International Journal of Neuroscience最新文献

Background: Failure of delayed neurological improvement (fDNI) following successful recanalization is a prevalent clinical phenomenon in patients who have experienced acute ischemic stroke (AIS). An investigation into the potential link between markers of systemic inflammation such as platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index known as SII, and the occurrence of fDNI in patients received successful reperfusion was conducted.

Methods: The study included patients diagnosed with AIS who underwent thrombectomy and experienced fDNI, as observed in a prospective study conducted from January 2017 to April 2020. In order to identify predictors of fDNI, we performed multivariable logistic regression and receiver operating characteristic (ROC) curve.

Results: Eighty-four patients (23.86%) without early neurological improvement (ENI) experienced DNI, and 268 (76.14%) patients did not show DNI. After adjustment for potential confounders, NLR (adjust OR, 2.131; 95%CI, 1.066-4.259; p = 0.032) and SII (adjust OR, 1.065; 95%CI, 1.001-1.132, p = 0.045) exhibited independent reationship with fDNI independently in multivariate analysis. The areas under AUC of multivariable NLR and SII mode were 0.862 and 0.861, respectively.

Conclusions: The immune-inflammatory biomarkers, including NLR and SII, exhibited associations with DNI in patients without ENI. Further investigations are warranted to elucidate the underlying mechanisms.

Introduction: Current electroencephalogram (EEG) indices for monitoring the depth of anaesthesia have not been adjusted for age. This study aims to identify the most suitable EEG indices for monitoring anaesthesia depth across different age groups.

Method: In here, we conducted a prospective observational trial to observe whether different EEG features in different age groups can detect anaesthesia depth and compare their performance.

Results: In Group 1, absolute θ had the highest prediction probability (Pk) value (0.78 ± 0.07), while relative γ had the lowest relative coefficient of variation (CV) value in states 1 (0.160), 2 (0.153), 3 (0.214) and 4 (0.144). In Group 2, absolute θ had the highest Pk value (0.81 ± 0.09), and relative γ had the lowest CV value in states 1 (0.159), 2 (0.178), 3 (0.140) and 4 (0.095). In Group 3, absolute δ had the highest Pk value (0.79 ± 0.08), with absolute slow wave having the lowest CV value in States 1 (0.196), 2 (0.258), 3 (0.295) and 4 (0.181). In Group 4, absolute δ had the highest Pk value (0.75 ± 0.10), and absolute slow wave had the lowest CV value in States 1 (0.176), 2 (0.192), 3 (0.185) and 4 (0.144).

Conclusion: In this study, we found that absolute θ and relative γ are the most suitable choices for young and middle-aged patients, while absolute δ and absolute slow wave are the most suitable choices for young elderly and elderly patients.

Background: Previous studies have shown that an increased number of immune cells is closely associated with the onset and course changes of intracerebral hemorrhage, but the exact causal relationship has not been clarified. The aim of this study was to investigate the causal relationship between immune cells and intracerebral hemorrhage by a two-way Mendelian randomization method.

Methods: Two sets of SNPs were used as instrumental variables and two-way Mendelian randomization analyses were performed and leave-one-out method were used to assess the validity and heterogeneity of the included genetic variation instruments. The level of multiplicity and heterogeneity of the included genetic variance instruments was assessed.

Results: The results showed a clear causal relationship between three immune cells and intracerebral hemorrhage, and no heterogeneity between SNPs related to intracerebral hemorrhage, while scatterplot and funnel plot confirmed that the causality was less likely to be biased; MR-Egger results suggested that no genetic pleiotropy was found. Leave-one-out analysis was applied to suggest that the MR analysis results for a single SNP were robust; meanwhile, Meta-analysis was applied to combine the two intracerebral hemorrhage datasets, and the analysis results suggested that in the fixed-effects model and random-effects model, the immunocyte CD66b on Granulocytic Myeloid-Derived Suppressor Cells and other three immune cells were significantly causally associated with intracerebral hemorrhage, while the heterogeneity test suggested that there was no significant difference between the different datasets.

Conclusions: The present study found a significant causal relationship between specific immune cell phenotypes and intracerebral hemorrhage by Mendelian randomization analysis.

Background: Intracranial Aneurysms (IA) are life-threatening cerebrovascular diseases, and their pathogenesis remains not fully understood. This study aims to systematically evaluate the causal relationship between cerebrospinal fluid (CSF) metabolites and IA through bidirectional two-sample Mendelian randomization (MR) analysis to identify potential biomarkers and therapeutic targets.

Methods: Using genome-wide association study data from public databases, the primary analysis was conducted with inverse variance weighting, supplemented by MR-Egger regression, weighted median, weighted mode, and simple mode. Sensitivity analyses were performed using heterogeneity tests, horizontal pleiotropy tests, and leave-one-out analyses to ensure the stability of the results.

