BMC Neurology最新文献

Background: Prolonged disorders of consciousness (pDOC) pose significant clinical and societal challenges. Evaluating the prognosis of patients with pDOC is of a great concern for clinicians. This derivation study aimed to establish a nomogram based on mismatch negativity (MMN) to predict the recovery of consciousness in patients with pDOC.

Methods: This is a single-center retrospective derivation study. From September 2021 to June 2023, demographic and clinical information and MMN results of patients with pDOC were collected. The prognosis of patients who were admitted to the hospital for 6 months was assessed using the Glasgow Outcome Scale, categorized as "unfavorable prognosis" and "favorable prognosis." In this study, Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were used to select the most relevant predictors and create the nomogram. Receiver Operating Characteristic (ROC), calibration curves and Decision Curves Analysis (DCA) in the nomogram explained the predictive efficacy and clinical utility of the patients.

Results: 101 patients with pDOC were included, with 57 and 44 having unfavorable and favorable prognose. Univariate and LASSO regression analysis indicated that the MMN amplitude at Fz, Coma Recovery Scale-Revised scores, disease duration, and multiple intensive care unit admissions were the independent factors that were used to develop the exploration nomogram. ROC curves, calibration curves, and DCA showed good predictive power (area under the ROC curve: 0.821), with DCA suggested potential net benefit within clinically relevant decision-probability ranges.

Conclusion: In this study, an exploratory nomogram developed based on MMN predicted the 6-month outcome of patients with pDOC. This exploratory, internally validated model may support prognostic assessment.

Background: Despite advancements in multimodal therapy, glioma remains a lethal brain tumor with limited prognostic biomarkers. Programmed cell death (PCD) pathways and long non-coding RNAs (lncRNAs) are emerging as therapeutic targets, but their combined prognostic potential in glioma remains underexplored.

Methods: RNA sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts were analyzed to identify PCD-related lncRNAs. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression and multivariate Cox proportional hazards regression analyses were applied to construct a prognostic signature, with external validation performed in an independent cohort. Tumor immune microenvironment characterization was subsequently conducted through the Tumor IMmune Estimation Resource (TIMER) database using the inverse fold product algorithm, which quantified six tumor-infiltrating immune cells (TIIC) subtypes across 10,897 pan-cancer samples. Immune checkpoint differential expression between risk strata was assessed via single-sample gene set enrichment analysis (ssGSEA). Drug sensitivity prediction was performed using the Cancer Genome Project (GCP) databases.

Results: A 4-lncRNA signature effectively stratified patients into high-risk and low-risk groups. The prognosis of the high-risk group is worse. In multivariate analysis, a risk score can independently predict the prognosis and has a robust prediction efficiency in the CGGA external verification set. The infiltration of immunosuppressive cells, such as M2 macrophages, increased the expression of immune checkpoints, such as PD-L1, increased, and the infiltration of CD8 + T cells decreased in high-risk patients. Computational screening identified elesclomol as a potential therapeutic agent showing subtype-specific efficacy.

Conclusions: This study highlights the prognostic significance of lncRNAs associated with PCD in glioma and provides computational evidence for their potential as therapeutic targets. While the results suggest novel avenues for treatment development, they must be interpreted cautiously due to the lack of experimental validation. Future studies should aim to validate these results through controlled clinical trials and explore underlying molecular mechanisms.

Background: Alzheimer's disease (AD) was a progressive neurodegenerative disorder characterised by an insidious onset and gradual cognitive decline. It remained a significant global health challenge. Methylparaben (MEP), a preservative commonly used in cosmetics and food processing, had been associated with the development and progression of AD.

Methods: First, we acquired the initial three-dimensional (3D) structure of MEP from PubChem (CID: 7456), followed by structural optimization via energy minimization using Chem3D software to complete its 3D structural characterisation. This was followed by systematic target prediction across the SwissTargetPrediction, SEA, GeneCards and OMIM databases. We then constructed protein-protein interaction (PPI) networks using STRING and visualised them in Cytoscape to identify core targets. Molecular docking simulations using CB-Dock2 elucidated the binding affinities between MEP and the key proteins. Experimental validation combined Gene Expression Omnibus (GEO) database analysis with quantitative reverse transcription polymerase chain reaction (qRT-PCR) to quantify transcriptional changes in SK-N-SH neural cells.

