International Journal of Neuroscience最新文献

Background: Chronic obstructive pulmonary disease (COPD) and stroke are leading contributors to mortality and disability. We analyzed national trends in deaths attributed to co-occurring stroke and COPD in US adults from 1999-2023.

Methods: Using CDC WONDER multiple cause-of-death data for adults aged ≥25 years, we identified decedents with co-existent stroke (ICD-10 I60.x, I61.x, I63.x, I64, I69.0, I69.1, I69.3, I69.4) and COPD (J40-J44). Crude and age-adjusted mortality rates (AAMRs) per 100,000 population were calculated using the 2000 US standard population. Joinpoint regression estimated annual percent changes (APCs) and identified trend inflection points. Analyses were stratified by sex, race/ethnicity, metropolitan status, and state.

Results: From 1999 to 2023, 311,375 deaths involved concurrent stroke and COPD. Overall AAMRs declined from 8.04 per 100,000 in 1999 to 5.17 in 2009 (APC -4.40%), with continued decline to 2018 (APC -1.34%). Rates then increased through 2020-2021 (APC +7.33%) before a modest decline toward 2023. Males consistently exhibited higher AAMRs than females. Non-Hispanic White adults had the highest AAMRs, whereas Hispanic and Asian/Pacific Islander groups had lower rates. Non-metropolitan areas experienced consistently higher AAMRs than metropolitan areas. State-level analyses identified the highest burdens in Appalachian and Deep South regions.

Conclusion: Mortality from coexisting stroke and COPD declined for two decades but rose around the COVID-19 period, revealing significant sex, racial/ethnic, and geographic disparities. These findings highlight the need for targeted prevention, improved access to care for high-risk populations, and further research into mechanisms driving recent inflection points. Data and methods are detailed in the manuscript.

Background: Opioid addiction is a major public health concern, associated with numerous health and social problems. Conventional diagnostic methods for addiction have notable limitations, highlighting the need for alternative approaches.

Methods: This study investigates the use of electroencephalography (EEG) signals in conjunction with transcranial direct current stimulation (tDCS) for the diagnosis and treatment of opioid addiction. Thirty-six male patients undergoing methadone maintenance treatment were recruited and randomly assigned to three groups: Group A received left anodal/right cathodal tDCS ($n=12$), Group B received right anodal/left cathodal tDCS ($n=12$), and Group C received sham stimulation ($n=12$). EEG recordings were obtained from all participants before and after tDCS, as well as from 24 healthy controls. Machine learning techniques were applied to develop an optimized algorithm capable of distinguishing between healthy and addicted individuals by selectively analyzing addiction-affected EEG channels, thereby reducing processing time and costs.

Results: The proposed method achieved a diagnostic accuracy of 94.30%. In addition, the effects of tDCS on craving reduction were assessed using EEG signals, psychological questionnaires, and blood biomarkers. Significant reductions in craving levels were observed in Groups A and B.

Conclusion: These findings suggest that tDCS can be an effective intervention for reducing craving in patients with opioid addiction.

Aim: To investigate the correlation of serum changes and markers of brain injury (BI) in cerebrospinal fluid (CSF) with postoperative cognitive dysfunction (POCD) in patients with cerebral aneurysmal subarachnoid haemorrhage (aSAH).

Methods: 120 patients diagnosed with aSAH were included. 3 months after surgery, these patients were divided into a normal cognition group and a cognitive dysfunction (CD) group relying on the Montreal Cognitive Assessment (MoCA) Scale.

Results: The correlations were analysed between the serological changes and the levels of BI markers, such as neurofilament-light (NF-L) protein, Ubisquitin C-terminal hydrolase L1(UCH-L1), Glial Fibrillary Acidic Protein (GFAP), and neuron specific enolase (NSE) in patients after surgery. Hunt-Hess grading standard was employed to determine the severity of aSAH in patients. The mean values of NF-L, UCH-L1, GFAP, and NSE were (8.2 ± 4.3) pg/mL, (0.7 ± 0.3) ng/mL, (2.2 ± 0.4) ng/mL, and (48.5 ± 10.9) ng/mL in patients with severe aSAH, which were remarkably higher than those in patients with mild aSAH [(3.5 ± 0.7) pg/mL, (0.5 ± 0.2) ng/mL, (1.3 ± 0.7) ng/mL, (30.7 ± 8.2) ng/mL]. The sensitivity, specificity, and accuracy of the combined prediction of four detections for POCD were 90.80%, 84.20%, and 82.80%, respectively, which were greatly higher than those of four independent predictions (p < 0.05). The combined prediction effect of the four items, with the area under the curve (AUC) of 0.938 and the 95% confidence interval (CI) of 0.851-0.926.

