Abstract Background There is an emerging understanding of the increased risk of stroke in patients with immune thrombocytopenic purpura (ITP) and immune thrombotic thrombocytopenic purpura (iTTP). We aimed to determine the prevalence and characteristics of acute ischemic stroke (AIS) and intracranial hemorrhage (ICH) in patients with ITP and iTTP in a systematic review and meta-analysis. Methods We used PubMed, Embase, Cochrane, Web of Science, and Scopus using text related to ITP, iTTP, stroke, AIS, and ICH from inception to 11/3/2023. Our primary outcome was to determine prevalence of AIS and/or ICH in a cohort of ITP or iTTP patients (age > 18). Our secondary outcomes were to determine stroke type associated with thrombopoietin receptor agonists (TPO-RAs) in ITP patients, as well as risk factors associated with stroke in ITP and iTTP patients. Results We included 42 studies with 118,019 patients (mean age = 50 years, 45% female). Of those, 27 studies (n = 116,334) investigated stroke in ITP patients, and 15 studies (n = 1,685) investigated stroke in iTTP patients. In all ITP patients, the prevalence of AIS and ICH was 2.1% [95% Confidence Interval (CI) 0.8-4.0%] and 1.5% (95% CI 0.9%-2.1%), respectively. ITP patients who experienced stroke as an adverse event (AE) from TPO-RAs had an AIS prevalence of 1.8% (95% CI 0.6%-3.4%) and an ICH prevalence of 2.0% (95% CI 0.2%-5.3%). Prevalence of stroke did not significantly differ between all ITP patients and those treated with TPO-RAs. iTTP patients had a prevalence of AIS and ICH of 13.9% (95% CI 10.2%-18.1%) and 3.9% (95% CI 0.2%-10.4%), respectively. Subgroup analysis revealed the prevalence of AIS and ICH was greater in iTTP patients vs. all ITP patients (p < 0.01 and p = 0.02, respectively). Meta-regression analysis revealed none of the collected variables (age, sex, history of diabetes or hypertension) were risk factors for stroke in all ITP patients, although there were high levels of data missingness. Conclusions Prevalence of different stroke types was lower in all ITP patients vs. iTTP patients. Additionally, ITP patients experienced a similar prevalence of stroke regardless of if they were specifically denoted to have been treated with TPO-RAs or not, supporting the continued use of TPO-RAs in management. Risk factors for stroke remain unclear, and future studies should continue to investigate this relationship.
Neurosciences. Biological psychiatry. Neuropsychiatry, Neurology. Diseases of the nervous system
Ashley R. Tetens, Tyler R. Findlay, Jordyn Craig-Schwartz
et al.
Abstract Atypical teratoid rhabdoid tumor (ATRT) is a highly aggressive but genetically simple pediatric central nervous system tumor, defined by biallelic inactivation of the chromatin regulator SMARCB1 with remarkably few other cooperating mutations. Despite its genetic homogeneity, ATRT exhibits profound clinical and epigenetic heterogeneity, with three major subgroups (ATRT-TYR, ATRT-MYC, and ATRT-SHH) defined by DNA methylation and transcriptional signatures. Beyond these subgroup-defining features, we aimed to investigate epigenetic variability within tumors by applying whole-genome bisulfite sequencing and probabilistic modeling to quantify stochastic DNA methylation in primary ATRT samples encompassing all three subgroups. We show that ATRT exhibits a destabilized and increasingly stochastic methylome. While ATRT global methylation patterns diverge according to subgroup, some methylation perturbations, such as hypermethylation and increased methylation entropy over bivalent promoters, are consistent across subgroups. We find that methylation stochasticity alterations map onto potential drivers of ATRT, such as LIN28a, the HOXD cluster for ATRT-MYC, and OTX2 for ATRT-TYR, and identify actionable targets, such as hypermethylation of the tumor suppressor CDKN2a across all subgroups. We investigate the sensitivity of the aberrant DNA methylation landscape of ATRT to pharmacologic DNA methyltransferase inhibition (DNMTi) and histone deacetylase inhibition (HDACi). We show that decitabine leads to profound demethylation of patient-derived ATRT cell lines, including reversal of hypermethylation at bivalent promoters and the CDKN2a locus. The addition of HDACi leads to dramatic gene expression changes, including upregulation of innate immune signaling pathways, such as STING/interferon signaling, genes under the regulation of bivalent promoters, and reactivation of the tumor suppressor CDKN2A. The combination of DNMTi and HDACi synergistically reduces cell viability. Taken together, we show that ATRT has a highly stochastic methylome sensitive to epigenetic manipulation.
