Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations

Environmental enrichment: a neurostimulatory approach to aging and ischemic stroke recovery and rehabilitation

Environmental enrichment (EE) represents a robust experimental framework exploring the intricate interplay between genes and the environment in shaping brain development and function. EE is recognized as a non-invasive intervention, easily translatable to elderly human cohorts, and extrapolated from research on animal aging models. Age is the most important risk factor for ischemic stroke. Research indicates that EE, characterized by increased sensory, cognitive, and social stimulation, leads to structural changes in the brain, such as enhanced dendritic complexity and synaptic density, particularly in the hippocampus and cortex. Tailored EE interventions for elderly stroke survivors include cognitively stimulating activities and participation in social groups. These interventions enhance cognitive function and support recovery by promoting neural repair. Additionally, EE helps to mitigate sensory deficits commonly observed in older adults, ultimately improving mental performance and quality of life. EE has shown promise in preventing relapse, enhancing attention, reducing anxiety, forestalling age-related DNA methylation alterations, and amplifying neurogenesis through heightened neural progenitor cell (NPC) populations. Aligning preclinical studies with clinical trials can enhance neurorehabilitation conditions for stroke patients, thereby optimizing the environments in which they recover. This can be achieved through the concerted efforts of multidisciplinary teams working collaboratively. This review explores how EE specifically impacts the aging brain and ischemic stroke, a major age-related neurological disorder with global health implications. The potential of enviro-mimetics and relevant clinical studies on EE's effects on ischemic stroke survivors are discussed. This review enhances our understanding of the effects of EE on aging and ischemic stroke, motivating further research aimed at refining strategies for stroke management and recovery.

Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation

Ischemic stroke's complex pathophysiology demands therapeutic approaches targeting multiple pathways simultaneously, yet current treatments remain limited. We developed an innovative drug discovery pipeline combining a deep learning approach with experimental validation to identify natural compounds with comprehensive neuroprotective properties. Our computational framework integrated SELFormer, a transformer-based deep learning model, and multiple deep learning algorithms to predict NC bioactivity against seven crucial stroke-related targets (ACE, GLA, MMP9, NPFFR2, PDE4D, and eNOS). The pipeline encompassed IC50 predictions, clustering analysis, quantitative structure-activity relationship (QSAR) modeling, and uniform manifold approximation and projection (UMAP)-based bioactivity profiling followed by molecular docking studies and experimental validation. Analysis revealed six distinct NC clusters with unique molecular signatures. UMAP projection identified 11 medium-activity (6 < pIC50 ≤ 7) and 57 high-activity (pIC50 > 7) compounds, with molecular docking confirming strong correlations between binding energies and predicted pIC50 values. In vitro studies using NGF-differentiated PC12 cells under oxygen-glucose deprivation demonstrated significant neuroprotective effects of four high-activity compounds: feruloyl glucose, l-hydroxy-l-tryptophan, mulberrin, and ellagic acid. These compounds enhanced cell viability, reduced acetylcholinesterase activity and lipid peroxidation, suppressed TNF-α expression, and upregulated BDNF mRNA levels. Notably, mulberrin and ellagic acid showed superior efficacy in modulating oxidative stress, inflammation, and neurotrophic signaling. This study establishes a robust deep learning-driven framework for identifying multitarget natural therapeutics for ischemic stroke. The validated compounds, particularly mulberrin and ellagic acid, are promising for stroke treatment development. Our findings demonstrate the effectiveness of integrating computational prediction with experimental validation in accelerating drug discovery for complex neurological disorders.

The role of ACSL4 in stroke: mechanisms and potential therapeutic target

Stroke, as a neurological disorder with a poor overall prognosis, has long plagued the patients. Current stroke therapy lacks effective treatments. Ferroptosis has emerged as a prominent subject of discourse across various maladies in recent years. As an emerging therapeutic target, notwithstanding its initial identification in tumor cells associated with brain diseases, it has lately been recognized as a pivotal factor in the pathological progression of stroke. Acyl-CoA synthetase long-chain family member 4 (ACSL4) is a potential target and biomarker of catalytic unsaturated fatty acids mediating ferroptosis in stroke. Specifically, the upregulation of ACSL4 leads to heightened accumulation of lipid peroxidation products and reactive oxygen species (ROS), thereby exacerbating the progression of ferroptosis in neuronal cells. ACSL4 is present in various tissues and involved in multiple pathways of ferroptosis. At present, the pharmacological mechanisms of targeting ACSL4 to inhibit ferroptosis have been found in many drugs, but the molecular mechanisms of targeting ACSL4 are still in the exploratory stage. This paper introduces the physiopathological mechanism of ACSL4 and the current status of the research involved in ferroptosis crosstalk and epigenetics, and summarizes the application status of ACSL4 in modern pharmacology research, and discusses the potential application value of ACSL4 in the field of stroke.

A Bibliometric Analysis of Research Trends in Psychological Interventions for Stroke Survivors: Focusing on Resilience and Psychological Well-Being (2000-2024)

Purpose

This study aims to conduct a bibliometric analysis of research literature on psychological interventions for stroke survivors published from 2000 to 2024, focusing on psychological resilience and psychological well-being, utilizing VOSviewer and CiteSpace.

Patients and Methods

Literature data was sourced from the Web of Science Core Collection database (WoSCC). A total of 373 relevant articles published between January 1, 2000, and September 30, 2024, were included. CiteSpace and VOSviewer analyzed the literature from perspectives including authorship, country of origin, institutions, journals, references, and keywords.