Conclusions: BI markers NF-L, UCH-L1, GFAP, and NSE could be utilized as predictors of POCD in patients with aSAH, deserving a reference value.

Objective: In order to provide a more accurate and effective basis for clinical diagnosis and treatment, patients with cognitive dysfunction after acute ischemic stroke (AIS) were evaluated and their influencing factors were analyzed.

Methods: A rigorous and systematic logistic regression analysis was conducted to comprehensively investigate the various influencing factors that contribute to cognitive dysfunction.

Results: Among them, the sex granulocyte/lymphocyte ratio (NLR), low-density lipoprotein cholesterol (LDL-C) level, and C-reactive protein (CRP) were also higher than those in the control group (p < 0.05). The scores of memory, orientation, visual and spatial function, abstract thinking and language in the control group were higher than those in the experimental group (p < 0.05). The results of multivariate logistic regression analysis showed that history of diabetes mellitus, high NLR, high LDL-C, high CRP, smoking and temporal lobe infarction were risk factors for cognitive dysfunction after AIS, while elevated BMI and love of exercise were protective factors for cognitive dysfunction after AIS.

Conclusion: Patients with cognitive dysfunction had the highest incidence of temporal lobe infarction, and they scored lower than the control group on memory, orientation, visual and spatial function, abstract thinking, and language function. Multivariate logistic regression analysis showed that a history of diabetes mellitus, high NLR, high LDL-C, high CRP, smoking, and temporal lobe infarction were independent risk factors for cognitive dysfunction after acute ischemic stroke, while elevated BMI and a love of exercise were protective factors for cognitive dysfunction after acute ischemic stroke.

Background: To promote carcinogenesis through diverse molecular pathways involving dysregulation of gene expression and abnormalities.

Methods: We employed Mendelian randomization (MR) to uncover causal relationships between genetic factors and HNSCC. We used the Inverse Variance Weighted (IVW) method as the primary MR analysis, and validated the results through complementary approaches like MR-Egger regression, weighted median, and mode analyses.

Results: Our analysis identified 2210 genes that are differentially expressed in head and neck cancer (HNSCC) compared to normal tissues. Within the protein interaction network, the genes IL1B, CXCL8, CXCL1, and CCL2 stood out as central hubs. Further investigation revealed that these key genes are involved in important biological processes like skin development, wound healing, and fat metabolism. Notably, our Mendelian randomization analysis provided evidence for a causal relationship between the expression of the IL1B gene and the development of HNSCC.

Conclusions: Our analysis identified 5 key genes - IL1B, CXCL8, CXCL1, CCL2, and IL1B - that show significant changes in expression in head and neck cancer. These genes could serve as important new biomarkers to help diagnose this disease and track how it progresses over time. Importantly, these genes are involved in regulating the immune system, suggesting that the body's immune response plays a critical role in head and neck cancer. This provides new avenues for future research to better understand the complex gene expression patterns underlying this type of cancer. Further investigation of these key genes and their regulatory networks could lead to important insights and potential new treatment approaches.

Brain tumors represent a significant neurological challenge, affecting individuals across all age groups. Accurate and timely diagnosis of tumor types is critical for effective treatment planning. Magnetic Resonance Imaging (MRI) remains a primary diagnostic modality due to its non-invasive nature and ability to provide detailed brain imaging. However, traditional tumor classification relies on expert interpretation, which is time-consuming and prone to subjectivity. This study proposes a novel deep learning architecture, the Dual-Feature Cross-Fusion Network (DF-CFN), for the automated classification of brain tumors using MRI data. The model integrates ConvNeXt for capturing global contextual features and a shallow CNN combined with Feature Channel Attention Network (FcaNet) for extracting local features. These are fused through a cross-feature fusion mechanism for improved classification. The model is trained and validated using a Kaggle dataset encompassing four tumor classes (glioma, meningioma, pituitary and non-tumor), achieving an accuracy of 99.33%. Its generalizability is further confirmed using the FigShare dataset, yielding 99.22% accuracy. Comparative analyses with baseline and recent models validate the superiority of DF-CFN in terms of precision and robustness. This approach demonstrates strong potential for assisting clinicians in reliable brain tumor classification, thereby improving diagnostic efficiency and reducing the burden on healthcare professionals.

Background: A growing body of strong evidence shows that voltage-gated sodium channels genes play key roles in the development of sporadic Parkinson's disease (sPD). However, little data have been reported on the association between single nucleotide polymorphisms (SNPs) and sPD. This study aimed to investigate the association between SCN2A gene polymorphisms and sPD.