Large language models (LLMs) have shown promise in medical domains, but their ability to handle specialized neurological reasoning requires systematic evaluation. We developed a comprehensive benchmark using 305 questions from Israeli Board Certification Exams in Neurology, classified along three complexity dimensions: factual knowledge depth, clinical concept integration, and reasoning complexity. We evaluated ten LLMs using base models, retrieval-augmented generation (RAG), and a novel multi-agent system. Results showed significant performance variation. OpenAI-o1 achieved the highest base performance (90.9% accuracy), while specialized medical models performed poorly (52.9% for Meditron-70B). RAG provided modest benefits but limited effectiveness on complex reasoning questions. In contrast, our multi-agent framework, decomposing neurological reasoning into specialized cognitive functions including question analysis, knowledge retrieval, answer synthesis, and validation, achieved dramatic improvements, especially for mid-range models. The LLaMA 3.3-70B-based agentic system reached 89.2% accuracy versus 69.5% for its base model, with substantial gains on level 3 complexity questions. The multi-agent approach transformed inconsistent subspecialty performance into uniform excellence, addressing neurological reasoning challenges that persisted with RAG enhancement. We validated our approach using an independent dataset of 155 neurological cases from MedQA. Results confirm that structured multi-agent approaches designed to emulate specialized cognitive processes significantly enhance complex medical reasoning, offering promising directions for AI assistance in challenging clinical contexts.
Ayush Noori, Joaquín Polonuer, Katharina Meyer
et al.
Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates testable hypotheses across molecular, organoid, and clinical systems. To evaluate PROTON, we apply it to Parkinson's disease (PD), bipolar disorder (BD), and Alzheimer's disease (AD). In PD, PROTON linked genetic risk loci to genes essential for dopaminergic neuron survival and predicted pesticides toxic to patient-derived neurons, including the insecticide endosulfan, which ranked within the top 1.29% of predictions. In silico screens performed by PROTON reproduced six genome-wide $α$-synuclein experiments, including a split-ubiquitin yeast two-hybrid system (normalized enrichment score [NES] = 2.30, FDR-adjusted $p < 1 \times 10^{-4}$), an ascorbate peroxidase proximity labeling assay (NES = 2.16, FDR $< 1 \times 10^{-4}$), and a high-depth targeted exome sequencing study in 496 synucleinopathy patients (NES = 2.13, FDR $< 1 \times 10^{-4}$). In BD, PROTON predicted calcitriol as a candidate drug that reversed proteomic alterations observed in cortical organoids derived from BD patients. In AD, we evaluated PROTON predictions in health records from $n = 610,524$ patients at Mass General Brigham, confirming that five PROTON-predicted drugs were associated with reduced seven-year dementia risk (minimum hazard ratio = 0.63, 95% CI: 0.53-0.75, $p < 1 \times 10^{-7}$). PROTON generated neurological hypotheses that were evaluated across molecular, organoid, and clinical systems, defining a path for AI-driven discovery in neurological disease.
This report documents the development and evaluation of domain-specific language models for neurology. Initially focused on building a bespoke model, the project adapted to rapid advances in open-source and commercial medical LLMs, shifting toward leveraging retrieval-augmented generation (RAG) and representational models for secure, local deployment. Key contributions include the creation of neurology-specific datasets (case reports, QA sets, textbook-derived data), tools for multi-word expression extraction, and graph-based analyses of medical terminology. The project also produced scripts and Docker containers for local hosting. Performance metrics and graph community results are reported, with future possible work open for multimodal models using open-source architectures like phi-4.
Prenatal ultrasound evaluates fetal growth and detects congenital abnormalities during pregnancy, but the examination of ultrasound images by radiologists requires expertise and sophisticated equipment, which would otherwise fail to improve the rate of identifying specific types of fetal central nervous system (CNS) abnormalities and result in unnecessary patient examinations. We construct a deep learning model to improve the overall accuracy of the diagnosis of fetal cranial anomalies to aid prenatal diagnosis. In our collected multi-center dataset of fetal craniocerebral anomalies covering four typical anomalies of the fetal central nervous system (CNS): anencephaly, encephalocele (including meningocele), holoprosencephaly, and rachischisis, patient-level prediction accuracy reaches 94.5%, with an AUROC value of 99.3%. In the subgroup analyzes, our model is applicable to the entire gestational period, with good identification of fetal anomaly types for any gestational period. Heatmaps superimposed on the ultrasound images not only provide a visual interpretation for the algorithm but also provide an intuitive visual aid to the physician by highlighting key areas that need to be reviewed, helping the physician to quickly identify and validate key areas. Finally, the retrospective reader study demonstrates that by combining the automatic prediction of the DL system with the professional judgment of the radiologist, the diagnostic accuracy and efficiency can be effectively improved and the misdiagnosis rate can be reduced, which has an important clinical application prospect.