Results: A total of 153 potential targets associated with MEP and AD were identified. Ten core targets were determined through screening using the STRING platform and Cytoscape software, including HIF1A, IGF1R, PDGFRB, PTK2, VCAM1, CXCL12, ERBB2, ESR1, JAK2 and BCL2L1. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the core MEP targets in AD primarily concentrate on the following key signalling pathways: Neuroactive ligand-receptor interactions, EGFR tyrosine kinase inhibitor resistance, HIF-1 signalling pathway and gamma-aminobutyric acid (GABA) synapse. Molecular docking simulations using CB-Dock2 confirmed a high binding affinity between MEP and these core targets. To investigate the mechanism of action of MEP, we validated the findings using clinical datasets and the human neuroblastoma cell line SK-N-SH. Upregulation of ten transcriptional expressions was observed, suggesting that MEP might influence cognitive function in patients with AD.

Conclusion: This study elucidated the potential molecular mechanisms of MEP in the progression of Alzheimer's disease-related tau pathology, offering new insights for the prevention and intervention of degenerative diseases that might be triggered by excessive exposure to MEP environments.

Objective: Cholesterol, High-density lipoprotein, and Glucose (CHG) index has recently been proposed as a marker of metabolic dysfunction. However, the association of CHG and CHG modified indices with the risk of stroke remains unclear.

Methods: We analyzed 8908 participants aged 45 years or older from the CHARLS. Baseline CHG and its modified forms (CHG-WC, CHG-BMI, CHG-BRI, CHG-WWI, CHG-WHtR, CHG-ABSI, and CHG-CVAI) were collected. Kaplan-Meier curves, Cox proportional hazards models, and restricted cubic spline (RCS) analyses were applied to assess associations with incident stroke.

Results: During a 9-year follow-up period, 828 (9.3%) participants had occurred strokes. Our analysis found a significant positive association between CHG, CHG-WC, CHG-BMI, CHG-BRI, CHG-WWI, CHG-WHtR, CHG-ABSI, CHG-CVAI, with stroke risk. The adjusted HR for the highest quartile compared to the lowest were: CHG 1.57 (95% CI:1.18-2.10), CHG-WC 1.72 (95% CI:1.34-2.20), CHG-BMI 1.62 (95% CI:1.26-2.08), CHG-BRI 1.65 (95% CI:1.29-2.10), CHG-WWI 1.71 (95% CI:1.32-2.23), CHG-WHtR 1.67 (95% CI:1.29-2.16), CHG-ABSI 1.41 (95% CI:1.10-1.80), and CHG-CVAI 1.99 (95% CI:1.54-2.57), with CHG-CVAI showing strongest associations. The RCS revealed a significant linear association between CHG, CHG-WC, CHG-BMI, and CHG-CVAI with the risk of stroke, whereas CHG-BRI, CHG-WWI, CHG-WHtR, and CHG-ABSI showed significant nonlinear associations with stroke risk. According to ROC analysis, CHG-CVAI had the highest predictive power for stroke risk (C-index:0.618).

Conclusions: Elevated CHG and its modified indices were strongly associated with stroke risk in middle-aged and older Chinese populations. CHG-related indices combined with obesity measures may help enhance the identification of individuals at higher risk of stroke.

Background: This study investigates how structural changes in deep medullary vein (DMV), glymphatic system dysfunction, and cognitive decline are interconnected in cerebral small vessel disease (CSVD), with a focus on whether impaired glymphatic function acts as a mediator in this relationship.

Methods: Clinical and MRI data from 93 CSVD patients were retrospectively analyzed. DMV burden was assessed using a semiquantitative scoring system (0-3 points per region), based on the visibility of DMVs in six anatomical regions on susceptibility-weighted imaging, yielding a total score ranging from 0 to 18. Glymphatic system function was evaluated using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Global cognitive function was assessed with the Montreal Cognitive Assessment (MoCA). Spearman correlation analysis, general linear modeling, and mediation analysis were conducted to examine the relationships among the variables.

Results: DMV scores(which higher scores indicate poorer venous visibility)were significantly negatively correlated with MoCA scores (r = -0.48, p< 0.001) and with the DTI-ALPS index (r = -0.28, p < 0.001), while the DTI-ALPS index was positively correlated with MoCA scores (r= 0.35, p < 0.05). Mediation analysis indicated that the DTI-ALPS index partially mediated the effect of DMV burden on cognitive performance, accounting for 14.08% of the total effect.

Conclusions: This study suggests that DMV structural abnormalities may exacerbate CSVD-related cognitive impairment by disrupting glymphatic function. DMV scoring may serve as a potential imaging biomarker, providing a foundation for early identification and intervention.

Background: Physical activity and exercise have been classified as safe and effective in mild to moderate cases of myasthenia gravis (MG) across various studies. Subsequently, adequate physical activity is generally recommended. Nevertheless, individuals with MG remain less physically active than the general population, without a precise definition of the low-activity group so far.

Methods: In this prospective single-center study, individuals with MG completed a questionnaire assessing general mobility, weekly physical activity levels, and beliefs toward specific statements about physical exercise. These data were contextualized with clinical parameters and MG-specific scores.