Results

Annual publication output has steadily increased, reaching a peak in 2023 (55 articles). Vranceanu Ana-Maria emerged as the most productive author with eight publications. The United States led in research output (98 articles) and centrality (0.32), with Harvard University being the leading institution (24 articles). "Disability and Rehabilitation" was the most productive journal (12 articles), while "Stroke" was the most co-cited journal (241 times). Recent research trends emphasized meta-analysis (strength=3.6), assessment tool validation (strength=3.49), and acceptance-based interventions (strength=2.89), mainly focusing on psychological resilience cultivation and well-being promotion.

Conclusion

This bibliometric analysis reveals the increasing scholarly interest in psychological interventions for stroke survivors, particularly in resilience and psychological well-being research. The field has evolved from focusing on disease-related factors to systematic intervention research, with a growing emphasis on methodological standardization and individualized interventions. These findings provide significant theoretical and practical implications for improving psychological health services for stroke survivors. Future research should strengthen high-quality empirical studies, refine assessment tools, and innovate intervention strategies to better address the complex psychological needs of stroke survivors and enhance their resilience and psychological well-being, ultimately improving rehabilitation outcomes and quality of life for stroke survivors.

Research progress on influencing factors and intervention measures of pre-hospital delays in acute ischemic stroke

BackgroundStroke, a leading cause of health impairment globally, sees intravenous thrombolysis as the primary treatment during the acute phase, yet delays persist due to pre-hospital and in-hospital factors. While research has reduced in-hospital delays significantly, pre-hospital delays remain a concern both domestically and internationally.ObjectiveThis article aims to provide a comprehensive review of the research progress on the influencing factors and intervention measures of pre-hospital delays in acute ischemic stroke.MethodsBy analyzing the literature, summarize the risk factors leading to treatment delay in acute ischemic stroke (AIS), and provide a review of potential improvement methods.ResultsPre-hospital delay in acute ischemic stroke (AIS) is influenced by both objective factors like age, gender, and regional economic status, as well as subjective factors such as stroke awareness. The introduction of "Stroke 120," a stroke education slogan tailored to Chinese language habits, aims to improve stroke awareness and address delayed treatment and low AIS venous thrombolysis utilization among the Chinese publicConclusionIn conclusion, collaborative efforts from the government, society, and hospitals are essential to enhance stroke education comprehensively. This will ensure widespread awareness of stroke knowledge, facilitating timely and effective treatment for AIS patients.

Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans

BACKGROUND

The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.

NEW METHOD

In this study, we explore the use of fully automated methods relying on state-of-the-art Deep Learning approaches to produce these annotations. More specifically, we propose a new segmentation framework made of a set of encoder-decoder blocks embedding self-attention mechanisms and atrous spatial pyramidal pooling to produce better maps of the hippocampus and identify four hippocampal regions: the dentate gyrus, the hippocampal head, the hippocampal body, and the hippocampal tail.

RESULTS

Trained using slices extracted from 15 postmortem T1-weighted, T2-weighted, and susceptibility-weighted MRI scans, our new approach produces hippocampus parcellations that are better aligned with the manually delineated parcellations provided by neuroradiologists.

COMPARISON WITH EXISTING METHODS

Four standard deep learning segmentation architectures: UNet, Double UNet, Attention UNet, and Multi-resolution UNet have been utilized for the qualitative and quantitative comparison of the proposed hippocampal region segmentation model.

CONCLUSIONS

Postmortem MRI serves as a highly valuable neuroimaging technique for examining the effects of neurodegenerative diseases on the intricate structures within the hippocampus. This study opens the way to large sample-size postmortem studies of the hippocampal substructures.

Comparison of Five Rehabilitation Interventions for Acute Ischemic Stroke: A Randomized Trial

Background: Comparative efficacy of rehabilitation interventions in persons with acute ischemic stroke (PwS) is limited. This randomized trial assessed the immediate and lasting effects of five interventions on clinical and mobility outcomes in 75 PwS. Methods: Five days after stroke, 75 PwS were randomized into five groups: physical therapy (CON, standard care, once daily); walking with a soft robotic exoskeleton (ROB, once daily); agility exergaming once (EXE1, once daily) or twice daily (EXE2, twice daily); and combined EXE1+ROB in two daily sessions. Interventions were performed 5 days per week for 3 weeks. Outcomes were assessed at baseline, post-intervention, and after 5 weeks of detraining. Results: Modified Rankin Scale (primary outcome) and Barthel Index showed no changes. EXE1, EXE2, ROB, and EXE1+ROB outperformed standard care (CON) in five secondary outcomes (Berg balance scale, 10m walking speed, 6-min walk test with/without robot, standing balance), with effects sustained after 5 weeks. Dose effects (EXE1 vs. EXE2) were minimal, while EXE1+ROB showed additive effects in 6-min walk tests. Conclusions: These novel comparative data expand evidence-based options for therapists to design individualized rehabilitation plans for PwS. Further confirmation is needed.

