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To predict individual emergence from post-traumatic amnesia (PTA) in patients with moderate to severe traumatic brain injury (TBI). Prospective nationwide cohort study based on data from a national registry: Danish Head Trauma Database. Two highly specialized neurorehabilitation hospitals in Denmark. TBI patients admitted between 2004 and 2020 were included in the study. Not applicable. Duration of PTA, defined as the number of days from TBI onset until regaining anterograde memory function, is a proxy for the resolution of the confusional state. Using competing risk survival analyses, we estimated absolute risks (probabilities) of emerging from PTA according to the included covariates of interest: sex, age, severity of TBI and time since injury. 955 TBI patients (mean age 45.2 (SD=17.8), 21% female) were included in the study, of which 658 emerged from PTA within one year. In the fully adjusted model, male sex, older age, and greater TBI severity were associated with a lower probability of emerging from PTA. Among patients with severe TBI, 99 out of 100 in the youngest age group will emerge from PTA within one year, compared with 72 out of 100 in the oldest age group. Among patients with very severe TBI, the corresponding estimated probabilities are 62 and 24 out of 100 within one year, respectively. The prognostic model offers clinicians an evidence-based tool to individually predict TBI patient's probability of emerging from PTA, incorporating key prognostic factors while accounting for competing risks such as mortality and non-emergence.

Several studies have identified transcutaneous spinal cord stimulation (tSCS) as a noninvasive neuromodulation technique for improving motor function in individuals with neurological disorders, including stroke. Despite a plethora of preliminary findings, there remains no standardized protocol regarding the optimal tSCS parameters tailored to patients with stroke. The objective of this study was to employ a supervised machine learning (ML) approach to determine the optimal tSCS frequency and intensity parameters for patients with chronic stroke by leveraging data from single-day interventions that systematically varied in frequency and intensity across four stimulation conditions. Twenty adults with chronic hemiparetic stroke (mean age 53.3 ± 10.8 years; 13 males, 7 females) who were ≥6 months post-stroke was enrolled, excluding individuals with multiple strokes, severe spasticity, or implanted devices. Each participant participated in a baseline session followed by five intervention sessions, during which the frequency and intensity of stimulation were varied randomly. Multimodal sensors, including surface EMG, inertial measurement unit-based kinematics, and spatiotemporal gait parameters, were used to quantify acute changes in gait symmetry, and optimal stimulation frequency and intensity were defined as those yielding the greatest improvement in the combined gait asymmetry metric. A supervised machine-learning classifier was trained using nested leave-one-subject-out cross-validation to predict the optimal stimulation frequency and intensity to maximize differences from the baseline data alone. AUROC values were calculated for the frequency and intensity predictions. The ML models achieved AUROC values of 0.86 [0.75-0.94] for frequency prediction and 0.82 [0.69-0.94] for intensity prediction. The top ten predictive features for each model spanned with spinal motor evoked potentials, wearable sensors, and demographic domains, highlighting multimodal contributions to stimulation optimization. These findings demonstrate that supervised learning can predict individualized tSCS parameters from demographic data and baseline sensor features that yield the greatest improvement in gait symmetry after stroke, representing a promising step toward the data-driven personalization of neuromodulation therapy in neurorehabilitation.

Dual-tasking is commonly used to assess functional capacity in neurologically intact and poststroke populations. Stroke survivors often experience significant dual-task deficits due to cognitive-motor interference and neurological impairments that limit mobility and daily participation. Emerging evidence suggests non-invasive brain stimulation (NIBS) may enhance neuroplasticity and improve dual-task performance; however, findings remain limited and inconsistent. This systematic review aims to evaluate the effects of NIBS as a central nervous system priming technique on dual-task performance in individuals poststroke. This protocol follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and is registered with PROSPERO (CRD420250644455). A comprehensive search will be conducted in PubMed (MEDLINE), Embase, Web of Science and Scopus using keywords related to stroke, dual-tasking and NIBS. Reference lists of included studies will also be manually screened. Eligible studies must be randomised controlled trials or crossover designs involving adults poststroke that use NIBS while explicitly evaluating dual-task performance. Two independent reviewers will assess study quality using the Cochrane Risk of Bias tool, with disagreements resolved by a third reviewer. Meta-analysis will be conducted when feasible; otherwise, a narrative synthesis will be provided.This review will clarify current evidence supporting NIBS for improving dual-task motor-cognitive outcomes in stroke rehabilitation and may guide clinical decision-making and future neurorehabilitation research. Ethical approval is not required because no human participants are involved. Findings will be disseminated through peer-reviewed publications and conference presentations. CRD420250644455.

