Personalized Prediction of Parkinson's Disease Progression Based on Deep Gaussian Processes.

Parkinson's disease is a chronic progressive neurodegenerative disease with highly heterogeneous symptoms and progression. It is helpful for patient management to establish a personalized model that integrates heterogeneous interpretation methods to predict disease progression. In the study, we propose a novel approach based on a multi-task learning framework to divide Parkinson's disease progression modeling into an unsupervised clustering task and a disease progression prediction task. On the one hand, the method can cluster patients with different progression trajectories and discover new progression patterns of Parkinson's disease. On the other hand, the discovery of new progression patterns helps to predict the future progression of Parkinson's disease markers more accurately through parameter sharing among multiple tasks. We discovered three different Parkinson's disease progression patterns and achieved better prediction performance (MAE=5.015, RMSE=7.284, r2=0.727) than previously proposed methods on Parkinson's Progression Markers Initiative datasets, which is a longitudinal cohort study with newly diagnosed Parkinson's disease.

Histological tumor micronecrosis in resected specimens after R0 hepatectomy for hepatocellular carcinomas is a factor in determining adjuvant TACE: A retrospective propensity score-matched study.

BACKGROUND: Tumor micronecrosis is a less investigated pathological feature of hepatocellular carcinoma (HCC). This study was aimed at evaluating the value of micronecrosis for guiding adjuvant transcatheter arterial chemoembolization (TACE) in HCC management. METHODS: We retrospectively reviewed the data of patients with HCC who underwent curative liver resection in our center from 2014 to 2018. The patients were divided into micronecrosis (+) and micronecrosis (-) groups. In each group, overall survival (OS) and disease-free survival (DFS) were compared between patients who underwent adjuvant TACE and those who did not. Propensity score matching (PSM) was conducted at a ratio of 1:1 to control selection bias. Univariate and multivariate analyses were performed to determine independent prognostic factors. Mass cytometry was applied to compare the immunological status of HCCs between the two groups. RESULTS: A total of 897 patients were included, with 417 and 480 patients in the micronecrosis (+) and micronecrosis (-) groups, respectively. No significant difference was detected in baseline parameters after PSM. In the micronecrosis (+) group, patients who underwent adjuvant TACE had significant longer OS than did those who did not (P = 0.004). However, patients in the micronecrosis (-) group did not benefit from adjuvant TACE. Although adjuvant TACE prolonged the DFS of patients with severe micronecrosis (P = 0.034), it may adversely affect the DFS of patients without micronecrosis (P = 0.131). Multivariate analysis showed that TACE was an independent prognostic factor for patients with micronecrosis but not for those without micronecrosis. The abundance of exhausted and regulatory T cells was significantly higher in patients with micronecrosis. CONCLUSIONS: For HCC patients with micronecrosis, adjuvant TACE after curative resection could improve the prognosis, while its survival benefits were limited in HCC patients without micronecrosis. TACE should be selectively performed in patients with micronecrosis, especially those with an Nscore = 2. The immunosuppressive status of HCC patients with micronecrosis may explain the effectiveness of adjuvant TACE in such scinario.

Motor Progression in Early-Stage Parkinson's Disease: A Clinical Prediction Model and the Role of Cerebrospinal Fluid Biomarkers.

Background: The substantial heterogeneity of clinical symptoms and lack of reliable progression markers in Parkinson's disease (PD) present a major challenge in predicting accurate progression and prognoses. Increasing evidence indicates that each component of the neurovascular unit (NVU) and blood-brain barrier (BBB) disruption may take part in many neurodegenerative diseases. Since some portions of CSF are eliminated along the neurovascular unit and across the BBB, disturbing the pathways may result in changes of these substances. Methods: Four hundred seventy-four participants from the Parkinson's Progression Markers Initiative (PPMI) study (NCT01141023) were included in the study. Thirty-six initial features, including general information, brief clinical characteristics and the current year's classical scale scores, were used to build five regression models to predict PD motor progression represented by the coming year's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III score after redundancy removal and recursive feature elimination (RFE)-based feature selection. Then, a threshold range was added to the predicted value for more convenient model application. Finally, we evaluated the CSF and blood biomarkers' influence on the disease progression model. Results: Eight hundred forty-nine cases were included in the study. The adjusted R2 values of three different categories of regression model, linear, Bayesian and ensemble, all reached 0.75. Models of the same category shared similar feature combinations. The common features selected among the categories were the MDS-UPDRS Part III score, Montreal Cognitive Assessment (MOCA) and Rapid Eye Movement Sleep Behavior Disorder Questionnaire (RBDSQ) score. It can be seen more intuitively that the model can achieve certain prediction effect through threshold range. Biomarkers had no significant impact on the progression model within the data in the study. Conclusions: By using machine learning and routinely gathered assessments from the current year, we developed multiple dynamic models to predict the following year's motor progression in the early stage of PD. These methods will allow clinicians to tailor medical management to the individual and identify at-risk patients for future clinical trials examining disease-modifying therapies.