Tetrahedral Framework Nucleic Acid-Based Delivery System for miRNA-124 and Stroke-Homing Peptide: Targeted Therapy for Acute Ischemic Stroke

Acute ischemic stroke (AIS) is associated with a high mortality rate and poor prognosis, with a lack of effective therapeutic drugs for post-thrombolytic treatment. MicroRNA-based gene therapy is a promising approach for treating AIS, but its clinical application has been limited due to challenges, such as poor targeting efficiency, unsatisfactory stability, and inadequate cellular uptake. In this study, we successfully developed a microRNA-targeted delivery system based on the tetrahedral framework nucleic acid (tFNA). This nanodelivery system, guided by stroke-homing peptides, effectively targeted and delivered miRNA124 to the ischemic hemisphere. With the assistance of tFNA, miRNA124 efficiently entered cells and exerted therapeutic effects. Additionally, it promoted the transformation of microglia from the pro-inflammatory M1 phenotype to the anti-inflammatory M2 phenotype, reducing neuronal apoptosis and, ultimately, decreasing the infarct size and mortality rate. These findings present a promising therapeutic strategy for the targeted treatment of AIS.

Evaluation of the Prevalence of Sleep Disorders and Their Association with Stroke: A Hospital-Based Retrospective Study

Background: Sleep disturbances are prevalent among stroke survivors, significantly impacting their recovery and quality of life. This study aimed to evaluate the prevalence of sleep disorders, sleep quality, risk of obstructive sleep apnea (OSA), and daytime sleepiness among stroke survivors and to identify potential associations with clinical and demographic factors. Materials and Methods: A retrospective observational study analyzed adult stroke survivors (aged ≥ 18 years) attending neurology clinics at our institution from November 2022 to November 2024. The primary outcome measures included overall sleep quality, sleep apnea and daytime sleepiness assessment. Data were collected using validated Arabic versions of the Pittsburgh Sleep Quality Index (PSQI), STOP-Bang Questionnaire, and Epworth Sleepiness Scale (ESS). Statistical analyses, including Chi-square tests and t-tests, were performed using SPSS version 30.1. Results: A total of 100 stroke survivors, mostly aged 40-60 years, were recruited in our study. The prevalence of sleep disorders was 60.0%, with poor sleep quality reflected by a mean global PSQI score of 9.13 ± 14.40. Additionally, 19.0% were at high risk of OSA, and 24.0% experienced abnormal daytime sleepiness. While no statistically significant associations were found between sleep disorders and clinical or demographic factors, trends indicated higher sleep disorder prevalence in those with hemorrhagic stroke and high-risk OSA profiles. Conclusions: Our study highlights a high prevalence of sleep disorders among stroke survivors, emphasizing the need for regular sleep assessments. Future studies should explore objective assessments and larger sample sizes to validate these findings and to assess their potential implication in stroke recovery and quality of life.

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. Traditional stroke risk assessments, relying on sporadic clinical visits, fail to capture dynamic changes in risk factors such as hypertension and atrial fibrillation (AF). Wearable technology (devices), combined with biometric data analysis, offers a transformative approach by enabling continuous monitoring of physiological parameters. This narrative review was conducted using a systematic approach to identify and analyze peer-reviewed articles, reports, and case studies from reputable scientific databases. The search strategy focused on articles published between 2010 till date using pre-determined keywords. Relevant studies were selected based on their focus on wearable devices and AI-driven technologies in stroke prevention, diagnosis, and rehabilitation. The selected literature was categorized thematically to explore applications, opportunities, challenges, and future directions. The review explores the current landscape of wearable devices in stroke risk assessment, focusing on their role in early detection, personalized care, and integration into clinical practice. The review highlights the opportunities presented by continuous monitoring and predictive analytics, where AI-driven algorithms can analyze biometric data to provide tailored interventions. Personalized stroke risk assessments, powered by machine learning, enable dynamic and individualized care plans. Furthermore, the integration of wearable technology with telemedicine facilitates remote patient monitoring and rehabilitation, particularly in underserved areas. Despite these advances, challenges remain. Issues such as data accuracy, privacy concerns, and the integration of wearables into healthcare systems must be addressed to fully realize their potential. As wearable technology evolves, its application in stroke care could revolutionize prevention, diagnosis, and rehabilitation, improving patient outcomes and reducing the global burden of stroke.

Association of stent thrombectomy and conventional treatment with neuroprotection, complications, anxiety, and depression in acute ischemic stroke patients

BACKGROUND

Acute ischemic stroke (AIS) is an abrupt blood flow cessation to a specific brain region within a vascular zone, causing a subsequent decline in neurological capabilities. Stent thrombectomy is a recently established technique for treating AIS. It provides the benefits of being a relatively simple and safe procedure, capable of partially enhancing a patient's condition. However, some patients may experience endothelial damage and recurrent thrombosis, with clinical outcomes that are not always satisfactory. Hence, the efficacy of this method remains unclear.

AIM

To survey the association of stent thrombectomy vs standard treatment with neurological function protection, complications, and short-term prognosis in patients diagnosed with AIS.

METHODS

This study assigned 90 patients with AIS to the observation and control groups (n = 45 patients) from December 2020 to December 2022. Stent thrombectomy was conducted in the observation group, whereas routine treatment was provided to the control group. The study assessed the therapeutic outcomes of two groups, including a comparison of their neurological function, living ability, anxiety and depression status, plaque area, serum inflammatory factors, serum Smur100 β protein, neuron-specific enolase (NSE), homocysteine (Hcy), and vascular endothelial function. Additionally, the incidence of complications was calculated and analyzed for each group.