The rise of large language models (LLMs) such as GPT-4 and DeepSeek has transformed healthcare information processing by enabling natural language-based clinical reasoning. However, the integration of LLMs with privacy-sensitive biomedical signals, particularly electroencephalogram (EEG) data used in brain-computer interface (BCI) systems, remains underexplored. EEG signals, especially during motor imagery (MI) tasks, are critical for assistive neurotechnologies but pose significant privacy risks due to their capacity to reveal cognitive and medical information. Traditional encryption techniques often distort signal structure or require decryption with additional noise, compromising classification performance and real-time usability. To address this gap, we propose a deep denoising structure-preserving neural encoding network (DSNet) that enables accurate classification of privacy-preserving encoded EEG representations without requiring decryption. EEG features were extracted using common spatial pattern (CSP) and transformed into privacy-preserving encoded representations while preserving their statistical structure. Here, encoding refers to a non-reversible neural transformation designed for privacy preservation rather than a formal cryptographic guarantee. Two deep learning architectures, a feedforward neural network (NN) and a recurrent neural network (RNN), were evaluated for classification in the encoded feature space. Furthermore, we integrated an LLM (GPT-4) to generate clinical-style summaries based on model outputs, enhancing interpretability for clinician review and potential clinical support use. Using publicly available datasets, DSNet-NN achieved over 87% accuracy for every subject, outperforming both the RNN variant and baseline models. It also demonstrated resilience to simulated privacy attacks. LLM-generated reports provided clinician-friendly interpretations of MI predictions, supporting potential real-world applicability. This study introduces an AI framework that bridges privacy-preserving EEG decoding with LLM-based clinical reasoning, offering a practical solution for privacy-preserving neurorehabilitation and digital health systems.

Attentional and working-memory processes can be monitored noninvasively using electroencephalography (EEG), which provides physiological indices of mental workload. Prior studies consistently report increased frontal-midline theta and beta power together with suppression of posterior alpha activity during cognitively demanding tasks. However, most investigations rely either on group-level statistical analyses or on machine-learning (ML) classification alone, often without examining whether the predictive features identified by ML models correspond to established neurophysiological markers. This study reanalyzed an open EEG dataset comprising 36 young adults performing a mental arithmetic task. EEG activity was quantified using power spectral density (PSD) estimation based on Welch's method (1-second Hamming windows with 50% overlap) across canonical frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (30-50 Hz). Event-related potentials (ERPs) time locked to arithmetic stimulus onset were also examined. Spectral features were subsequently used to train three ML classifiers Logistic Regression, Support Vector Machine (SVM), and Random Forest using subject-level cross-validation to distinguish resting and task conditions. ERP analysis revealed early stimulus-locked modulations within the 20-50 ms latency range, reflecting rapid sensory engagement during task performance. Spectral analysis demonstrates significant workload-related changes: theta power increased (η2 = 0.49), alpha power decreased (η2 = 0.37), beta power increased (η2 = 0.18), and delta power decreased (η2 = 0.38), whereas gamma-band differences did not remain significant after correction for multiple comparisons. Among the ML models, Random Forest achieved the highest classification performance (accuracy = 0.92 ± 0.03, AUC = 0.94). Feature-importance analysis indicated that theta and alpha band powers contributed most strongly to classification, consistent with the statistical findings. The results replicate well-established EEG signatures of cognitive workload and demonstrate convergence between statistical inference and machine-learning prediction. The alignment between physiological interpretation and predictive modeling supports frontal theta enhancement and posterior alpha suppression as reliable indicators of cognitive engagement. These findings highlight the potential of EEG-based workload monitoring for healthcare and applied neuroscience applications, including early detection of cognitive decline and neurorehabilitation monitoring. Nevertheless, the modest sample size and single dataset design warrant cautious interpretation and future validation in larger and independent cohorts.

