Astrocytes Regulate Brain State Transitions

Your competent? doctor has had years to figure how to use astrocytes to get you recovered, right? But DID NOTHING, RIGHT? Been fired yet? If not, your board of directors is completely fucking incompetent and has no idea how to run a stroke hospital!

Do you prefer your doctor and hospital incompetence NOT KNOWING? OR NOT DOING?

astrocytes (106 posts to June 2011)

Astrocytes Regulate Brain State Transitions

Review

Published: 27 June 2025

• Volume 50, article number 218, (2025)
• Cite this article

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• Alexei Verkhratsky, 
• Max Gippert & 
• Vladimir Parpura 

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Abstract

We celebrate the life of our close friend and collaborator, Arne Schousboe, by writing this prose of the role of astrocytes in distinct aspects of arousal. Most animals exhibit cyclic behavioural transitions between sleep and wakefulness. Highly interconnected and complex networks of neurones, which release neurotransmitters, particularly noradrenaline, that target astrocytes by volume transmission, support the arousal system. Astrocyte noradrenergic signalling pathways are intricately connected to energy metabolism, whereby noradrenaline stimulates metabolism and leads to changes in cellular morphology, which is consistent with the maturation, territorial reach and complexity of these glial cells. We briefly discuss historic hypotheses contributing to the ever-going notion that cellular morphology and function affect each other. The message is that astrocytes contribute to sleep-wake transitions through the regulation of homeostatic control; these glial cells are responsible for ionostasis, metabolism, biosynthesis and degradation of neurotransmitters and the regulation of microcirculation and interstitial fluid flow. By regulating brain homeostasis, astrocytes in turn affect neuronal activity in the context of sleep-arousal regulation. “It’s complicated.”^® (Arne Schousboe, Denmark).

Foreword

The purpose of this prose is to contribute to the Special Issue celebrating the life and glory of our close friend and collaborator Professor Arne Schousboe (Fig. 1). As our memories have faded on when exactly in the last millennium we get to meet and befriend Arne, we (AV and VP, as MG had not had privilege to meet Arne) will remember, to the last breath we take, his trademark declarative sentence: “It’s complicated.” Namely, we learn most, if not all, we know on metabolism in astrocytes and the brain from Arne. We unequivocally trusted his expertise on the subject. Arne had an incredible ability to explain to us metabolism in terms that we understand, yet challenging us to learn more. We fondly remember our discussions on glutamate and ATP on the crossroads of signalling and metabolism at various places be that on a funicular railway ride to idyllic grounds of Ljubljana Castle in Slovenia or by enjoying a twilight in Heraklion on the island of Crete. Gastronomical pairings on those occasions might have been explorations of our metabolism. Suffice to say, we (AV and VP) commemorated some of our discussions in a triptych of reviews [1,2,3] and by co-editing a book in Advances in Neurobiology with Arne [4]. During these discussions, we learned that metabolism is more complex than our naïve understanding of it at the time. We incorporated metabolism in our lectures on its interface with signalling in and by astrocytes. In a friendly manner, Arne would frequently remind us of additional complexity, which challenged us to learn more. The write up that follows on global states in the brain, which require metabolism and energy to power them. While we did our best to present the subject and refer to metabolic needs, we know very well that “It’s complicated.”^® (Arne Schousboe, Denmark).

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A Common Assumption About Aging May Be Wrong, Study Suggests

 What will your competent? doctor do with this for you to have healthy aging? 

A Common Assumption About Aging May Be Wrong, Study Suggests

Early psychological therapy effective against depression and anxiety in stroke survivors: study

You don't need psychological therapy if you solve the primary problem by creating 100% recovery protocols!  Needing this means you're a COMPLETE FAILURE AS A STROKE HOSPITAL! Are you that stupid you can't see the solution in front of your face?