Methods: 267 patients with sPD and 267 healthy controls were included in this study. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was performed. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of SCN2A in the serum of patients and healthy individuals.

Results: The distribution of the G allele of rs2304016 or the A allele of rs17183814 in SCN2A was significantly higher in patients with sPD (p = 0.001). In subtype analysis, the frequency of the rs2304016 AG heterozygote significantly differed between the early onset PD (EOPD) and late-onset PD (LOPD) groups (p < 0.001). The frequency of the rs17183814 AG heterozygote was significantly higher in the male patients (p = 0.002). Furthermore, we found that the level of SCN2A mRNA transcription in the serum of sPD patients was significantly lower than that in the control group (p < 0.05). The serum expression level of SCN2A in patients with the AA genotype at rs17183814 was lower (p < 0.05).

Conclusions: This study demonstrated a significant association between SNPs and the expression of SCN2A with sPD. These findings contribute to a better understanding of the role of SCN2A and SCN2A SNPs in sPD.

Ischemic stroke (IS) is a leading cause of death and disability worldwide, with a complex pathogenesis involving neuroinflammation, oxidative stress, and disruption of the blood-brain barrier. Recent research has brought histone lactylation into focus as a novel epigenetic modification that plays a crucial role in IS. Under cerebral ischemia and hypoxia, lactate levels rise, providing an alternative energy source to support ischemic brain regions. Lactate also functions as a signaling molecule, regulating key processes such as neuroprotection, angiogenesis, and anti-inflammatory responses. This review synthesizes the current research on histone lactylation in IS, emphasizing its role in neuronal survival, inflammatory regulation, and blood-brain barrier integrity. A comprehensive literature review was conducted using databases such as PubMed, Scopus, and Web of Science, focusing on studies published from 2015 to 2023. Additionally, the review explores the potential of histone lactylation as a therapeutic target for post-stroke recovery. Histone lactylation can be targeted therapeutically through the use of specific inhibitors or enhancers of acetyltransferase and deacetylase enzymes, offering a promising strategy for modulating stroke-related cellular processes. By integrating recent findings, this review aims to provide new insights into ischemic stroke and promote the development of innovative therapeutic strategies.

Background: EGb 761, a standardized dry extract of Ginkgo biloba leaves, has certain anti-inflammatory and thrombotic effects and can be used to treat cerebrovascular diseases.

Methods: A total of 49 patients were randomly assigned to the Aspirin group (24 cases in Controlled group) and the Aspirin + Ginkgo biloba group (25 cases in Treatment group). The quantitative magnetic sensitivity and venous oxygen saturation of cerebral microbleeds were analyzed at admission, discharge, and after follow-up for 3 and 6 months.

Results: The demographic details age, gender, and admission to NIHSS were not significantly different between the two groups (p < 0.05). Quantitative susceptibility mapping (QSM) showed that the magnetic sensitivity of patients in both groups remained stable after 3 and 6 months of follow-up, while the venous oxygen saturation of the Treatment group increased. The venous oxygen saturation at 3 and 6 months of follow-up was negatively correlated with the modified mRS grade score.

Conclusions: QSM can be used as a quantitative follow-up tool in monitoring both oxygen saturation and Magnetic susceptibility of microbleeds noninvasively in ischemic stroke patients with cerebral microbleeds. EGB combined with Aspirin can improve blood oxygen saturation in those patients and this effect is particularly significant in the long-term efficacy of secondary prevention.

Objective: Acute Stanford Type A aortic dissection (AAAD) is a critical condition in vascular surgery, and total aortic arch replacement surgery is the preferred method to save patients' lives. In recent years, as clinical research has advanced, there has been a growing realization of the close association between poor postoperative outcomes in patients and neurological functional deficits. Neurological function monitoring is a medical technique used to evaluate and monitor the functional status of the nervous system.

Methods: This monitoring involves the assessment of various aspects of the nervous system, including but not limited to nerve conduction velocity, neuromuscular function, electroencephalographic activity, and sensory nerve transmission. Neurological function monitoring has broad clinical applications and can be used to diagnose and monitor many neurological disorders, helping physicians understand patients' neurological functional status and guide treatment plans. During the postoperative recovery process, neurological function monitoring can assist physicians in assessing the potential impact of surgery on the nervous system and monitor the recovery of patients' neurological function.

Results: Studies have shown that neurological function monitoring holds promise in predicting neurological functional prognosis and interventions for patients with aortic dissection.

Conclusion: Therefore, the primary objective of this study is to evaluate the effectiveness and reliability of various intraoperative neurological monitoring techniques, neuroimaging examinations, and biomarkers in predicting and assessing postoperative neurological outcomes in patients undergoing AAAD surgery.