Spinal cord injury (SCI) is a severely disabling neurological disorder caused by primary mechanical trauma and subsequent secondary damage, resulting in persistent neurological deficits and imposing a significant social and economic burden. The existing treatment methods - including drug therapy, surgical treatment, and rehabilitation therapy - have limited effects in promoting functional recovery. In recent years, neural stem cells (NSCs) and their derived exosomes have emerged as highly promising innovative treatment options. NSCs promote neural repair through their multifaceted differentiation ability, secretion of neurotrophic factors, and regulation of the immune response in the injured microenvironment. However, factors such as low graft survival rate, ethical restrictions, and the risk of immune rejection have hindered their clinical translation. In contrast, NSC-derived exosomes offer an emerging cell-free alternative solution. NSC-derived exosomes can inhibit neuroinflammation, reduce glial scar formation, enhance axonal regeneration and promote angiogenesis. Compared with the transplantation of NSCs, NSC-derived exosomes may have lower immunogenicity and can avoid direct graft-related excessive proliferation. At the same time, they are convenient for loading, surface modification, and integration with hydrogels, scaffolds, or other nanomaterial delivery systems. Despite these advantages, their clinical application still faces challenges. The main challenges include vesicle heterogeneity, standardization of dosage and efficacy, target specificity of the lesion, biodistribution, timing of treatment, safety of repeated administration, storage stability, and GMP-compliant scalable manufacturing. Overall, NSCs and NSC-derived exosomes demonstrate significant therapeutic potential in the repair of SCI.

This study was designed to identify key predictors of non-return to work (non-RTW) in young and middle-aged patients with acute ischemic stroke due to large vessel occlusion (AIS-LVO) after endovascular therapy (EVT). Based on these predictors, we developed and validated an individualized nomogram for non-RTW risk stratification to facilitate early identification of high-risk patients and guide personalized rehabilitation for better functional recovery and less occupational loss. In this retrospective cohort study, 350 consecutive AIS-LVO patients who underwent EVT at Dongguan Hospital of Traditional Chinese Medicine (July 2018-July 2025) were included. Potential predictors were selected using least absolute shrinkage and selection operator (LASSO) regression, and independent predictors were identified via multivariable logistic regression. A nomogram was constructed and assessed for discrimination using the area under the receiver operating characteristic curve (AUC), for calibration using calibration curves and the Hosmer-Lemeshow test, and for clinical utility via decision curve analysis (DCA). Six independent predictors of non-RTW were identified: instrumental activities of daily living (IADL), admission NIHSS score, Nutritional Risk Screening 2002 (NRS-2002) score, balance impairment (as measured by the Berg Balance Scale, BBS), post-stroke rehabilitation (Rehab), and anxiety-depressive state (ADS). The nomogram demonstrated robust discriminative performance (AUC = 0.858, 95% CI: 0.812-0.903). Calibration curves confirmed favorable calibration between predicted and observed probabilities. Decision curve and clinical impact analyses revealed clinically meaningful net benefit across most threshold probabilities. We developed and validated a clinically actionable nomogram to predict non-RTW in young and middle-aged AIS-LVO patients after EVT. This tool enables early risk stratification and personalized rehabilitation planning, promoting long-term functional and vocational recovery.