 Early psychological therapy effective against depression and anxiety in stroke survivors: study

Research progress in the use of botulinum toxin type a for post-stroke spasticity rehabilitation: a narrative review

More useless crapola that gets us no closer to curing spasticity!

You'll want spasticity cured when you are the 1 in 4 per WHO that has a stroke! And then it will be too late! I shouldn't hope schadenfreude hits you with spasticity, but I'm not that good of a person! So I hope like hell you suffer spasticity, just as much as Dr. William M. Landau with his absolutely idiotic opinions on spasticity!  

His statement from here:

Spasticity After Stroke: Why Bother? Aug. 2004 

The latest here:

 Research progress in the use of botulinum toxin type a for post-stroke spasticity rehabilitation: a narrative review

Zongjun Zhu,Yuanyuan Guan Ya Wang Article: 2521427 Received 26 Oct 2024 Accepted 27 May 2025 Published online: 23 Jun 2025 Cite this article 

Abstract

Background 

Stroke is a leading cause of long-term disability and death worldwide. Spasticity after stroke seriously affects patients’ quality of life. If this state persists for a long time, it will lead to severe joint atrophy, reduced motor coordination, and even permanent disability. Therefore, clinical research has focused on the treatment of spasticity and the recovery of motor function after stroke.AimThe aim of this paper is to explore the use of botulinum toxin type A in the rehabilitation of spasticity after stroke and to provide a theoretical basis for optimizing rehabilitation strategies, highlighting its potential value in reducing spasticity and improving motor function.

Method

This article reviews the latest research progress on the application of BTX-A in spasticity after stroke, discusses the potential and challenges of BTX-A in reducing spasticity and improving motor function in patients with stroke.

Result

Botulinum toxin type A (BTX-A) is a local muscle paralytic agent that has received extensive attention in recent years for its application in reducing muscle spasticity and promoting post-stroke rehabilitation.

Conclusion

This article confirms that botulinum toxin type A has a significant clinical effect in treating muscle spasticity after stroke and also helps improve motor function restoration in patients. Studies have shown that botulinum toxin type A injections are effective in reducing spasticity(NOT GOOD ENOUGH! It's not a cure, so then it failed! Survivors hate failure!) and, when combined with rehabilitation training, can facilitate the recovery of motor function in post-stroke patients. Therefore, botulinum toxin type A has a broad application prospect in the rehabilitation of post-stroke spasticity.

KEY MESSAGES

• The application of botulinum toxin type A (BTX-A) in post-stroke rehabilitation primarily focuses on reducing muscle spasticity and improving motor function.

• Spasticity is a common clinical manifestation of damage to the upper motor neurons. It is caused by the increased excitability of gamma motor neurons and manifests as excessive involuntary muscle contraction. Its causes include cerebral palsy, stroke, traumatic brain injury, multiple sclerosis, brain or spinal cord tumours, and spinal cord injuries

Safety and efficacy of glibenclamide on functional outcomes in ischemic and hemorrhagic stroke: a systematic review and meta-analysis of randomized clinical trials

But NO protocol written up, so useless.

 Safety and efficacy of glibenclamide on functional outcomes in ischemic and hemorrhagic stroke: a systematic review and meta-analysis of randomized clinical trials

Ola Bin Shilash^1,2*Hussam Alhathlol^1,2Rana Alduhaysh^1,2Razan Almufarriji³Mohammed Bafaquh⁴

• ¹College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
• ²King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
• ³Department of Neurosurgery, National Neuroscience Institute, King Fahad Medical City, Riyadh, Saudi Arabia
• ⁴Department of Neuro-Oncology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia

Background: Secondary brain injuries, including delayed cerebral ischemia, neuroinflammation, and stroke induced cerebral edema can occur following both ischemic and hemorrhagic strokes, contributing to a negative impact on clinical outcomes. Glibenclamide, a sulfonylurea antidiabetic medication, has shown potential in minimizing these consequences by targeting the SUR1-TRPM4 channel. However, glibenclamide’s therapeutic effectiveness and safety in stroke patients remain unknown. Therefore, this systematic review aims to assess the safety and efficacy of glibenclamide in improving outcomes following both ischemic and hemorrhagic strokes.