Results: Among 84 individuals (50% female), 73.8% reported general positive effects, and 77.4% noted improvements in mood and well-being due to physical activity. No significant differences in physical activity levels were found depending on sex, BMI or age. Weekly physical activity averaged 94.6 min (SD: 85.6), falling below current recommendations. Physical activity was inversely correlated with lower QMG (p = 0.019) and MG-ADL scores (p = 0.004). Despite the reported positive impact of physical activity on quality of life, no relevant connection was detected between physical activity and MG-QoL15 scores. Barriers preventing individuals affected by MG from engaging in physical activity included muscle pain (35.4%) and motivational challenges (22%). Individuals with motivational problems were younger (mean age 55.5 vs. 66.6 years, p = 0.011) and more frequently reported depressive symptoms; no other significant differences were observed in gender or disease severity in this subgroup.

Conclusion: Individuals with MG perceive physical activity as beneficial to their physical well-being, mood, and overall quality of life. Those with less severe disease tend to be more active. However, barriers such as motivational issues and post-exercise pain must be addressed. Clinicians should aim to identify individuals with low activity levels, encourage engagement in physical activity, highlight its benefits, and alleviate patient concerns.

Trial registration: Study approval by the Ethics Committee of the University Medical Center Göttingen was granted (number 33/12/21). The study was retrospectively registered at the German Clinical Trial Registry (DRKS) under the study ID DRKS00033171 (Date of trial registration December 1st, 2023).

Background: Posterior cerebral artery (PCA) infarctions represent 5-10% of all acute ischemic strokes, often manifesting with visual and cognitive deficits that can substantially impair quality of life. Although endovascular thrombectomy (EVT) is an established treatment for large-vessel occlusions, its role in isolated PCA occlusion remains uncertain, with limited evidence and heterogeneous outcomes across studies. We conducted this systematic review and meta-analysis to compare the effictiveness and safety of EVT versus medical management (MM) in patients with acute PCA occlusion.

Methods: A systematic review and meta-analysis was conducted according to PRISMA guidelines. PubMed, Scopus, and Web of Science were searched until July 2025. Eligible studies included randomized or cohort studies comparing EVT with MM in PCA occlusion. Primary outcomes were excellent (modified Rankin Scale [mRS] 0-1) and favorable (mRS 0-2) functional outcomes at 90 days. Secondary outcomes included change in National Institutes of Health Stroke Scale (NIHSS), symptomatic intracranial hemorrhage (sICH), and all-cause mortality. Pooled risk ratios (RR) and mean differences (MD) with 95% confidence intervals (CI) were calculated using random-effects models.

Results: Nine high-quality cohort studies including 57,287 patients (EVT = 2475; MM = 54,812) were analyzed. EVT was not associated with significant improvement in excellent (RR = 1.05; 95% CI, 0.91-1.21) or favorable (RR = 0.94; 95% CI, 0.84-1.05) functional outcomes compared to MM. Mortality (RR = 1.33; 95% CI, 0.99-1.80) and sICH (RR = 1.60; 95% CI, 0.87-2.93) rates were comparable between groups. However, EVT was associated with short-term neurological improvement on NIHSS at discharge (MD=-1.21; 95% CI, -1.96 to -0.46; p = 0.002).

Conclusions: EVT for PCA was associated with short-term neurological improvement versus MM, with mortality and sICH remaining comparable. However, these benefits did not translate into superior long-term functional outcomes as measured by mRS. The unique clinical features of PCA stroke, particularly visual and cognitive impairments not adequately captured by global disability scales, may contribute to this discrepancy. High-quality randomized trials incorporating domain-specific outcomes are warranted to define the role of EVT in PCA occlusion.

Background: Non-contrast CT (NCCT) is first-line imaging for suspected acute ischemic stroke (AIS) but has limited early sensitivity; deep learning (DL) may improve patient-level detection.

Objectives: To estimate the diagnostic accuracy of DL applied to NCCT for patient-level AIS detection and to examine prespecified sources of between-study heterogeneity.

Methods: We searched MEDLINE, Embase, and Web of Science (January 2010-May 2025). Eligible prospective or retrospective diagnostic studies evaluated DL on NCCT against an appropriate reference standard and reported (or allowed reconstruction of) patient-level 2 × 2 data. Two-gate case-control and lesion-only reports were excluded. Dual reviewers screened/extracted data; risk of bias was assessed with QUADAS-2, and AI-reporting against items adapted from STARD-AI/CLAIM/CONSORT-AI. Bivariate random-effects/HSROC models summarized sensitivity and specificity. Prespecified moderators were posterior-fossa inclusion, reference-standard robustness, and validation type. Sensitivity analyses included external-only cohorts, robust standards, posterior-fossa inclusion, and a "Direct AIS" construct subset.