Inconsistent and variably interpreted definitions of spasticity, alongside evolving mechanistic understanding, highlight the need for a clear, clinically relevant consensus definition. An international expert panel used a modified Delphi process to develop a concise, clinically applicable definition that reflects current understanding and supports consistent assessment and management. Participants reviewed existing definitions, completed a pre‑meeting survey, and engaged in structured discussions, with draft definitions iteratively refined through successive rounds of voting to achieve consensus. Key components identified included disordered sensorimotor control, central nervous system involvement, and velocity‑ and length‑dependent resistance to passive stretch, while existing definitions were considered either overly narrow or insufficiently relevant to clinical practice. The consensus definition characterizes spasticity as "A disorder of sensorimotor control resulting from upper motor neuron disease. It is characterized by velocity- and length-dependent involuntary muscle overactivity, which is intermittent or sustained, during passive stretch." This definition integrates contemporary mechanistic concepts with clinical applicability and is intended to improve conceptual clarity, facilitate communication, and promote consistency in diagnosis, measurement, and treatment.

The concept of 'muscle health' is increasingly recognized as a central determinant of physical function, metabolic regulation, and disease resilience, yet its clinical integration remains fragmented by inconsistent definitions and measurement approaches. This Perspective synthesizes insights from the Research Topic 'Advancing Muscle Health: From Technical and Clinical Research to Practice', which brings together nine contributions spanning assessment technologies, biomarkers, clinical populations, and interventions. Collectively, these works illustrate a field transitioning from isolated advances toward more integrated, clinically meaningful frameworks. Emerging ultrasound-based methods demonstrate how improved reliability and automation may enable scalable muscle assessment, while biomarker studies highlight both the promise and limitations of metabolomic, functional, and surrogate metrics in capturing the systemic nature of muscle health. Evidence from neurological, vascular, and oncological contexts reinforces that muscle is not only an outcome of disease, but a key modifier of disease progression and risk. Across these domains, exercise, particularly resistance-based and multimodal approaches, continues to emerge as a key, yet under-implemented, strategy. Despite this progress, critical gaps remain. The field lacks longitudinal, diverse-cohort data, standardized measurement frameworks, and robust integration of emerging technologies such as multi-omics and artificial intelligence. Moving forward, advancing muscle health will require interdisciplinary, translational approaches that align mechanistic insight with clinical application, enabling precise phenotyping and scalable interventions. Bridging these gaps is essential to move muscle health from a research construct to a core component of routine clinical care and public health strategy.

Progressive supranuclear palsy (PSP) is a 4-repeat tauopathy characterized by clinicopathological heterogeneity. The complex interplay between tau deposition, structural changes, and disease spread are unclear. To investigate the temporospatial patterns of tau propagation and neurodegeneration using multimodal imaging with MRI and flortaucipir (FTP) PET, and determine relationship with clinical heterogeneity. 150 PSP patients (n = 66 Richardson's syndrome [PSP-RS], n = 26 parkinsonism [PSP-P], n = 25 speech-language [PSP-SL], n = 13 progressive gait freezing (PSP-PGF), n = 10 corticobasal syndrome [PSP-CBS], n = 3 frontal, n = 1 oculomotor and n = 6 postural instability) underwent 3 T-MRI and FTP-PET. Forty-three patients died and underwent autopsy. Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that separates data-driven disease phenotypes distinguished by diverse temporal progression patterns, was applied to both MRI and FTP-PET W-scores (adjusted for age, sex and scanner, using 102 controls). Longitudinal MRI (n = 76) and FTP (n = 56) data, analysed with linear mixed models, were used to assess regional progression patterns and compared with the machine learning predictions. Two subtypes emerged across modalities. Subtype 1 exhibited initial subcortical involvement, mainly included PSP-RS and PSP-P patients and mostly featured PSP pathology, while subtype 2 exhibited early cortical involvement, PSP-SL patients and CBD pathology. FTP-PET stages preceded MRI stages, suggesting tau deposition anticipates atrophy. MRI stages were better in capturing clinical progression and predicting longitudinal disease evolution. These findings suggest the existence of subcortical and cortical subtypes of PSP, with distinct clinicopathological features. Tau PET and MRI provide complementary insights into disease progression, with MRI more closely reflecting clinical evolution.

