Background The prognostic significance of misfolded alpha-synuclein (alpha-Syn) aggregates in Parkinson's disease (PD) has not been well investigated. The aim of this study was to reveal the relationship between misfolded alpha-Syn aggregate concentration in cerebrospinal fluid (CSF) and cognitive decline risk in PD. Methods A total of 278 patients with PD were retrospectively included. They were diagnosed between 2011 and 2013. The end-point was 2016, and the follow-up period was 54.3 +/- 10.0 months. Cognitive decline was defined as a 4-point decrease in the Mini-Mental State Examination score during follow-up. Misfolded alpha-Syn aggregate concentration in baseline CSF was measured using the protein misfolding cyclic amplification (PMCA) technique. Time to reach 50% of the maximum fluorescence value was recorded. Results The PMCA technique successfully detected the level of misfolded alpha-Syn aggregates in CSF with a sensitivity of 85.3% and a specificity of 91.4%. The time to reach 50% of the maximum fluorescence value was shorter in the patients with cognitive decline than in the patients without cognitive decline (190.7 +/- 40.1 h vs. 240.8 +/- 45.6 h, P < 0.001). Multifactorial Cox regression analysis revealed that reaching 50% of the maximum fluorescence value in <= 219 h at baseline was associated with increased risk of cognitive decline during the follow-up (HR: 4.90, 95% CI: 2.75-8.74, P < 0.001). Conclusion Baseline concentration of misfolded alpha-Syn aggregates in CSF measured by the PMCA technique predicts risk of cognitive decline in PD.

As a high-cost neurological disability, spinal cord injury (SCI) can result in permanent paralysis and loss of sensation. To identify the temporal genes involved in the pathogenesis of SCI, we analysed the expression profile of GSE45006. GSE45006 was downloaded from Gene Expression Omnibus, including 20 SCI samples (samples at 1 day, 3 days, 1 week, 2 weeks and 8 weeks after injury, four repetitions for each time point) and 4 normal samples. The Bayesian Estimation of Temporal Regulation (BETR) and randomForest packages were used to screen the temporal genes and the top 100 temporal genes, respectively. Then, the gplots package and Pearson correlation analysis separately were used to perform hot map analysis and expression pattern clustering for the top 100 temporal genes. Using the clusterProfiler package and TargetMine tool, their potential functions were analysed by enrichment analyses. Moreover, interaction relationships between these temporal genes and pathways were investigated by pathway–gene crosslinking networks. In total, 1907 temporal genes were identified. The top 100 temporal genes were obtained and divided into six clusters. Most of the gene functions were enriched in biological process categories. ARG1 and NOS3 in cluster 4 were enriched in biological process of arginine catabolic process. TGFβ2, TGFβ3, ALDH2 and ALDH3A2 were correlated with numerous pathways in the pathway–gene crosslinking network. Pathways related to TGFβ2 and TGFβ3 were connected to pathways related to ARG1 and NOS3 via ARG1. Several temporal genes, including TGFβ2, TGFβ3, ALDH2, ALDH3A2, ARG1 and NOS3, might be involved in SCI.