Methods: Four databases were searched for RCTs published up to November 2024. Studies were included if they involved adult patients with ischemic stroke, hemorrhagic stroke, or subarachnoid hemorrhage, and reported relevant safety and efficacy outcomes. Efficacy outcomes were measured using the Modified Rankin Scale at 3 and 6 months. Safety outcomes included adverse events such as hypoglycemia, hydrocephalus, and mortality.

Results: Data from six RCTs, involving 555 patients (280 intervention, 275 control), were included: 4 trials in subarachnoid hemorrhage, one trial in ischemic stroke, and one in hemorrhagic stroke. At 3 months, the pooled odds ratio (OR) for poor functional outcomes was 0.98 (95% CI: 0.65–1.48), and at 6 months, 0.52 (95% CI: 0.24–1.12; p = 0.094), with no significant differences between glibenclamide and placebo. Safety analysis showed a significant increase in symptomatic hypoglycemia (OR 4.69, 95% CI: 1.45–15.23; p = 0.010) but no significant differences for hydrocephalus (OR 1.60, 95% CI: 0.76–3.37; p = 0.220) or mortality (OR 0.57, 95% CI: 0.32–1.05; p = 0.071). Delayed cerebral ischemia (DCI) showed a borderline reduction in risk (OR 0.43, 95% CI: 0.18–1.00; p = 0.051) in the treatment group.

Conclusion: In patients with ischemic or hemorrhagic stroke, glibenclamide demonstrates a favorable safety profile but shows limited efficacy in improving functional outcomes. The elevated risk of hypoglycemia emphasizes the necessity of using this medication with caution.

Introduction

Stroke remains one of the leading causes of morbidity and mortality globally, with an increasing burden due to rising incidents and prevalent cases over the past three decades (1). It is broadly categorized into ischemic stroke, caused by vascular occlusion, and hemorrhagic stroke, which includes intracerebral hemorrhage (ICH) (2). Despite advancements in acute stroke care, such as improved diagnostic tools and interventions, long-term outcomes remain suboptimal due to secondary complications like neuroinflammation, cerebral edema, and delayed neuronal injury (1, 2). This highlights the urgent need for novel therapeutic approaches targeting these mechanisms. Current treatments primarily concentrate on managing the acute phase, including reperfusion intervention (1, 2). Nevertheless, there is a considerable deficiency in therapies aimed at the molecular mechanisms that contribute to secondary injury (3). Glibenclamide, a well-known sulfonylurea antidiabetic medication, is one promising therapeutic approach. Glibenclamide works by blocking the sulfonylurea receptor 1 (SUR1)—transient receptor potential melastatin 4 (TRPM4) channel complex, which is essential in the pathophysiology of many central nervous system (CNS) injuries, including aSAH (4). The activation of the SUR1-TRPM4 channel has been linked to vasogenic edema, neuroinflammation aggravation, and neuronal integrity impairment. Glibenclamide, which targets this channel, has the ability to minimize cerebral edema, limit neuronal death, and reduce inflammation, therefore enhancing neurological recovery (5). Preclinical research has provided solid evidence for glibenclamide’s neuroprotective properties (6). In animal models of ischemic brain damage, glibenclamide treatment has been demonstrated to decrease vasogenic edema, reduce infarct volume, and enhance functional recovery (6). Building on this basis, preliminary clinical studies have assessed the function of glibenclamide in the setting of stroke. For example, recent research found that high-dose oral glibenclamide significantly reduced radiological indicators of cerebral edema during 10 days of therapy, implying possible advantages in preventing decompressive surgeries (7). Other trials, however, have shown conflicting results, with some failing to detect substantial increases in functional outcomes or decrease in mortality rates (3). While preliminary data suggests that it can minimize vasospasm and enhance perfusion, inconsistencies in research design, dosage regimens, and outcome measures have restricted the generalizability of findings (5). Furthermore, some concerns do exist about glibenclamide’s safety profile, including its possible impact on glucose homeostasis and other systemic side effects in non-diabetics (4, 8, 9).