Chordoma is a rare malignant bone tumor with high local recurrence, metastatic spread in 40% to 60% of patients over the disease course, and significant morbidity. Because of its rarity, anatomical complexity, and prolonged natural history, high-quality evidence to guide management is limited. International consensus guidelines for localized chordoma were first published in 2015; however, advances in pathology, imaging, surgery, radiotherapy, and supportive care since then necessitate updated multidisciplinary recommendations. To update and expand the 2015 consensus recommendations on the diagnosis, treatment, and follow-up of pediatric and adult patients with primary, localized chordoma. In June 2025, a meeting of the Global Chordoma Consensus Group was held in Milan, Italy, that included experts from all relevant specialties as well as patient representatives. A comprehensive literature review guided structured discussions on the management of primary localized disease. Levels of evidence and grades of recommendation were assigned. A total of 305 articles were included in the literature review. Management strategies were stratified by anatomical site (skull base, mobile spine, and sacrum). The central principal of care was treatment at experienced, multidisciplinary centers, with maximally safe surgery followed by high-dose, highly conformal radiotherapy. Guidance was provided on diagnosis, surgical approaches, and radiotherapy planning for each anatomical site. Systemic therapy options; long-term, risk-adapted follow-up; and supportive, palliative, and rehabilitative care were also addressed. This global consensus statement provided updated multidisciplinary guidance for the management of primary, localized chordoma. It aimed to harmonize clinical practice, support shared decision-making, and identify priorities for future collaborative research in this rare and challenging disease.

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Cognitive dysfunction is a prevalent mental health problem following hemorrhagic shock with resuscitation (HSR). Our previous work indicated that neuroinflammation caused by nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3), which is modulated by glial cells, such as microglia, is potentially significant in developing emotional and cognitive dysfunction. However, little is known about the potential of microglial NLRP3 to treat HSR-induced cognitive dysfunction. Therefore, this study established an HSR rodent model to investigate whether microglial NLRP3 represents a potential therapeutic target for improving cognitive dysfunction after HSR. An HSR model was developed by inducing bleeding and retransfusion in mice. The Morris water maze and Novel object recognition tests were used for behavioral evaluation. To selectively knock out the NLRP3 inflammasome in microglia, the AAV‑CX3CR1‑Cre (pAAV‑CX3CR1‑NLS‑Cre‑P2A‑EGFP‑3xFLAG‑WPRE) virus was stereotaxically injected into the hippocampal CA1 region of male C57BL/6 mice with NLRP3flox/flox. Immunofluorescence and local field potential recordings were used to assess pathological alterations at different intervals after injury. Our findings indicated that the NLRP3 inhibitor MCC950 significantly reversed HSR-induced lower recognition index, increased escape latency, reduced platform crossings, decreased θ and γ power and θ-γ phase coupling, decreased intensity of PSD95 and Synaptophysin, increased number of Iba1+ cells, normalized soma size and total process length cell of Iba1, and increased colocalization of cleaved caspase-1 and interleukin-18 with Iba-1 in the CA1 area of the hippocampus. It also significantly alleviated the HSR-induced cognitive dysfunction. Moreover, we observed that HSR-induced cognitive dysfunction, neuroinflammation, and synaptic plasticity damage may be reversed by knocking out NLRP3 in the hippocampal CA1 microglia. Our study demonstrates that inhibition of microglia‑specific NLRP3 can mitigate cognitive impairment following HSR, identifying microglial NLRP3 as a promising therapeutic target. This effect may be associated with suppressing pyroptosis via the NLRP3 signaling pathway in microglia.

Hyperemesis gravidarum (HG) is a severe form of nausea and vomiting in pregnancy that may result in dehydration, malnutrition, and electrolyte imbalance. Wernicke's encephalopathy (WE), caused by thiamine deficiency, and refeeding syndrome (RFS), a metabolic complication of nutritional rehabilitation, are rare but serious sequelae of prolonged starvation in HG. We present the case of a multiparous woman in her 30s with severe HG and missed miscarriage at approximately 16 weeks, who developed refractory hypokalaemia, hypomagnesaemia, and evolving neurological symptoms following medical management of miscarriage. Magnetic resonance imaging demonstrated symmetrical medial thalamic and mammillary body changes suspicious for WE. She improved following intravenous thiamine and vitamin B complex replacement, potassium and magnesium correction, and multidisciplinary care. This case underscores the importance of early thiamine prophylaxis in severe HG and highlights persistent refractory electrolyte abnormalities as a potential early indicator of RFS and evolving WE.