Results: Of 1,899 records, 16 studies met inclusion; 13 contributed patient-level data to meta-analysis. Summary sensitivity was 0.91 (95% CI, 0.81-0.96) and specificity 0.90 (0.85-0.94). Sensitivity was lower for externally validated models than internally validated ones (0.82 [0.67-0.91] vs. 0.95 [0.89-0.98]) with similar specificity (0.88 [0.83-0.92] vs. 0.93 [0.82-0.97]). Findings were directionally robust across sensitivity analyses. QUADAS-2 frequently indicated concerns in patient selection and index-test domains; AI-reporting quality was mostly moderate, and explicit external validation remained uncommon.

Conclusions: DL applied to NCCT shows high accuracy for patient-level AIS detection. However, generalizability is the principal gap; broader external validation and guideline-concordant reporting are needed to support safe clinical adoption.

Sleep disorders are the most common non-motor symptoms in patients with Parkinson's disease (PD) and can significantly impact quality of life. However, the pharmacological treatments for sleep disorders in patients with PD remain controversial. Databases (PubMed, Embase, Web of Science, and the Cochrane Library) were searched up to 20 January 2025. Randomized controlled trials were included if they investigated the efficacy of pharmacological interventions for sleep disorders in populations with PD. The weighted mean difference (WMD) and standardized mean difference (SMD) were used as effect size. The random-effects model was applied due to the heterogeneity between studies. The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation tool. A total of 23 studies were included in the systematic review and meta-analysis. Seven trials focused on the treatment of rapid eye movement sleep behavior disorder (RBD), seven on excessive daytime sleepiness (EDS), six on insomnia, and three on sleep disorders that were not specifically classified. The meta-analysis showed that pharmacological interventions were effective in improving EDS (SMD: -0.36; 95% CI: -0.63, -0.10) and insomnia in patients with PD (SMD: -0.53; 95% CI: -0.82, -0.24). However, pharmacological interventions did not show significant benefits for managing RBD (SMD: -0.19; 95% CI: -0.54, 0.16). The certainty of evidence across all studies ranged from very low to moderate. In population with PD, pharmacological interventions were effective in improving EDS and insomnia. However, the management of RBD with pharmacological treatments remains challenging. In the future, strong evidence is needed to address this challenge.

Background: Studies often investigated risk factors for post-stroke depression (PSD) at a single timepoint, neglecting its dynamic nature. We aimed to identify distinct trajectories of depressive symptoms over the first-year post-stroke and explore their early predictors.

Methods: We conducted a prospective cohort study at a stroke center in China from 2022 to 2024. Stroke patients with HAMD assessments at 3-, 6-, and 12-months were included. We identified trajectories using group-based trajectory modeling. The optimal number was determined by statistical criteria and interpretability. To explore predictors of trajectory membership, we conducted two separate binary logistic regression analyses, adjusted for covariates, to differentiate subtypes within favorable (Resilient vs. Recovering) and unfavorable (Fluctuating vs. Worsening) outcome clusters. We also conducted a separate analysis to identify predictors for developing any form of post-stroke depression (Recovering, Fluctuating, or Worsening) versus remaining in the Resilient group.

Findings: The analysis comprised 634 participants. We identified four distinct trajectories: Resilient (50.8%, n = 322), Recovering (9.6%, n = 61), Fluctuating (18.8%, n = 119), and Worsening (20.8%, n = 132). Compared to Recovering, Resilient patients were less likely to be smokers (OR = 0.48, 95% CI: 0.25-0.93) and have hypertension (OR = 0.40, 95% CI: 0.17-0.92), and had lower nutritional risk. Comparing unfavorable trajectories, smoking (OR = 4.17, 95% CI: 1.75-9.96) and hyperlipidemia (OR = 3.83, 95% CI: 1.97-7.47) predicted the Fluctuating group versus Worsening. Functional improvement pace was also significantly associated. A subsequent analysis of overall risk, conducted on 553 participants with complete data, found that male sex was the only significant independent predictor, and was strongly associated with a reduced likelihood of developing any form of PSD (OR = 0.35, 95% CI: 0.17-0.73).

Interpretation: The course of PSD is heterogeneous and classifiable into four trajectories. Our primary analysis identified male sex as a strong protective factor against any form of PSD. Concurrently, secondary analyses suggest that early modifiable factors, particularly smoking and metabolic status, are significant predictors differentiating unfavorable subtypes. The novel association observed with VTE risk scores should be considered exploratory and requires further validation. These findings highlight the value of using clinically accessible markers for early risk stratification, though underscore the complexity of PSD prediction. This study was limited to a single center.