Shaheer Ahmad Khan, Muhammad Usamah Shahid, Ahmad Abdullah
et al.
This study addresses a critical gap in the healthcare system by developing a clinically meaningful, practical, and explainable disease surveillance system for multiple chronic diseases, utilizing routine EHR data from multiple U.S. practices integrated with CureMD's EMR/EHR system. Unlike traditional systems--using AI models that rely on features from patients' labs--our approach focuses on routinely available data, such as medical history, vitals, diagnoses, and medications, to preemptively assess the risks of chronic diseases in the next year. We trained three distinct models for each chronic disease: prediction models that forecast the risk of a disease 3, 6, and 12 months before a potential diagnosis. We developed Random Forest models, which were internally validated using F1 scores and AUROC as performance metrics and further evaluated by a panel of expert physicians for clinical relevance based on inferences grounded in medical knowledge. Additionally, we discuss our implementation of integrating these models into a practical EMR system. Beyond using Shapley attributes and surrogate models for explainability, we also introduce a new rule-engineering framework to enhance the intrinsic explainability of Random Forests.
Alberto Pliego Marugán, Jesús M. Pinar-Pérez, Fausto Pedro García Márquez
Efficient maintenance has always been essential for the successful application of engineering systems. However, the challenges to be overcome in the implementation of Industry 4.0 necessitate new paradigms of maintenance optimization. Machine learning techniques are becoming increasingly used in engineering and maintenance, with reinforcement learning being one of the most promising. In this paper, we propose a gamma degradation process together with a novel maintenance model in which repairs are increasingly imperfect, i.e., the beneficial effect of system repairs decreases as more repairs are performed, reflecting the degradational behavior of real-world systems. To generate maintenance policies for this system, we developed a reinforcement-learning-based agent using a Double Deep Q-Network architecture. This agent presents two important advantages: it works without a predefined preventive threshold, and it can operate in a continuous degradation state space. Our agent learns to behave in different scenarios, showing great flexibility. In addition, we performed an analysis of how changes in the main parameters of the environment affect the maintenance policy proposed by the agent. The proposed approach is demonstrated to be appropriate and to significatively improve long-run cost as compared with other common maintenance strategies.
Franco Oberti, Stefano Di Carlo, Alessandro Savino
The Controller Area Network (CAN) protocol, essential for automotive embedded systems, lacks inherent security features, making it vulnerable to cyber threats, especially with the rise of autonomous vehicles. Traditional security measures offer limited protection, such as payload encryption and message authentication. This paper presents a novel Intrusion Detection System (IDS) designed for the CAN environment, utilizing Hardware Performance Counters (HPCs) to detect anomalies indicative of cyber attacks. A RISC-V-based CAN receiver is simulated using the gem5 simulator, processing CAN frame payloads with AES-128 encryption as FreeRTOS tasks, which trigger distinct HPC responses. Key HPC features are optimized through data extraction and correlation analysis to enhance classification efficiency. Results indicate that this approach could significantly improve CAN security and address emerging challenges in automotive cybersecurity.
Aims
This study focuses on the North Central London Child and Adolescent Mental Health Services (NCL CAMHS) Co-production workstream, initiated to establish co-production as a foundational method for service planning and delivery in the NCL region.
To understand what the CAMHS experts by experience members found useful and did not find useful in co-production projects within Barnet Enfield and Haringey Mental Health NHS Trust and NCL wide co-production.
Methods
Semi-structured interviews conducted with experts by experience within the Barnet Enfield and Haringey (BEH) NHS Trust aimed to explore their co-production experiences, identifying facilitators and barriers. The study employed an inductive thematic analysis, grounded in a constructionist epistemological position, to analyse qualitative responses from semi-structured interviews. Braun and Clarke's (2006) methodology guided the analysis, consisting of six phases. The researchers emphasized reflexivity, reflection, and maintaining coherence, consistency, and flexibility throughout the recursive process. The voices of the lived experience co-production members played a central role in the research, influencing the entire report.