RESULTS

The total effective rate of treatment was 77.78% and 95.56% in the control and observation groups, respectively. After 8 weeks of treatment, the scores on the National Institutes of Health Stroke Scale, Hamilton Anxiety Scale, and Hamilton Depression Scale decreased remarkably; the Barthel index increased remarkably, with better improvement effects of the scores in the observation group (P < 0.05); total cholesterol, triglyceride, C-reactive protein, and plaque area lessened remarkably, with fewer patients in the observation group (P < 0.05); S-100β protein, NSE, and Hcy levels lessened remarkably, with fewer patients in the observation group (P < 0.05); serum vascular endothelial growth factor and nitric oxide synthase levels increased remarkably, whereas the endothelin-1 level decreased, with better improvement effect in the observation group (P < 0.05). Complications occurred in 8.88% of patients in the observation group compared with 33.33% in the control group.

CONCLUSION

Stent thrombectomy appeared to provide more remarkable neuroprotective effects in patients with AIS compared to the intravenous thrombolysis regimen. Additionally, it has effectively improved the neurological function, daily activities, and vascular endothelial function of patients, while reducing the incidence of complications and improving short-term prognosis.

Breaking Barriers in Stroke Therapy: Recent Advances and Ongoing Challenges

Acute ischemic stroke remains a significant global health challenge, prompting notable advancements in its management. Diagnostic innovations, including advanced imaging techniques and biomarker discovery, have greatly improved early detection and treatment precision. Mobile stroke units have expedited pre-hospital care, emphasizing the urgency of intervention under the principle "time is brain." These developments underscore the importance of timely and accurate diagnosis in improving outcomes. Therapeutic breakthroughs in thrombolytic therapy and mechanical thrombectomy have transformed acute stroke treatment. Refined thrombolytic protocols, new agents, and extended treatment windows have expanded therapeutic possibilities. Mechanical thrombectomy, bolstered by improved devices and techniques, has achieved higher recanalization rates and broader applicability, solidifying its role in endovascular interventions. Meanwhile, post-stroke care has embraced personalized, multidisciplinary rehabilitation approaches, with telemedicine breaking geographical barriers and enhancing recovery through patient-centered models. Despite these advancements, challenges persist, including disparities in access to advanced care and the underexplored management of minor strokes. Emerging technologies, such as artificial intelligence and machine learning, offer opportunities for better risk prediction and earlier interventions, though their long-term impacts require further study. This review highlights the transformative progress in stroke care while emphasizing the need for continued innovation, research, and equitable access to ensure better outcomes for all patients.

NEAT1 Promotes Valproic Acid-Induced Autism Spectrum Disorder by Recruiting YY1 to Regulate UBE3A Transcription

Evidence suggests that long non-coding RNAs (lncRNAs) play a significant role in autism. Herein, we explored the functional role and possible molecular mechanisms of NEAT1 in valproic acid (VPA)-induced autism spectrum disorder (ASD). A VPA-induced ASD rat model was constructed, and a series of behavioral tests were performed to examine motor coordination and learning-memory abilities. qRT-PCR and western blot assays were used to evaluate target gene expression levels. Loss-and-gain-of-function assays were conducted to explore the functional role of NEAT1 in ASD development. Furthermore, a combination of mechanistic experiments and bioinformatic tools was used to assess the relationship and regulatory role of the NEAT1-YY1-UBE3A axis in ASD cellular processes. Results showed that VPA exposure induced autism-like developmental delays and behavioral abnormalities in the VPA-induced ASD rat model. We found that NEAT1 was elevated in rat hippocampal tissues after VPA exposure. NEAT1 promoted VPA-induced autism-like behaviors and mitigated apoptosis, oxidative stress, and inflammation in VPA-induced ASD rats. Notably, NEAT1 knockdown improved autism-related behaviors and ameliorated hippocampal neuronal damage. Mechanistically, it was observed that NEAT1 recruited the transcription factor YY1 to regulate UBE3A expression. Additionally, in vitro experiments further confirmed that NEAT1 knockdown mitigated hippocampal neuronal damage, oxidative stress, and inflammation through the YY1/UBE3A axis. In conclusion, our study demonstrates that NEAT1 is highly expressed in ASD, and its inhibition prominently suppresses hippocampal neuronal injury and oxidative stress through the YY1/UBE3A axis, thereby alleviating ASD development. This provides a new direction for ASD-targeted therapy.

Predictive Value of Inflammatory Scores for Left Atrium Thrombosis in Ischemic Stroke Without Atrial Fibrillation

Background and Objectives: Studies have shown that inflammation markers can be used as prognostic tools in predicting acute ischemic stroke. In this study, we conducted a comparison of several inflammation scores in predicting left atrial thrombosis (LAT) in patients with ischemic stroke without AF. Materials and Methods: In this single-center, retrospective study, we included 303 consecutive patients with ischemic stroke. Each patient underwent a transesophageal echocardiography (TEE) examination within 10 days of admission to detect the presence of LAT. To identify independent predictors of LAT, we conducted a multivariate logistic regression analysis. Results: In total, 303 patients who had ischemic stroke were included in the analysis. LAT was detached in 34 patients at the time of the TEE examination. The patients were categorized into two groups based on their LAT status. The Prognostic Nutritional Index (PNI), HALP score, and C-reactive Protein-Albumin Ratio (CAR) were identified as statistically significant predictors of LAT. Based on the results of the multivariate regression analysis, the CAR emerged as the only independent predictor of LAT. Conclusions: Among several inflammation scores, the PNI, HALP, and CAR were statistically significant predictors of LAT in ischemic stroke patients without AF. CAR was identified as the optimal score for the prediction of LAT in patients with stroke and without AF.