To overcome these gaps, this systematic review and meta-analysis will analyze data from randomized controlled trials to assess the effectiveness and safety of glibenclamide in stroke. This study aims to clarify the influence of glibenclamide on major clinical outcomes.

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Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke

Predictions aren't needed; PREVENTION IS! GET THERE! I'd have you all fired!

You've known about this problem for a long time. SOLVE IT! 

Just maybe this vaccine!

• Pneumonia Vaccine (3 posts to July 2020)

 

• 11% Stroke-associated pneumonia (2 posts to October 2020)

Is everyone in stroke so blitheringly stupid that they don't realize that you solve and prevent problems? Rather than lazily describing them? Serious question!

Send me hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and my response in my blog. Or are you afraid to engage with my stroke-addled mind? Your patients need an explanation of why you aren't trying to get survivors recovered.

Why isn't your 'professional' solving stroke?

Laziness? Incompetence? Or just don't care? NO leadership? NO strategy? Not my job? Not my Problem!

 Development and validation of a machine learning-based risk prediction model for stroke-associated pneumonia in older adult hemorrhagic stroke

Yi Cao^1,2†Haipeng Deng^1†Shaoyun Liu^1†Xi Zeng¹Yangyang Gou²Weiting Zhang²Yixinyuan Li²Hua Yang^1*Min Peng^3*

• ¹Department of Neurosurgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China
• ²School of Nursing, Guizhou Medical University, Guiyang, China
• ³Department of Nursing Quality Management, Affiliated Hospital of Guizhou Medical University, Guiyang, China

Objective: To develop and validate a machine learning (ML)-based model for predicting stroke-associated pneumonia (SAP) risk in older adult hemorrhagic stroke patients.

Methods: A retrospective collection of older adult hemorrhagic stroke patients from three tertiary hospitals in Guiyang, Guizhou Province (January 2019–December 2022) formed the modeling cohort, randomly split into training and internal validation sets (7:3 ratio). External validation utilized retrospective data from January–December 2023. After univariate and multivariate regression analyses, four ML models (Logistic Regression, XGBoost, Naive Bayes, and SVM) were constructed. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated for training and internal validation sets. Model performance was compared using Delong's test or Bootstrap test, while sensitivity, specificity, accuracy, precision, recall, and F1-score evaluated predictive efficacy. Calibration curves assessed model calibration. The optimal model underwent external validation using ROC and calibration curves.

Results: A total of 788 older adult hemorrhagic stroke patients were enrolled, divided into a training set (n = 462), an internal validation set (n = 196), and an external validation set (n = 130). The incidence of SAP in older adult patients with hemorrhagic stroke was 46.7% (368/788). Advanced age [OR = 1.064, 95% CI (1.024, 1.104)], smoking[OR = 2.488, 95% CI (1.460, 4.24)], low GCS score [OR = 0.675, 95% CI (0.553, 0.825)], low Braden score [OR = 0.741, 95% CI (0.640, 0.858)], and nasogastric tube [OR = 1.761, 95% CI (1.048, 2.960)] were identified as risk factors for SAP. Among the four machine learning algorithms evaluated [XGBoost, Logistic Regression (LR), Support Vector Machine (SVM), and Naive Bayes], the LR model demonstrated robust and consistent performance in predicting SAP among older adult patients with hemorrhagic stroke across multiple evaluation metrics. Furthermore, the model exhibited stable generalizability within the external validation cohort. Based on these findings, the LR framework was subsequently selected for external validation, accompanied by a nomogram visualization. The model achieved AUC values of 0.883 (training), 0.855 (internal validation), and 0.882 (external validation). The Hosmer-Lemeshow (H-L) test indicates that the calibration of the model is satisfactory in all three datasets, with P-values of 0.381, 0.142, and 0.066 respectively.