Results: Forward MR analysis revealed that 1-arachidonoyl-gpc (20:4n6), 2'-deoxyuridine, 3-hydroxystachydrine, 5-hydroxyhexanoate, isobutyrylcarnitine (C4), phenylacetylglutamine, and X-10457 were protective factors for IA. In contrast, 2-hydroxybutyrate/2-hydroxyisobutyrate, arabonate/xylonate, argininosuccinate, citrate, cysteinylglycine disulfide, and isovalerate were identified as risk factors for IA. Reverse MR analysis showed a significant causal relationship between IA and changes in the concentrations of 14 CSF metabolites. Sensitivity analyses indicated the robustness of these findings.

Conclusions: This study, through bidirectional MR analysis, uncovered the causal relationship between CSF metabolites and IA, highlighting the significant roles of various amino acids and lipid metabolites in the pathophysiology of IA. These metabolites not only provide new insights into the mechanisms underlying IA but also present potential biomarkers and therapeutic targets, offering theoretical support for early intervention and prevention strategies for the disease.

Persistent post-concussion symptoms can greatly affect a person's life, thinking abilities, and their capacity to go back to normal daily tasks. This narrative review gives a detailed look at new treatments for ongoing symptoms after a concussion. It includes methods like brain stimulation, medications, and holistic approaches. We searched for information using PubMed, Embase, and Google Scholar, looking for keywords like 'post-concussion syndrome', 'neuromodulation', 'rehabilitation', 'cognitive behavioral therapy', 'vestibular therapy', and 'medications. 'This review talks about how each therapy works, the proof from clinical trials, and practical tips for using them. Methods like transcranial magnetic stimulation and transcranial direct current stimulation could help improve thinking skills and mood problems. Medicines like amantadine and zolpidem may help treat certain symptoms. Methods like aerobic exercise, balance training, and talk therapy provide overall benefits. The review also points out future areas to explore, like stem cell treatment, using virtual reality for rehab, timing treatments better, and using light therapy. This review looks at new information and trends to help doctors and researchers understand the latest ways to treat ongoing symptoms after a concussion. It also aims to guide future studies in this important field.

Aim: Symptomatic carotid artery stenosis (SCAS) patients face a high risk of ischemic stroke (IS) recurrence. This study evaluates the prognostic value of the serum lactate dehydrogenase (LDH)-to-albumin ratio (L/A) for predicting IS recurrence among SCAS patients.

Methods: In this retrospective study (January 2020-January 2023), 307 conservatively managed SCAS patients were stratified into non-recurrence (n = 238) and recurrence (n = 69) groups based on 24-month follow-up. Serum LDH and albumin were measured, and L/A was calculated. Relationships between L/A and plaque neovascularization-related parameters (peak intensity, AUCTC, CAS rate) were assessed using Pearson correlation. Independent risk factors were determined with multivariate Cox regression, while the predictive performance and risk stratification of L/A were evaluated with ROC and Kaplan-Meier curves.

Results: Significant differences existed in age, hypertension, hyperlipidemia, systolic blood pressure, low-density lipoprotein cholesterol, albumin, peak intensity, AUCTC, CAS rate, vulnerable plaques, and medication adherence between the two groups. Recurrent patients exhibited higher L/A (p < 0.005). L/A correlated positively to peak intensity (r = 0.323), AUCTC (r = 0.450), and CAS rate (r = 0.529; all p < 0.001). Hyperlipidemia, vulnerable plaques, peak intensity, CAS rate, and elevated L/A were independent risk factors for IS recurrence in SCAS patients. L/A could assist in predicting IS recurrence in SCAS patients (AUC: 0.801; sensitivity: 63.77%; specificity: 85.71%). High L/A significantly increased 2-year recurrence risk in SCAS patients.

Conclusion: Elevated L/A potentially aids in predicting IS recurrence in SCAS patients and correlates to plaque neovascularization.

Background: Reliable molecular biomarkers for predicting cerebral infarction outcomes remain limited, highlighting the need for integrative approaches that bridge bioinformatic discovery with clinical validation.

Objective: To identify key differentially expressed genes (DEGs) prognostic for cerebral infarction and evaluate their clinical utility for risk stratification through integrated bioinformatic analysis and prospective cohort validation.

Methods: Functional annotation employed GO enrichment and protein-protein interaction network analysis. A prospective cohort enrolled 151 cerebral infarction patients, with peripheral blood samples subjected to qPCR analysis of candidate genes. Prognostic predictive capacity was assessed via multivariable Cox regression, Kaplan-Meier survival analysis, and ROC curve analysis with clinical follow-up data.