Emerging Trends: Neurofilament Biomarkers in Precision Neurology

Neurofilaments are structural proteins found in the cytoplasm of neurons, particularly in axons, providing structural support and stability to the axon. They consist of multiple subunits, including NF-H, NF-M, and NF-L, which form long filaments along the axon's length. Neurofilaments are crucial for maintaining the shape and integrity of neurons, promoting axonal transport, and regulating neuronal function. They are part of the intermediate filament (IF) family, which has approximately 70 tissue-specific genes. This diversity allows for a customizable cytoplasmic meshwork, adapting to the unique structural demands of different tissues and cell types. Neurofilament proteins show increased levels in both cerebrospinal fluid (CSF) and blood after neuroaxonal damage, indicating injury regardless of the underlying etiology. Precise measurement and long-term monitoring of damage are necessary for determining prognosis, assessing disease activity, tracking therapeutic responses, and creating treatments. These investigations contribute to our understanding of the importance of proper NF composition in fundamental neuronal processes and have implications for neurological disorders associated with NF abnormalities along with its alteration in different animal and human models. Here in this review, we have highlighted various neurological disorders such as Alzheimer's, Parkinson's, Huntington's, Dementia, and paved the way to use neurofilament as a marker in managing neurological disorders.

Exploring the Research Landscape of Transcranial Direct Current Stimulation in Stroke: A Bibliometric Review

Transcranial direct current stimulation (tDCS) has gained significant attention as a potential therapeutic tool in stroke rehabilitation, promoting neuroplasticity and enhancing motor and cognitive recovery. Despite growing research, the field's evolution and key trends remain underexplored. This study aims to perform a bibliographic analysis of publications related to tDCS and stroke rehabilitation to assess the growth of the field. Published literature was searched in PubMed and Web of Science (WOS) on 10 December 2024. We used the keywords "transcranial direct current stimulation" and "stroke" to collect studies without any time limitation. Articles found in WOS were used to get trends, and a search from PubMed was used to analyze co-occurrences in VOSviewer, version 1.6.20 (Centre for Science and Technology Studies, Leiden University, the Netherlands). A total of 1,598 articles were found in WOS, and 1,300 were found in PubMed. As there was overlapping of subject categories, countries, and affiliations, the total number differed in calculating the percentages. The analysis revealed significant growth in publications on tDCS and stroke rehabilitation, peaking at 137 publications in 2022. Most studies focus on neuroscience (860, or 30.6%), clinical neurology (562, or 20%), and rehabilitation (244, or 8.68%). The United States leads contributions (519, or 22.4%), followed by Germany (249, or 10.7%) and China (179, or 7.7%). Publications are concentrated among major publishers like Elsevier (358, or 22.4%) and key journals such as Brain Stimulation and Frontiers in Human Neuroscience. English dominates as the primary language (1,572, or 98.37%). Research emphasizes tDCS's role in motor recovery and brain plasticity in stroke rehabilitation. This bibliometric analysis highlights a substantial and growing interest in tDCS for stroke rehabilitation, with a steady increase in publications. The focus of research predominantly lies in neuroscience, clinical neurology, and rehabilitation, reflecting the central role of tDCS in advancing stroke recovery and brain plasticity. The concentration of publications among major publishers and journals underscores the prominence of specific platforms in disseminating tDCS research. More research from developing countries is needed to achieve a balanced geographical diversity on this topic.

Stanford Type A Aortic Dissection Masquerading as Acute Ischemic Stroke: A Case Report

Aortic dissection (AD), a life-threatening cardiovascular emergency, is characterized by the separation of the aorta's inner and middle layers due to a tear in the intima. It is classified as Stanford type A or B based on the tear's location and extent. Symptoms vary but commonly include severe pain in the chest, back, or abdomen, along with atypical presentations such as shock, heart failure, or syncope. End-organ ischemia, including stroke and limb necrosis, may occur. Timely diagnosis and intervention are crucial for survival. Here, we report a 31-year-old male patient who presented with acute neurological symptoms, initially suspected of having a stroke, but was ultimately diagnosed with Stanford type A AD upon computed tomography (CT) angiography. This case underscores the importance of considering AD in the differential diagnosis of patients with neurological symptoms for accurate and prompt management.

Research progress of traditional Chinese medicine in the treatment of ischemic stroke by regulating mitochondrial dysfunction

Ischemic stroke (IS) is a severe cerebrovascular disease with increasing incidence and mortality rates in recent years. The pathogenesis of IS is highly complex, with mitochondrial dysfunction playing a critical role in its onset and progression. Thus, preserving mitochondrial function is a pivotal aspect of treating ischemic brain injury. In response, there has been growing interest among scholars in the regulation of mitochondrial function through traditional Chinese medicine (TCM), including herb-derived compounds, individual herbs, and herbal prescriptions. This article reviews recent research on the mechanisms of mitochondrial dysfunction in IS and explores the potential of TCM in treating this condition by targeting mitochondrial dysfunction.