Conclusions: This study constructed and validated a risk prediction model for SAP in older adult patients with hemorrhagic stroke based on multi-center data. The results indicated that among the four machine learning algorithms (XGBoost, LR, SVM, and Naive Bayes), the LR model demonstrated the best and most stable predictive performance. Age, smoking, low GCS score, low Braden score, and nasogastric tube were identified as predictive factors for SAP in these patients. These indicators are easily obtainable in clinical practice and facilitate rapid bedside assessment. Through internal and external validation, the model was proven to have good generalization ability, and a nomogram was ultimately drawn to provide an objective and operational risk assessment tool for clinical nursing practice. It helps in the early identification of high-risk patients and guides targeted interventions, thereby reducing the incidence of SAP and improving patient prognosis.

1 Introduction

Stroke-associated pneumonia (SAP) refers to newly acquired pneumonia in non-mechanically ventilated patients within 7 days of stroke onset (1). First proposed by German scholar Hilker in 2003 (2), subsequent studies report its incidence rate ranging from 6.5 to 58.4%, with risk factors including advanced age, male sex, smoking, dysphagia, hyperglycemia, and lower Glasgow Coma Scale (GCS) scores (3–8). Compared to non-SAP patients, SAP significantly worsens prognosis, leading to increased disability and mortality rates, prolonged hospitalization, and elevated healthcare costs (3, 5, 8–10). Meanwhile, with the intensification of population aging in China, the nursing needs of older adult patients with hemorrhagic stroke are becoming increasingly prominent (11). Current nursing strategies have deficiencies in aspects such as infection prevention, individualized intervention, and uneven distribution of medical resources. Especially in the context of limited medical resources, there is a lack of validated tools to prioritize the identification of high-risk patients and optimize nursing priorities, which restricts the prevention and control of SAP.

Machine Learning (ML) a subset of artificial intelligence (AI), enables in-depth exploration and analysis of extensive datasets, offering novel methodologies and research frameworks for precise prediction. Its applications span diverse fields, particularly in medicine, where ML facilitates the development of automated tools for clinical decision-making based on multidimensional medical data (10, 12). Risk prediction models, initially applied in cardiothoracic surgery (13, 14), leverage patient-specific risk factors and ML algorithms to forecast disease progression, therapeutic responses, and outcomes. Recent studies have utilized ML to integrate vital signs, epidemiological data, and laboratory/imaging findings for diagnostic or prognostic purposes. However, there are currently few risk prediction models for SAP in older adult patients with hemorrhagic stroke based on ML algorithms. The absence of such models not only limits the clinical early-warning ability but also hinders the precise allocation of nursing resources. This need is particularly urgent considering the characteristics of older adult patients with multiple underlying diseases and a short window period for nursing intervention. In this study, the prediction of SAP in older adult patients with hemorrhagic stroke was defined as a binary classification problem. Therefore, four widely used ML algorithms for solving classification problems (Logistic regression, Naive Bayes, Support Vector Machine, and eXtreme Gradient Boosting algorithm) were selected to construct the risk prediction model. The effectiveness of the model was evaluated through internal and external validation, aiming to provide references for clinical nursing practice, prevention, and control.

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Developing a predictive model for lower extremity deep vein thrombosis in acute ischemic stroke using a nomogram

Predictions aren't needed; PREVENTION IS! GET THERE! I'd have you all fired!

 Developing a predictive model for lower extremity deep vein thrombosis in acute ischemic stroke using a nomogram

Weicong Chen^1,2Chaohua Cui^1,2Changsheng Lai^2,3*

• ¹Life Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
• ²Clinical College of Youjiang Medical University for Nationalities, Baise, China
• ³Red Cross Hospital of Yulin City, Yulin, China

Background: Deep vein thrombosis (DVT) is a prevalent complication among patients with acute ischemic stroke (AIS). However, there remains a deficiency of patient-specific predictive models. This study aims to develop a nomogram to estimate the risk of lower extremity DVT in AIS patients during the acute phase (within 14 days of onset).