Results: Five candidate DEGs (VIM, OSM, PTGS2, SOD2, SAMSN1) were identified, enriched in inflammatory response, nitric oxide metabolism, and lipopolysaccharide response pathways. qPCR confirmed significantly elevated VIM, OSM, and PTGS2 expression in poor prognosis group (p < 0.01). Multivariable Cox regression identified VIM (HR = 4.475), OSM (HR = 2.800), and homocysteine (Hcy; HR = 1.120) as independent prognostic risk factors. Kaplan-Meier analysis demonstrated significantly reduced survival in high-expression groups (all p < 0.01). The combined model integrating VIM, OSM, and Hcy achieved superior predictive performance (AUC = 0.811; sensitivity 72.55%, specificity 78.00%, Youden's index 0.506) compared to VIM alone (AUC = 0.760).

Conclusion: VIM and OSM exhibit robust bioinformatic associations and stable expression with independent prognostic value in clinical cohorts.

Emotional states are fundamental attributes distinguishing humans from machines, and productivity represents one of the primary life objectives for this emotionally driven being. However, existing research on productivity and job performance frequently underestimates the impact of underlying emotional mechanisms. Thus, a systematic examination of the emotion-productivity interface is essential to clarify the psychophysiological processes that regulate work efficiency. In this study, emotional induction was achieved through a curated video stimulus set designed to evoke positive (happiness) and negative (sadness) responses in 39 participants, followed by a computer-based Stroop task. Electroencephalography (EEG) was employed to capture emotional states within the two-dimensional valence-arousal framework. During task performance, parameters related to productivity metrics were recorded. Three machine learning models - Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) - were implemented to predict productivity levels. For positive emotions, mean absolute error (MAE) values were 0.1031 (ANN), 0.0760 (SVM), and 0.0721 (RF). For negative emotions, the values were 0.1165, 0.0902, and 0.0659, respectively. Results demonstrated that productivity levels increased during tasks performed after the induction of positive emotions. Overall, this study provides empirical evidence that productivity can be predicted from emotional states, emphasizing their integral role in cognitive processes and their potential utility for optimizing workplace performance.

Objective: We investigated the association between middle cerebral artery atherosclerotic plaque features and cerebral small vessel disease (CSVD) imaging markers as well as the total CSVD burden, in patients with branch atheromatous disease (BAD).

Methods: Plaque parameters were quantified using high-resolution magnetic resonance imaging (HR-MRI) with ImageJ software, to characterize distribution, lumen stenosis, remodeling patterns, and other relevant features. Conventional MRI assessed CSVD imaging markers and total CSVD burden. Multivariate logistic regression analysis was performed following adjustment for potential confounders. Receiver operating characteristic (ROC) curve analysis with the DeLong test assessed the predictive value of plaque features for total CSVD burden.

Results: Compared with the non-plaque group, the plaque group showed significantly higher proportions of severe white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and moderate-to-severe CSVD burden (p < 0.05). In multivariate analysis, the presence of plaque was an independent risk factor for WMHs (OR = 2.920), CMBs (OR = 1.995), and moderate-to-severe CSVD burden (OR = 2.853); plaque distribution was an independent risk factor for WMHs (OR = 3.367); eccentric plaques were independent risk factors for lacunar infarction (OR = 8.670) and CMBs (OR = 7.891); positive remodeling (OR = 9.285) and eccentric plaques (OR = 10.355) were independent risk factors for moderate-to-severe CSVD burden. ROC analysis demonstrated plaque vulnerability effectively predicted moderate-to-severe CSVD burden (AUC = 0.8808, p < 0.05).

Conclusion: In ischemic stroke patients, distinct intracranial atherosclerotic stenosis (ICAS) plaque features correlate with specific CSVD phenotypes. Vulnerable plaques not only significantly increase total CSVD burden but also effectively predict CSVD severity. These findings elucidate how ICAS influences CSVD burden progression from an HR-MRI perspective and facilitate clinical risk stratification.

Purpose: Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods.

Methods: 131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set. Data from additional 8 patients were collected as the test set, and the accuracy of the model was evaluated from a dosimetric standpoint.

Results: Comparing the synthetic CT images with the original CT images, the difference in dosimetric parameters D98, D95, D2 and D_mean of PTV in 8 patients was less than 0.5%. The gamma passed rates of PTV and whole body volume were: 1%/1 mm: 93.96%±6.75%, 2%/2 mm: 99.87%±0.30%, 3%/3 mm: 100.00%±0.00%; and 1%/1 mm: 99.14%±0.80%, 2%/2 mm: 99.92%±0.08%, 3%/3 mm: 99.99%±0.01%.

Conclusion: MR images can be used both in delineation and treatment efficacy evaluation and in dose calculation. Using the deep learning way to convert MR image to CT image is a viable method and can be further used in dose calculation.