PD-RBD

Prevalence of Work-Related Musculoskeletal Disorders in the Nurses Working in Hospitals of Xinjiang Uygur Autonomous Region

Objective. To investigate the status of work-related musculoskeletal disorders (WMSDs) in nurses working in the hospitals in Xinjiang Uygur Autonomous Region. Methods. The prevalence of WMSDs since working and in the previous 12 months was evaluated using self-administrated modified musculoskeletal questionnaire based on North European questionnaire. In this cross-sectional study, 6674 nurses involved in the nursing profession were selected from 16 hospitals using the stratified cluster sampling method. Results. The most commonly affected regions by WMSDs were lower back, neck, shoulder, and back, with an annual prevalence of 62.71%, 59.77%, 49.66%, and 39.50%, respectively. Statistical differences were noticed in the annual prevalence of WMSDs in those with different ages (P40 hrs per week; poor health status; and feeling of fatigue. Rest time of >10 min and no history of WMSDs were the protective factors of WMSDs. Conclusions. Shift and working/rest duration was closely related to WMSDs

Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data

Abstract Background Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model. Methods A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison. Results During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838–0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively). Conclusions Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice

Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson’s disease

Background: Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease. Objectives: To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods. Methods: We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance. Results: A total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62–0.68; ACC range: 0.63–0.77; SEN range: 0.45–0.66; SPE range: 0.64–0.84). Models trained with structural connectivity (AUC range, 0.81–0.84; ACC range, 0.75–0.86; SEN range, 0.77–0.91; SPE range, 0.71–0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81–0.85; ACC range, 0.74–0.85; SEN range, 0.79–0.91; SPE range, 0.70–0.89). Conclusions: Baseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients

Additional file 1 of Accurate prediction of HCC risk after SVR in patients with hepatitis C cirrhosis based on longitudinal data

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