Despite recent advances, the pathophysiology of functional neurological disorder (FND) remains incompletely understood. Structural neuroimaging studies have identified grey matter alterations in somatomotor, salience, limbic, and default mode network associated areas, although findings are inconsistent. Mega-analyses, which combine individual-level data across studies, can help clarify structural alterations. We conducted a mega-analysis of brain structural morphometrics derived from T1-weighed MRI scans from fifteen international research groups. After across-site harmonisation with ComBat, we compared 493 functional motor and seizure patients with 564 healthy controls. Euler numbers were included to account for head motion. The FND cohort showed reduced cortical thickness in the bilateral superior frontal gyri (left d = 0.22, right d = 0.21) and sulci (d = 0.22 & 0.23), bilateral superior precentral sulcus (d = 0.22 & 0.26), right precentral gyrus (d = 0.25), right paracentral gyrus and sulcus (d = 0.23), right cuneus (d = 0.23), and right inferior opercular gyrus (d = 0.21); reduced left postcentral gyrus surface area (d = 0.25) and right hippocampal volume (d = 0.22). No regions were different in relative surface area. There were no associations between morphometrics and illness duration, or lifetime history of depression or anxiety. Differences between motor and seizure variants were not identified. This large mega-analysis suggests subtle morphometric differences, particularly in prefrontal and motor regions. This may represent predisposing vulnerabilities, compensatory mechanisms, or FND-specific alterations. Improved neuropsychiatric characterisation of FND research cohorts will help further contextualise the biological relevance of structural alterations.

Radial nerve palsy is a recognized complication of humeral shaft fractures, often associated with distal-third spiral patterns described by Holstein and Lewis. However, authentic mechanical entrapment of the nerve at the mid-shaft level is rather rare. These events test the limits of normal anatomy and show how important it is to look into things right once when fracture geometry suggests possible nerve entrapment. A 28-year-old right-handed female sustained a humeral fracture at the mid-shaft (AO 12-A3) following a collision with a two-wheeled vehicle. She showed early signs of radial nerve palsy, which meant that her wrist and finger extensors were weak, but her triceps were still working. Radiographs confirmed a transverse fracture/very short oblique configuration. An open reduction and internal fixation within 6 h of injury through the posterior revealed that the radial nerve was stuck between broken pieces of bone and was being compressed by sharp cortical edges. During fixation, the nerve was carefully released and secured. Further dissection proximally revealed an auxiliary branch originating from the radial cord, an anatomical variant. After surgery, rehabilitation included early range-of-motion exercises and wrist splinting. The patient achieved 50% recovery of wrist extension at 12 weeks and complete neurological recovery with full bony union by 24 weeks. This case elucidates that entrapment of the radial nerve can occur at the mid-shaft level when fracture morphology and varied nerve anatomy overlap. Cadaveric studies confirm the variations in radial nerve branching and its proximity to the humeral cortex. Early surgical exploration and decompression are crucial to avoid chronic palsy, particularly when the clinical presentation or fracture morphology is atypical. Atypical mid-shaft humeral fractures with radial-nerve palsy require prompt surgical evaluation. It is vital to know about high-level radial-nerve branching variants because locating and releasing them early helps the patient fully recover and helps us learn more about the Holstein-Lewis mechanism outside its usual distal zone.