Global trends in ischemic stroke burden attributable to high BMI

We aimed to assess the worldwide burden of ischemic stroke (IS) associated with high body mass index (BMI) using data from the Global Burden of Disease 2019. This study examined the impact of high BMI on IS-related age-standardized death rates (ASDR) and disability-adjusted life years (DALYs). Estimated annual percentage changes (EAPC) is estimated annual percentage change. Trends were assessed using EAPCs. Over the past 3 decades, there has been a declining trend in the global burden of IS associated with high BMI, especially in Western Europe (EAPC = -3.09 for DALYs) and high-income Asia Pacific (EAPC = -4.6 for ASDR). However, certain developing regions, notably Southeast Asia, have experienced significant increases in ASDR (EAPC = 3.7) and age-standardized DALY rates (EAPC = 3.64). The most substantial increase in burden was observed in Southeast Asia for both males (EAPC = 3.85) and females (EAPC = 3.53). Importantly, the burden was most pronounced in regions with low to middle sociodemographic index. The rising disease burden linked to high BMI is largely due to insufficient medical infrastructure and ineffective public health policies in the region. Urgent action is needed from decision-makers to improve these areas and implement effective interventions. This study reveals a consistent global decline in IS related to high BMI, with a more significant burden observed in males under the age of 65, particularly in Southeast Asia, where increases in IS associated with high BMI are notable. Public health officials and global policymakers need timely and reliable quantitative data. This information is essential for implementing effective behavioral interventions, such as monitoring diet and physical activity, to address identified risk factors and reduce the burden of high BMI.

Evaluating the Effectiveness of Neuroprotective Strategies in Enhancing Post-stroke Recovery: A Systematic Review of Meta-Analyses and Clinical Trials

This systematic review evaluates the effectiveness of various neuroprotective strategies in enhancing recovery following acute ischemic stroke, focusing on interventions such as normobaric oxygen (NBO), lithium, selective serotonin reuptake inhibitors (SSRIs), and Cerebrolysin. Drawing upon data from six primary studies, including randomized controlled trials (RCTs) and meta-analyses, we assessed these therapies' impact on functional outcomes, motor recovery, and neurological improvement. Normobaric oxygen, across 12 RCTs, demonstrated limited efficacy in improving recovery outcomes or reducing mortality. Lithium, supported by animal models but with inconclusive human data, showed potential in reducing stroke volume but did not significantly enhance functional recovery in clinical trials. SSRIs, particularly fluoxetine, showed moderate success in improving motor recovery, as evidenced by the FLAME (Fluoxetine for Motor Recovery after Acute Ischaemic Stroke) trial and meta-analyses. Cerebrolysin demonstrated consistent improvement in early neurological function and motor recovery, with a number-needed-to-treat (NNT) of 7.1 for early NIHSS (National Institutes of Health Stroke Scale) score improvements. Our Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided search covered PubMed, Medline, Embase, and the Cochrane Library up to September 2024. These findings emphasize the mixed efficacy of these neuroprotective interventions and underscore the necessity for personalized treatment protocols and further large-scale, controlled trials to clarify their roles in clinical practice. This review contributes to the ongoing dialogue on optimizing post-stroke recovery and highlights the critical need for evidence-based neuroprotective strategies.

Radiomics model based on dual-energy CT can determine the source of thrombus in strokes with middle cerebral artery occlusion

PURPOSE

To develop thrombus radiomics models based on dual-energy CT (DECT) for predicting etiologic cause of stroke.

METHODS

We retrospectively enrolled patients with occlusion of the middle cerebral artery who underwent computed tomography (NCCT) and DECT angiography (DECTA). 70 keV virtual monoenergetic images (simulate conventional 120kVp CTA images) and iodine overlay maps (IOM) were reconstructed for analysis. Five logistic regression radiomics models for predicting cardioembolism (CE) were built based on the features extracted from NCCT, CTA and IOM images. From these, the best one was selected to integrate with clinical information for further construction of the combined model. The performance of the different models was evaluated and compared using ROC curve analysis, clinical decision curves (DCA), calibration curves and Delong test.

RESULTS

Among all the radiomic models, model NCCT+IOM performed the best, with AUC = 0.95 significantly higher than model NCCT, model CTA, model IOM and model NCCT+CTA in the training set (AUC = 0.88, 0.78, 0.90,0.87, respectively, P < 0.05), and AUC = 0.92 in the testing set, significantly higher than model CTA (AUC = 0.71, P < 0.05). Smoking and NIHSS score were independent predictors of CE (P < 0.05). The combined model performed similarly to the model NCCT+IOM, with no statistically significant difference in AUC either in the training or test sets. (0.96 vs. 0.95; 0.94 vs. 0.92, both P > 0.05).

CONCLUSION

Radiomics models constructed based on NCCT and IOM images can effectively determine the source of thrombus in stroke without relying on clinical information.

Non-surgical nursing care for tumor patients: an overview of sedation, analgesia, and recent innovations

With the increasing prevalence of tumors, effective symptom management has emerged as a cornerstone of patient care. While surgical interventions remain pivotal, non-surgical nursing methods have gained prominence in providing relief from pain, discomfort, and other tumor-related symptoms. This review delves into the various non-surgical approaches employed, emphasizing tumor sedation and analgesia. We discuss the array of non-pharmacological and pharmacological strategies, shedding light on their indications, contraindications, and potential side effects. Furthermore, the importance of addressing individual differences in pain perception and the ethical considerations in symptom management are highlighted. We conclude by providing insights into the recent innovations in the field, emphasizing the need for personalized and comprehensive care to enhance patients' quality of life. Tumor sedation, Tumor analgesia, Non-surgical nursing care, Pain management, Non-pharmacological interventions, Palliative care, Recent innovations, Symptom management.

