Tackling the glial scar in spinal cord regeneration: new discoveries and future directions

Axonal regeneration and functional recovery are poor after spinal cord injury (SCI), typified by the formation of an injury scar. While this scar was traditionally believed to be primarily responsible for axonal regeneration failure, current knowledge takes a more holistic approach that considers the intrinsic growth capacity of axons. Targeting the SCI scar has also not reproducibly yielded nearly the same efficacy in animal models compared to these neuron-directed approaches. These results suggest that the major reason behind central nervous system (CNS) regeneration failure is not the injury scar but a failure to stimulate axon growth adequately. These findings raise questions about whether targeting neuroinflammation and glial scarring still constitute viable translational avenues. We provide a comprehensive review of the dual role of neuroinflammation and scarring after SCI and how future research can produce therapeutic strategies targeting the hurdles to axonal regeneration posed by these processes without compromising neuroprotection

Neutrophil extracellular traps and long COVID

Post-acute COVID-19 sequelae, commonly known as long COVID, encompasses a range of systemic symptoms experienced by a significant number of COVID-19 survivors. The underlying pathophysiology of long COVID has become a topic of intense research discussion. While chronic inflammation in long COVID has received considerable attention, the role of neutrophils, which are the most abundant of all immune cells and primary responders to inflammation, has been unfortunately overlooked, perhaps due to their short lifespan. In this review, we discuss the emerging role of neutrophil extracellular traps (NETs) in the persistent inflammatory response observed in long COVID patients. We present early evidence linking the persistence of NETs to pulmonary fibrosis, cardiovascular abnormalities, and neurological dysfunction in long COVID. Several uncertainties require investigation in future studies. These include the mechanisms by which SARS-CoV-2 brings about sustained neutrophil activation phenotypes after infection resolution; whether the heterogeneity of neutrophils seen in acute SARS-CoV-2 infection persists into the chronic phase; whether the presence of autoantibodies in long COVID can induce NETs and protect them from degradation; whether NETs exert differential, organ-specific effects; specifically which NET components contribute to organ-specific pathologies, such as pulmonary fibrosis; and whether senescent cells can drive NET formation through their pro-inflammatory secretome in long COVID. Answering these questions may pave the way for the development of clinically applicable strategies targeting NETs, providing relief for this emerging health crisis

Using artificial intelligence to improve body iron quantification: A scoping review

This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies that utilize machine learning in iron-related disorders. The search revealed a wide range of machine learning algorithms used by different studies. Notably, most studies used a single data type. The studies varied in terms of sample sizes, participant ages, and geographical locations. AI's role in quantifying iron concentration is still in its early stages, yet its potential is significant. The question is whether AI-based diagnostic biomarkers can offer innovative approaches for screening, diagnosing, and monitoring of iron overload and anemia.Open Access funding provided by the Qatar National Library.Scopu

Cellular senescence in brain aging and cognitive decline

Cellular senescence is a biological aging hallmark that plays a key role in the development of neurodegenerative diseases. Clinical trials are currently underway to evaluate the effectiveness of senotherapies for these diseases. However, the impact of senescence on brain aging and cognitive decline in the absence of neurodegeneration remains uncertain. Moreover, patient populations like cancer survivors, traumatic brain injury survivors, obese individuals, obstructive sleep apnea patients, and chronic kidney disease patients can suffer age-related brain changes like cognitive decline prematurely, suggesting that they may suffer accelerated senescence in the brain. Understanding the role of senescence in neurocognitive deficits linked to these conditions is crucial, especially considering the rapidly evolving field of senotherapeutics. Such treatments could help alleviate early brain aging in these patients, significantly reducing patient morbidity and healthcare costs. This review provides a translational perspective on how cellular senescence plays a role in brain aging and age-related cognitive decline. We also discuss important caveats surrounding mainstream senotherapies like senolytics and senomorphics, and present emerging evidence of hyperbaric oxygen therapy and immune-directed therapies as viable modalities for reducing senescent cell burden