Gait assessment provides key indicators of mobility, neurological function, and fall risk. It is widely used to monitor rehabilitation and disease progression. However, conventional motion-capture systems are costly, take up space, and require expert operation. Recent advances in pose estimation have enabled camera-based gait analysis, yet most implementations remain research prototypes that rely on manual intervention or offline processing. Fully automated and deployable assessment pipelines integrating data capture, processing, and reporting remain limited. This study presents and validates the accuracy of MobileGait, a low-cost, camera-based mobile application for automated gait assessment that integrates mobile data acquisition, server-side processing, and web-based reporting. The MobileGait workflow included video acquisition, 2D pose estimation, gait event detection, and automated computation of spatiotemporal parameters as well as sagittal-plane kinematics. For validation testing, 20 healthy elderly adults (52.6 &#xb1; 15.1 years; 13 women and 7 men) performed 5-m walk tests that were recorded simultaneously using the MobileGait iPad application and a Vicon motion-capture system. Agreement of spatiotemporal parameters with the Vicon system was assessed using intraclass correlation coefficients (ICC) and mean percentage error (MPE), while sagittal-plane kinematics were evaluated using coefficient of multiple correlation (CMC) and mean absolute error (MAE). MobileGait demonstrated strong agreement with Vicon for step time, stride time, and cadence (ICC = 0.95-0.99; MPE <1.08%), and for knee flexion-extension (MAE &#x2248;5.5&#xb0;, CMC &#x2248;0.96). Spatial parameters tended to be slightly overestimated (MPE <10%), while event-dependent metrics such as double-support time and ankle motion exhibited slightly greater variability. MobileGait enables low-cost, automated gait assessment on mobile devices, with analyses completed in minutes. It provides a practical solution to facilitate clinical gait assessment and remote monitoring in resource-limited settings, such as community centers and nursing homes.

Fasting-related headache has traditionally been attributed to dehydration, caffeine withdrawal, and sleep disruption, but pre-existing primary headache disorders may also play an important role in determining susceptibility. To determine the prevalence, clinical characteristics, and predictors of headache during Ramadan fasting and to explore the relative contribution of pre-existing primary headache disorders and the lifestyle-related factors assessed in this study to headache occurrence during fasting. In this multinational cross-sectional survey across 14 countries, adults (18-65&#x202f;years) observing Ramadan fasting completed a 17-item questionnaire. Headache during Ramadan was the primary outcome. Classification and regression tree (CRT) modelling were used to identify predictors and characterize fasting related headache. Headache attacks occurred mainly in the afternoon/pre-iftar period and improved after iftar in 54.6% of cases. Pre-existing primary headache disorders, particularly migraine, were more strongly associated with headache occurrence within the exploratory CRT model than the lifestyle variables assessed in this study. Fasting-related headache is highly prevalent and is influenced by both lifestyle-related factors and underlying headache susceptibility. A history of primary headache disorders was strongly associated with headache occurrence during Ramadan fasting, highlighting the importance of considering individual headache history alongside fasting- related exposures when assessing vulnerability.

This study investigates how melatonin affects white matter damage and oligodendrocyte pyroptosis after ischemic stroke by modulating microglia polarization through the ROR&#x3b1;/AMPK&#x3b1;/STAT1 pathway. A mouse model of middle cerebral artery occlusion (MCAO) was used, with melatonin (20&#xa0;mg/kg) administered post-reperfusion and daily for 14 days. Neurological function and white matter damage were assessed, along with gasdermin D (GSDMD) and caspase-1 expression to evaluate pyroptosis. In vitro, a Transwell co-culture system of microglia and oligodendrocytes exposed to oxygen-glucose deprivation (OGD) was employed. The role of ROR&#x3b1; in modulating microglia polarization and oligodendrocyte pyroptosis was explored using siRNA-mediated knockdown and AAV-based ROR&#x3b1; shRNA microinjection. Melatonin reduced white matter injury, inhibited oligodendrocyte pyroptosis, and improved sensorimotor function. These effects were linked to reduced APC/caspase-1 and APC/GSDMD double-positive cells and were dependent on ROR&#x3b1; signaling. Melatonin also shifted microglia from a pro-inflammatory to an anti-inflammatory phenotype, an effect reversed by ROR&#x3b1; knockdown. In vitro, melatonin inhibited OGD-induced pyroptosis via the ROR&#x3b1;/AMPK&#x3b1;/STAT1 pathway. These findings suggest that melatonin promotes long-term recovery by reducing neuroinflammation and protecting white matter integrity through ROR&#x3b1; signaling, highlighting its potential as a therapeutic agent for stroke recovery.