Understanding Amyotrophic Lateral Sclerosis: Pathophysiology, Diagnosis, and Therapeutic Advances

This review offers an in-depth examination of amyotrophic lateral sclerosis (ALS), addressing its epidemiology, pathophysiology, clinical presentation, diagnostic techniques, and current as well as emerging treatments. The purpose is to condense key findings and illustrate the complexity of ALS, which is shaped by both genetic and environmental influences. We reviewed the literature to discuss recent advancements in understanding molecular mechanisms such as protein misfolding, mitochondrial dysfunction, oxidative stress, and axonal transport defects, which are critical for identifying potential therapeutic targets. Significant progress has been made in refining diagnostic criteria and identifying biomarkers, leading to earlier and more precise diagnoses. Although current drug treatments provide some benefits, there is a clear need for more effective therapies. Emerging treatments, such as gene therapy and stem cell therapy, show potential in modifying disease progression and improving the quality of life for ALS patients. The review emphasizes the importance of continued research to address challenges such as disease variability and the limited effectiveness of existing treatments. Future research should concentrate on further exploring the molecular foundations of ALS and developing new therapeutic approaches. The implications for clinical practice include ensuring the accessibility of new treatments and that healthcare systems are equipped to support ongoing research and patient care.

Stroke rehabilitation: from diagnosis to therapy

Stroke remains a significant global health burden, necessitating comprehensive and innovative approaches in rehabilitation to optimize recovery outcomes. This paper provides a thorough exploration of rehabilitation strategies in stroke management, focusing on diagnostic methods, acute management, and diverse modalities encompassing physical, occupational, speech, and cognitive therapies. Emphasizing the importance of early identification of rehabilitation needs and leveraging technological advancements, including neurostimulation techniques and assistive technologies, this manuscript highlights the challenges and opportunities in stroke rehabilitation. Additionally, it discusses future directions, such as personalized rehabilitation approaches, neuroplasticity concepts, and advancements in assistive technologies, which hold promise in reshaping the landscape of stroke rehabilitation. By delineating these multifaceted aspects, this manuscript aims to provide insights and directions for optimizing stroke rehabilitation practices and enhancing the quality of life for stroke survivors.

Proteomics profiling of extracellular vesicle for identification of potential biomarkers in Alzheimer's disease: A comprehensive review

The intricate origins and diverse symptoms of Alzheimer's disease (AD) pose significant challenges for both diagnosis and treatment. Exosomes and microvesicles, which carry disease-specific cargo from a variety of central nervous system cell types, have emerged as promising reservoirs of biomarkers for AD. Research on the screening of possible biomarkers in Alzheimer's disease using proteomic profiling of EVs is systematically reviewed in this comprehensive review. We highlight key methodologies employed in EV isolation, characterization, and proteomic analysis, elucidating their advantages and limitations. Furthermore, we summarize the evolving landscape of EV-associated biomarkers implicated in AD pathogenesis, including proteins involved in amyloid-beta metabolism, tau phosphorylation, neuroinflammation, synaptic dysfunction, and neuronal injury. The literature review highlights the necessity for robust validation strategies and standardized protocols to effectively transition EV-based biomarkers into clinical use. In the concluding section, this review delves into potential future avenues and technological advancements pivotal in crafting EV-derived biomarkers applicable to AD diagnostics and prognostics. This review contributes to our comprehension of AD pathology and the advancement of precision medicine in neurodegenerative diseases, hinting at a promising era in AD precision medicine.

Stem cell grafts enhance endogenous extracellular vesicle expression in the stroke brain

Endogenous brain repair occurs following an ischemic stroke but is transient, thus unable to fully mount a neuroprotective response against the evolving secondary cell death. Finding a treatment strategy that may render robust and long-lasting therapeutic effects stands as a clinically relevant therapy for stroke. Extracellular vesicles appear to be upregulated after stroke, which may represent a candidate target for neuroprotection. In this study, we probed whether transplanted stem cells could enhance the expression of extracellular vesicles to afford stable tissue remodeling in the ischemic stroke brain. Aged rats were initially exposed to the established ischemic stroke model of middle cerebral artery occlusion then received intravenous delivery of either bone marrow-derived mesenchymal stem cell transplantation or vehicle. A year later, the animals were assayed for brain damage, inflammation, and extracellular vesicle expression. Our findings revealed that while core infarction was not reduced, the stroke animals transplanted with stem cells displayed a significant reduction in peri-infarct cell loss that coincided with downregulated Iba1-labeled inflammatory cells and upregulated CD63-positive extracellular vesicles that appeared to be co-localized with GFAP-positive astrocytes. Interestingly, grafted stem cells were not detected at one year post-transplantation period, suggesting that the extracellular vesicles likely originated within the host brain. That long-lasting functional benefits persisted in the absence of surviving transplanted stem cells, but with upregulation of endogenous extracellular vesicles, advances the concept that transplantation of stem cells acutely after stroke propels host extracellular vesicles to the ischemic brain, altogether promoting chronic brain remodeling.

Polymeric nanocarriers delivery systems in ischemic stroke for targeted therapeutic strategies

Ischemic stroke is a complex, high-mortality disease with multifactorial etiology and pathogenesis. Currently, drug therapy is mainly used treat ischemic stroke in clinic, but there are still some limitations, such as limited blood-brain barrier (BBB) penetration efficiency, a narrow treatment time window and drug side effects. Recent studies have pointed out that drug delivery systems based on polymeric nanocarriers can effectively improve the insufficient treatment for ischemic stroke. They can provide neuronal protection by extending the plasma half-life of drugs, enhancing the drug's permeability to penetrate the BBB, and targeting specific structures and cells. In this review, we classified polymeric nanocarriers used for delivering ischemic stroke drugs and introduced their preparation methods. We also evaluated the feasibility and effectiveness and discussed the existing limitations and prospects of polymeric nanocarriers for ischemic stroke treatment. We hoped that this review could provide a theoretical basis for the future development of nanomedicine delivery systems for the treatment of ischemic stroke.