Methods: This retrospective multicenter study analyzed 391 eligible AIS patients from two tertiary hospitals in Guangxi, China. Sixty-three clinical variables encompassing demographic profiles, clinical characteristics, laboratory parameters, and therapeutic interventions were systematically extracted from electronic health records. All participants underwent standardized Doppler ultrasound assessments for bilateral lower extremity DVT within 14 days of symptom onset. Variable selection via backward stepwise logistic regression informed nomogram construction, with model performance evaluated through calibration curves and decision curve analysis.

Results: Data from one hospital were used as the modeling cohort, while data from another hospital were used for external validation. Multivariate logistic regression analysis showed that gender, age, diabetes, anemia, bed rest exceeding 3 days, and medium-frequency electrical therapy are independent risk factors for DVT in AIS patients. A nomogram was developed based on these six independent risk factors, with the area under the ROC curve (AUC) for predicting DVT risk within 14 days post-AIS being 0.812 for the modeling cohort and 0.796 for the external validation, indicating good predictive performance. Calibration of the nomogram showed Hosmer-Lemeshow test results with p values of 0.200 for the modeling set and 0.432 for the validation set, indicating good model consistency. In decision curve analysis, the nomogram demonstrated superior net benefit over staging systems across a wide range of threshold probabilities.

Conclusion: We developed a nomogram to personalize the prediction of DVT risk in patients with AIS, assisting healthcare professionals in the early identification of high-risk groups for DVT and in implementing appropriate interventions to effectively prevent its occurrence.

Introduction

Stroke ranks as the second most common cause of death worldwide, characterized by high incidence, elevated disability rates, and significant recurrence (1–3). Stroke typically manifests with clinical features including paralysis and mobility impairment, and without standardized preventive measures, complications such as venous thromboembolism (VTE) are likely (4, 5). VTE comprises deep vein thrombosis (DVT) and pulmonary embolism (PE). Migration of lower limb DVT clots often precipitates PE, representing approximately 25% of early post-stroke mortality (5, 6). DVT risk peaks during the initial two-week period post-stroke, with possible onset as early as day two and peaking between days two and seven (7, 8). Ischemic stroke accounts for the majority of stroke cases, representing approximately 80% of total stroke incidence (5). Studies report DVT incidence ranging from 18.0 to 23.5% in acute ischemic stroke (AIS) patients (9, 10). DVT development in AIS patients impedes rehabilitation progress, prolongs hospitalization, and increases disability and mortality rates (11, 12).

DVT following stroke frequently manifests asymptomatically in clinical practice. Cognitive, speech, or consciousness impairments may further compromise symptom reporting, leading to diagnostic omissions (13, 14). The clinical dilemma persists in balancing pharmacological thromboprophylaxis against bleeding risks in ischemic stroke management (6, 15). Current diagnostic gold standards (Doppler ultrasound and venography) demonstrate limited predictive utility and time-dependent sensitivity (16). Currently, commonly utilized tools in clinical practice for assessing VTE risk, such as the Widely used VTE risk assessment tools (Caprini RAM, Padua Score) exhibit inadequate specificity for stroke populations. Despite multifactorial DVT etiology, evidence remains limited regarding AIS-specific predictors, with no validated algorithms for identifying high-risk patients.

The nomogram, a graphical predictive instrument integrating multiple risk factors, has demonstrated clinical utility across various diseases (17, 18). This investigation comprehensively evaluates demographic, clinical, laboratory, and therapeutic parameters to develop a personalized nomogram for acute-phase DVT prediction (≤14 days post-AIS onset), aiming to enhance clinical decision-making and preventive strategies.

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