The prevalence rates of freezing of gait (FOG) in Parkinson’s disease (PD) vary widely, ranging from 14.0 to 55.1%. Our aim is to calculate the overall prevalence of FOG in all PD patients with different disease durations and severities. Using Medline/PubMed/Embase, we carried out a systematic literature search for studies reporting the PD and clinically relevant FOG. After primary screening, a total of 35 studies were identified and further analyzed for inclusion into the analysis, and 29 studies fulfilled the quality criteria and included in this meta-analysis. The overall prevalence of FOG in PD was 39.9% (95% CI 35.3-44.5%). The FOG identified by the freezing of gait questionnaire item 3 may be more prevalent (43.8%, 95% CI 38.5-49.1%) than the FOG identified by the Unified Parkinson’s Disease Rating Scale item 14 (36.0%, 95% CI 29.0-43.1%). Disease duration and severity are both the clinical features associated with the FOG. The highest FOG prevalence rate in PD patients was seen in patients with disease durations ≥ 10 years, at 70.8%, followed that of PD patients with disease durations ≥ 5 years (53.3%), and PD patients with disease durations < 5 years (22.4%). FOG presented in 28.4% of PD patients with Hoehn and Yahr staging (H&Y) score ≤ 2.5, and in 68.4% of PD patients with H&Y score ≥ 2.5. This meta-analysis confirms that the prevalence of FOG in PD is considerable, and highlights the need for accurate identification of FOG in PD.

The mechanisms underlying lipid metabolic disorders in Parkinson's diseases (PD) remain unclear. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify PD-related modular genes and differentially expressed genes (DEGs). Lipid metabolism-related genes (LMRGs) were extracted from Molecular Signatures Database. Candidate genes were assessed with overlapping modular genes, DEGs, and LMRGs for the purpose of building protein–protein interaction (PPI) networks. Then, biomarkers were generated by machine learning and Backpropagation Neural Network development according to candidate genes. Biomarker-based enrichment and network modulation analyses were executed to investigate related signaling pathways. Following dimensionality reduction clustering and annotation, scRNA-seq was submitted to cellular interactions and trajectory analysis to analyze regulatory mechanisms of critical cells. Finally, qRT-PCR was conducted to confirm the expression of biomarkers in PD patients. Four biomarkers (MSMO1, ELOVL6, AACS, and CERS2) were obtained and highly predictive after analysis mentioned above. Then, OPC, Oli, and Neu cells were the primary expression sites for biomarkers according to scRNA-seq studies. Finally, we confirmed mRNA of MSMO1, ELOVL6 and AACS were downregulated in PD patients comparing with control, while CERS2 was upregulated. In conclusion, MSMO1, ELOVL6, AACS, and CERS2 related to LMRGs could be new biomarkers for diagnosing and treating PD.

Background: Early hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth.Methods: We used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration 6 mL or a hematoma volume increase of 33% compared with the baseline NCCT.Results: A total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %.Conclusion: The CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.

Background: Hematoma expansion (HE) is a significant predictor of poor outcomes in patients with intracerebral hemorrhage (ICH). Non-contrast computed tomography (NCCT) markers in ICH are promising predictors of HE. We aimed to determine the association of the NCCT markers with HE by using different temporal HE definitions. Methods: We utilized Risa-MIS-ICH trial data (risk stratification and minimally invasive surgery in acute intracerebral hemorrhage). We defined four HE types based on the time to baseline CT (BCT) and the time to follow-up CT (FCT). Hematoma volume was measured by software with a semi-automatic edge detection tool. HE was defined as a follow-up CT hematoma volume increase of >6 mL or a 33% hematoma volume increase relative to the baseline CT. Multivariable regression analyses were used to determine the HE parameters. The prediction potential of indicators for HE was evaluated using receiver-operating characteristic analysis. Results: The study enrolled 158 patients in total. The time to baseline CT was independently associated with HE in one type (odds ratio (OR) 0.234, 95% confidence interval (CI) 0.077–0.712, p = 0.011), and the blend sign was independently associated with HE in two types (OR, 6.203–6.985, both p < 0.05). Heterogeneous density was independently associated with HE in all types (OR, 6.465–88.445, all p < 0.05) and was the optimal type for prediction, with an area under the curve of 0.674 (p = 0.004), a sensitivity of 38.9%, and specificity of 96.0%. Conclusion: In specific subtypes, the time to baseline CT, blend sign, and heterogeneous density were independently associated with HE. The association between NCCT markers and HE is influenced by the temporal definition of HE. Heterogeneous density is a stable and robust predictor of HE in different subtypes of hematoma expansion.

Emerging evidence has shown a strong correlation between serum TAG levels, the inflammatory response and Parkinson’s disease (PD) onset. However, the causal relationship between TAG levels and PD has not been well established. We aimed to investigate the relationship between serum TAG levels and risk of PD and explore the potential mediating role of circulating immune cells and inflammatory proteins. We utilised genotype data from the GeneRISK cohort, and summary data from genome-wide association studies investigating PD, circulating immune cells, inflammatory proteins and plasma lipidomes. Using Mendelian randomisation (MR) and multivariate MR (MVMR) analysis, we further adjusted for phosphatidylcholine (17:0_18:1) and TAG (58:7). Our results suggested a robust causal link between higher serum TAG (51:4) levels and a decreased risk of PD, with 1 sd genetically instrumented higher serum TAG (51:4) level leading to a 21 per cent (95 % CI 0·66, 0·96) reduction in the risk of PD (P= 0·015). Additionally, the results of the mediation analysis suggested a possible role for mediation through circulating immune cells (including IgD-CD38-B cells and resting CD4 regulatory T cells), but not circulating inflammatory proteins, in the causal relationship between the plasma lipidomes and PD. Our study confirms a causal relationship between higher serum TAG (51:4) levels and a lower risk of PD and clarifies a possible role for mediation through circulating immune cells, but not inflammatory proteins. These findings indicate that serum TAG (51:4) regulates immunity to effectively lower the risk of PD.