Machine learning-based prognostication of mortality in stroke patients

Objectives: Predicting stroke mortality is crucial for personalized care. This study aims to design and evaluate a machine learning model to predict one-year mortality after a stroke. Materials and methods: Data from the National Multiethnic Stroke Registry was utilized. Eight machine learning (ML) models were trained and evaluated using various metrics. SHapley Additive exPlanations (SHAP) analysis was used to identify the influential predictors. Results: The final analysis included 9840 patients diagnosed with stroke were included in the study. The XGBoost algorithm exhibited optimal performance with high accuracy (94.5%) and AUC (87.3%). Core predictors encompassed National Institutes of Health Stroke Scale (NIHSS) at admission, age, hospital length of stay, mode of arrival, heart rate, and blood pressure. Increased NIHSS, age, and longer stay correlated with higher mortality. Ambulance arrival and lower diastolic blood pressure and lower body mass index predicted poorer outcomes. Conclusions: This model's predictive capacity emphasizes the significance of NIHSS, age, hospital stay, arrival mode, heart rate, blood pressure, and BMI in stroke mortality prediction. Specific findings suggest avenues for data quality enhancement, registry expansion, and real-world validation. The study underscores machine learning's potential for early mortality prediction, improving risk assessment, and personalized care. The potential transformation of care delivery through robust ML predictive tools for Stroke outcomes could revolutionize patient care, allowing for personalized plans and improved preventive strategies for stroke patients. However, it is imperative to conduct prospective validation to evaluate its practical clinical effectiveness and ensure its successful adoption across various healthcare environments

Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke

Abstract Background Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management. Methods We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew’s correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance. Results The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value. Conclusion This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient’s clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings

Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning

(1) Objective: This study aimed to construct a machine learning model for predicting the prognosis of ischemic stroke patients who underwent thrombolysis, assessed through the modified Rankin Scale (mRS) score 90 days after discharge. (2) Methods: Data were sourced from Qatar’s stroke registry covering January 2014 to June 2022. A total of 723 patients with ischemic stroke who had received thrombolysis were included. Clinical variables were examined, encompassing demographics, stroke severity indices, comorbidities, laboratory results, admission vital signs, and hospital-acquired complications. The predictive capabilities of five distinct machine learning models were rigorously evaluated using a comprehensive set of metrics. The SHAP analysis was deployed to uncover the most influential predictors. (3) Results: The Support Vector Machine (SVM) model emerged as the standout performer, achieving an area under the curve (AUC) of 0.72. Key determinants of patient outcomes included stroke severity at admission; admission systolic and diastolic blood pressure; baseline comorbidities, notably hypertension (HTN) and coronary artery disease (CAD); stroke subtype, particularly strokes of undetermined origin (SUO); and hospital-acquired urinary tract infections (UTIs). (4) Conclusions: Machine learning can improve early prognosis prediction in ischemic stroke, especially after thrombolysis. The SVM model is a promising tool for empowering clinicians to create individualized treatment plans. Despite limitations, this study contributes to our knowledge and encourages future research to integrate more comprehensive data. Ultimately, it offers a pathway to improve personalized stroke care and enhance the quality of life for stroke survivors

Long-term functional outcomes and complications of microsurgical resection of brainstem cavernous malformations: a systematic review and meta-analysis

Brainstem cavernous malformations (CMs) encompass up to 20% of all intracranial CMs and are considered more aggressive than cerebral CMs because of their high annual bleeding rates. Microsurgical resection remains the primary treatment modality for CMs, but long-term functional outcomes and complications are heterogenous in the literature. The authors performed a systematic review on brainstem CMs in 4 databases: PubMed, EMBASE, Cochrane library, and Google Scholar. We included studies that reported on the long-term functional outcomes and complications of brainstem CMs microsurgical resection. A meta-analysis was performed and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The search yielded 4781 results, of which 19 studies met our inclusion criteria. Microsurgery was performed on 940 patients (mean age 35 years, 46.9% females). Most of the brainstem CMs were located in the pons ( n  = 475). The pooled proportions of improved, stable, and worsened functional outcomes after microsurgical resection of brainstem CMs were 56.7% (95% CI 48.4–64.6), 28.6% (95% CI 22.4–35.7), and 12.6% (95% CI 9.6–16.2), respectively. CMs located in the medulla were significantly ( p  = 0.003) associated with a higher proportion of improved outcome compared with those in the pons and midbrain. Complete resection was achieved in 93.3% (95% CI 89.8–95.7). The immediate postoperative complication rate was 37.2% (95% CI 29.3–45.9), with new-onset cranial nerve deficit being the most common complication. The permanent morbidity rate was 17.3% (95% CI 10.5–27.1), with a low mortality rate of 1% from the compiled study population during a mean follow-up of 58 months. Our analysis indicates that microsurgical resection of brainstem CMs can result in favorable long-term functional outcomes with transient complications in the majority of patients. Complete microsurgical resection of the CM is associated with a lower incidence of CM hemorrhage and the morbidity related to it