BMC Neurology最新文献

Background: Acute basilar artery occlusion (BAO) carries high risks of disability and mortality. While endovascular thrombectomy (EVT) improves outcomes, reliable prognostic tools are needed. This study aimed to evaluate the prognostic value of the novel Basilar Artery and Posterior Circulation Collaterals (BAPCOR) score on digital subtraction angiography (DSA) for predicting clinical outcomes after EVT in acute BAO.

Methods: A retrospective analysis was conducted on consecutive acute BAO patients treated with EVT at two comprehensive stroke centers (October 2020-January 2024). The BAPCOR score (15-point DSA-based system) assessed thrombus burden and posterior circulation collateral status. Successful recanalization was defined as modified Thrombolysis in Cerebral Infarction (mTICI) 2b-3, and good outcome as modified Rankin Scale (mRS) ≤ 2 at 3 months. Multivariate regression and receiver operating characteristic (ROC) analyses identified predictors of outcome.

Results: Among 98 acute BAO patients treated with endovascular thrombectomy, successful recanalization (mTICI 2b-3) was achieved in 91.8% (90/98), with 37.8% (37/98) attaining good functional outcomes (mRS ≤ 2) at 3 months. The novel BAPCOR score demonstrated strong prognostic value, showing an AUC of 0.821 (95% CI: 0.738-0.903) for predicting clinical outcomes. A BAPCOR score ≤ 5.5 optimally predicted poor outcomes with 89.2% sensitivity and 63.9% specificity. Patients with good outcomes had significantly higher median BAPCOR scores (9 vs. 5, P < .001). Multivariate analysis confirmed BAPCOR > 5.5 as an independent predictor of favorable outcomes (OR = 11.345; 95% CI: 3.137-41.591; P = .001), and the BAPCOR score outperformed the BATMAN-DSA score (AUC 0.821 vs. 0.735) in prognostic accuracy.

Conclusion: The BAPCOR score demonstrated significant prognostic value for predicting clinical outcomes in acute BAO patients undergoing EVT. The scoring system's superiority stems from its comprehensive assessment of both thrombus burden (vertebrobasilar segments) and posterior circulation collaterals (including primary PCoA pathways and secondary cerebellar artery anastomoses), providing enhanced characterization of hemodynamic compensatory mechanisms.

Background: Multiple organ dysfunction syndrome (MODS) is a severe consequence in individuals with acute intracerebral hemorrhage (ICH), resulting in elevated morbidity and fatality rates. Precise risk forecasting is crucial for prompt interventions. This study sought to create and verify a nomogram model for predicting the likelihood of MODS in patients with acute ICH.

Methods: A retrospective cohort study was conducted at our hospital from January 2022 to December 2024. A total of 159 patients with acute ICH were included, of whom 31 developed MODS. Baseline data, including demographic, clinical, and laboratory parameters, were collected. Multivariate logistic regression was performed to identify independent risk factors for MODS. A nomogram was constructed incorporating significant predictors. The model's performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and internal validation with bootstrap resampling (1,000 iterations).

Results: The logistic regression analysis indicated type 2 diabetes, an Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 35 or higher, endotoxemia, and an ICH volume of 30 mL or greater as independent risk factors for MODS (all P < 0.05). The nomogram had outstanding predictive capability, exhibiting an area under the receiver operating characteristic curve (AUC) of 0.861 (95% CI: 0.786-0.928). Internal validation produced a corrected C-index of 0.811 (95% CI: 0.776-0.852), and the calibration curve demonstrated strong concordance between predicted and actual outcomes. DCA exhibited a superior net advantage of the nomogram relative to extreme treatment approaches.

Conclusions: The nomogram developed in this study effectively predicts the risk of MODS in patients with acute ICH. It offers a practical tool for early risk stratification and may guide clinical decision-making in critical care settings. Further external validation in larger cohorts is needed to confirm its generalizability.

Background: Glioblastoma (GBM) represents a profoundly aggressive and heterogeneous brain neoplasm linked to a bleak prognosis. Palmitoylation plays a key role in the development and progression of GBM, but its molecular mechanism and prognostic significance in GBM are still not fully understood. This study aims to explore the prognostic biomarkers of GBM based on palmitoylation-related genes.

Methods: Eight scoring methods, including AUCell, UCell, singscore, ssGSEA, JASMINE, VAM, scSE, and viper, were used to score each sample. In addition, the palmitoylation score calculated by the AUCell algorithm is selected as the representative. In order to screen the genes related to GBM survival and build a risk prognosis model, 101 algorithms constructed by 10 kinds of machine learning are arranged and combined for variable screening and model building, and then immune infiltration and immunotherapy evaluation, drug screening, and molecular docking are carried out.

Results: We observed that macrophages in GBM cell types have the highest palmitoylation score. The secondary dimensionality reduction clustering of macrophages showed that the palmitoylation of PLCG2 + macrophages was significantly higher than that of other subtypes, and three core prognostic genes (ZDHHC2, ZDHHC4, ZDHHC20) were screened out by machine learning. A higher risk score is significantly related to worse clinical status and most immune labels. Among them, ZDHHC2 was significantly up-regulated in GBM in several verification groups. Molecular docking found that quercetin was the best targeted drug for ZDHHC2.