Artificial intelligence in ischemic stroke images: current applications and future directions

This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.

NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

A Decade of Dedication: Pioneering Perspectives on Neurological Diseases and Mental Illnesses

Welcome to Biomedicines' 10th Anniversary Special Issue, a journey through the human mind's labyrinth and complex neurological pathways [...].

The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection

Cerebrovascular accidents (CVAs) often occur suddenly and abruptly, leaving patients with long-lasting disabilities that place a huge emotional and economic burden on everyone involved. CVAs result when emboli or thrombi travel to the brain and impede blood flow; the subsequent lack of oxygen supply leads to ischemia and eventually tissue infarction. The most important factor determining the prognosis of CVA patients is time, specifically the time from the onset of disease to treatment. Artificial intelligence (AI)-assisted neuroimaging alleviates the time constraints of analysis faced using traditional diagnostic imaging modalities, thus shortening the time from diagnosis to treatment. Numerous recent studies support the increased accuracy and processing capabilities of AI-assisted imaging modalities. However, the learning curve is steep, and huge barriers still exist preventing a full-scale implementation of this technology. Thus, the potential for AI to revolutionize medicine and healthcare delivery demands attention. This paper aims to elucidate the progress of AI-powered imaging in CVA diagnosis while considering traditional imaging techniques and suggesting methods to overcome adoption barriers in the hope that AI-assisted neuroimaging will be considered normal practice in the near future. There are multiple modalities for AI neuroimaging, all of which require collecting sufficient data to establish inclusive, accurate, and uniform detection platforms. Future efforts must focus on developing methods for data harmonization and standardization. Furthermore, transparency in the explainability of these technologies needs to be established to facilitate trust between physicians and AI-powered technology. This necessitates considerable resources, both financial and expertise wise which are not available everywhere.

Elevated troponin levels as a predictor of mortality in patients with acute stroke: a systematic review and meta-analysis

Background and Aim

The prognostic potential of cardiac troponin (cTn) in acute stroke patients has been a subject of ongoing debate. Our objective was to provide a comprehensive evidence for predicting mortality in acute stroke patients by using the elevated troponin levels.

Methods

We conducted an extensive literature search, including PubMed, EMbase, and Trip Databases, covering studies published up to September 30, 2023. We computed risk ratios (RR) with 95% confidence intervals (CIs), performed sensitivity analysis, and conducted trial sequential analysis (TSA).

Results

In total, 53 studies were analyzed, with 37 focusing on acute ischemic stroke (AIS), 11 on subarachnoid hemorrhage (SAH), and 7 on Intracerebral hemorrhage (ICH). Elevated cTn levels were significantly showed a higher predictive risk for In-hospital mortality in both AIS (RR=3.80, 95% CI; 2.82 to 5.12) as well as SAH (RR=2.23, 95% CI; 1.64 to 3.02). However, no significant predictive risk between elevated cTn levels and in-hospital mortality for ICH patients (RR=1.13, 95% CI: 0.46 to 2.79). A similar pattern was observed for elevated cTn levels, indicating an increased risk of last follow-up mortality for AIS (RR=2.41, 95% CI: 1.98 to 2.93) and SAH (RR=3.08, 95% CI: 2.25 to 4.21).

Conclusion

Elevated troponin levels can serve as a promising predictive marker for both in-hospital and last follow-up mortality in AIS and SAH patients but not in ICH patients. Further prospective studies are needed to validate our findings along with exploring the preventive management of mortality in acute stroke settings.

Correlation Between Oncostatin M and Acute Ischemic Stroke: A Case-Control Study

BACKGROUND

The expression of oncostatin M (OSM) has been studied in various diseases related to inflammatory response, but its implementation in acute ischemic stroke (AIS) remains to be explored.  Objective: The objective of this study is to assess the correlation between serum OSM expression and various aspects of AIS in a clinical setting.

MATERIALS AND METHOD

A single-centered case-control study was performed in the First Affiliate Hospital of Chongqing Medical University from October 2020 to March 2021. A total of 134 patients were enrolled in the AIS group and 34 healthy individuals were enrolled in the control group. Physical examinations were performed and venous blood samples were collected. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum OSM. Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, National Institutes of Health Stroke Scale (NIHSS) score, magnetic resonance imaging (MRI) scan, and modified Rankin scale (mRS) were used to assess the classification, etiology, severity, and prognosis of the AIS group. Assessments were done to analyze serum OSM expression based on sensitivity, etiology, severity, prognosis, and several risk factors of AIS. Regression models, correlation, and sensitivity tests were performed to explore the correlation of OSM expression with various aspects of AIS.

RESULTS

There was a statistically significant elevation of serum OSM expression in the AIS group (P<0.001). All AIS subgroups showed elevation in OSM level and statistically significant results were reflected in three subgroups. The area under the curve to differentiate AIS patients and control by serum OSM level was 0.747 (P<0.001), with the optimal cut-off value showing sensitivity at 58.82% and specificity at 75.37%. The elevation of serum OSM expression was proportional with severity, not proportional to the volume of infarct, and less elevated in the favorable outcome group. Serum OSM correlation with several risk factors of AIS was statistically significant in age, low-density lipoprotein, non-high-density lipoprotein, prothrombin time, and systolic blood pressure.

CONCLUSION

Serum OSM was expressed differently in correlation with various aspects of AIS. Our findings supported the initial hypothesis that OSM is correlated with various aspects of AIS in humans.