Conclusion: This study revealed for the first time the heterogeneity of palmitoylation at the GBM single-cell level. The identification of ZDHHC2, ZDHHC4, and ZDHHC20 as key regulators of palmitoylation in GBM emphasized their potential as biomarkers and therapeutic targets.

Background: Most adults with migraine never consult their family doctor or receive a formal diagnosis. It is often these people that turn to the internet for healthcare information. Here we describe the development and testing of an on-line version of a previously validated telephone migraine classification interview.

Methods: We co-produced and tested the tool with people with migraine, clinicians, a web-design company and the National Migraine Centre, a UK based charity. We started with an online stakeholder meeting to understand what resources people wanted and any potential obstacles and facilitators to successful implementation. At a consensus meeting we used nominal group technique to gain consensus on the key questions to include. We used a 'Think Aloud technique' with people with migraine to explore the performance and acceptability of the questions in the new tool. To validate it, we asked people with headache disorders, including migraine, to complete the online tool before a doctor specialising in headache diagnosed their headache type. Level of agreement was measured between the outcome of the new migraine tool and the diagnosis by the doctor for 100 participants.

Results: At the consensus meeting it was agreed that the questions for the new tool should be clear and easy to understand and distinguish between migraine, tension type headache, other primary headache disorders and medication overuse headache. For our initial validation exercise the level of agreement between the migraine quiz and the doctor diagnosis for 100 participants was 78% agreement. We made some adjustments to the wording and logic of the tool and repeated the validation exercise with 130 participants; the level of agreement was 80%.

Conclusions: Despite a careful development process our on-line tool did not perform to an adequate standard. There is a risk that misclassification could lead to people receiving inappropriate treatment. Any on-line classification tool for multiple headache disorders should be adequately validated before widespread use to avoid risk of iatrogenic harm.

Clinical trial number: Not applicable.

Background: Neuroinflammation-mediated epilepsy has become a focus of attention in recent years. Post-stroke epilepsy (PSE) is common, with most studies focusing on ischemic stroke rather than intracerebral hemorrhage (ICH) patients. Therefore, we aimed to determine the association between monocyte-to-lymphocyte ratio (MLR) levels and epilepsy after ICH. Additionally, we sought to develop a clinical score integrating MLR with the CAVE score to improve the risk stratification of PSE.

Methods: We retrospectively included consecutive ICH patients from January 2010 to July 2020. MLR was acquired from a routine blood test at admission. Multiple logistics and linear regression analyses were performed to identify risk factors for the PSE when MLR levels were divided into tertiles. Receiver operating characteristic (ROC) curves were used to assess the predictive accuracy of epilepsy models based on the CAVE score and the MNCAVE score.

Results: Among the 834 patients with ICH, 47 (5.6%) patients had PSE. In multivariable logistics and linear regression analysis, elevated MLR levels were independently associated with the PSE after adjusting for several confounders (OR 2.545, 95% CI 1.036-6.255, P = 0.042). Six risk factors were selected to derive the MNCAVE score (higher MLR, severe stroke, cortical location, young age, large hematoma volume, and early seizures), which showed better prognostic performance than the conventional CAVE score (0.875 vs. 0.848, P = 0.041).

Conclusions: Elevated MLR levels were associated with PSE after ICH. We developed the MNCAVE score for predicting PSE, which could improve the management of patients after ICH. Inflammatory markers may provide personalized guidance for patients with varying degrees of neurological impairment.

Background: Restless Legs Syndrome (RLS), characterized by an urge to move the legs, is linked to neurodegenerative diseases. Emerging evidence suggests a higher RLS prevalence in Amyotrophic Lateral Sclerosis (ALS), impacting quality of life. However, there is lack of comprehensive review addressing its prevalence and associated risk factors. This meta-analysis estimates RLS prevalence in ALS patients compared to healthy controls.

Methods: We searched PubMed, Embase, Web of Science, and Scopus for studies assessing RLS in ALS patients versus controls, adhering to PRISMA guidelines. Two reviewers independently extracted data and assessed bias using Joanna Briggs Institute (JBI) checklist. Meta-analysis used Comprehensive Meta-Analysis software with a random-effects model due to heterogeneity. The certainty of evidence was appraised using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework.

Results: Out of 291 studies, eight studies (792 ALS, 716 controls) were included. The pooled RLS prevalence in ALS was 17% (95% CI: 14.0%-21.1%; I²: 56.5%; p-value < 0.001). The fixed effect meta-analysis of four studies indicated that the difference of RLS prevalence was statically significant between patients with ALS and healthy controls (OR: 5.65; CI: 2.86-11.13; P-value < 0.001; I²: 28.7.).

Conclusion: RLS is significantly more prevalent in ALS patients, potentially worsening sleep and quality of life, mental health, and social well-being. Therefore, it is essential to draw clinicians' attention to RLS in ALS patients due to its potential impact on overall health.