Two members of the NCL CAMHS lived experience group served as “Lived Experience Researchers” and received training on coding reliability based on Braun and Clarke's (2006) guidance.
Results
Thematic analysis revealed several key findings. Recognition of co-production values within the group highlighted the importance of giving voice to service users, valuing their individual experiences, and promoting power-sharing. Facilitators included good team working, valuing diversity, accessible online sessions, and promoting equality through interactions. Conversely, barriers included inconsistent meeting timings, power imbalances, and a consultation-style dominance. Participants expressed the need for more involved projects and recommended a transformation of BEH's co-production strategy.
Conclusion
Recommendations for BEH include a comprehensive evaluation of their co-production projects on the ladder of participation, emphasizing the importance of higher-level collaborations. Training for staff on co-production principles is crucial for fostering a mindset shift, and the establishment of a dedicated co-production team, including a co-production lead, is advised by service-users who co-produce. These roles can drive co-production projects, provide organizational structure, and facilitate stakeholder engagement.
Gellan K. Ahmed, Gellan K. Ahmed, Ahmed A. Karim
et al.
BackgroundAvoidant Restrictive Food Intake Disorder (ARFID) is a newly classified eating disorder that requires further understanding of its presentation. There is no previous report of ARFID in a child post-tonsillectomy. ARFID may be a potential negative outcome for children following oropharyngeal surgery.Case presentationA female child aged 10 years and 2 months presented with ARFID associated with depression, anxiety and nutritional deficiency following tonsillectomy. She had more difficulty in swallowing solids than fluids and had repeated vomiting and spitting food after chewing it. She became dehydrated and malnourished with a BMI of 10.5 and was misdiagnosed with myasthenic gravis.ConclusionsTo our knowledge, this is the first case report of ARFID in a child post-tonsillectomy. We discuss the pathophysiology of ARFID, which remains elusive, and recommend psychiatric assessment when evaluating children post operative tonsillectomy.
Jessilyn Dunn, Varun Mishra, Md Mobashir Hasan Shandhi
et al.
Smartphones and wearable sensors offer an unprecedented ability to collect peripheral psychophysiological signals across diverse timescales, settings, populations, and modalities. However, open-source software development has yet to keep pace with rapid advancements in hardware technology and availability, creating an analytical barrier that limits the scientific usefulness of acquired data. We propose a community-driven, open-source peripheral psychophysiological signal pre-processing and analysis software framework that could advance biobehavioral health by enabling more robust, transparent, and reproducible inferences involving autonomic nervous system data.
The opening or closing mechanism of a voltage-gated ion channel is triggered by the potential difference crossing the cell membrane in the nervous system. Based on this picture, we model the ion channel as a nanoscale two-terminal ionic tunneling junction. External time-varying voltage exerting on the two-terminal ionic tunneling junction mimics the stimulation of neurons from the outside. By deriving the quantum Langevin equation from quantum mechanics, the ion channel current is obtained by the quantum tunneling of ions controlled by the time-varying voltage. The time-varying voltage induces an effective magnetic flux which causes quantum coherence in ion tunnelings and leads to sideband effects in the ion channel current dynamics. The sideband effects in the ionic current dynamics manifest a multi-crossing hysteresis in the I-V curve, which is the memory dynamics responding to the variation of the external voltage. Such memory dynamics is defined as the active quantum memory with respect to the time-varying stimuli. We can quantitatively describe how active quantum memory is generated and changed. We find that the number of the non-zero cross points in the I-V curve hysteresis and the oscillation of the differential conductance are the characteristics for quantitatively describing the active quantum memory. We also explore the temperature dependence of the active quantum memory in such a system. The discovery of this active quantum memory characteristics provides a new understanding about the underlying mechanism of ion channel dynamics.
Sai Krishna Tikka, Sangha Mitra Godi, M Aleem Siddiqui
et al.
Repetitive transcranial magnetic stimulation (rTMS) is potentially effective as an augmentation strategy in the treatment of many neuropsychiatric conditions. Several Indian studies have been conducted in this regard. We aimed to quantitatively synthesize evidence from Indian studies assessing efficacy and safety of rTMS across broad range of neuropsychiatric conditions. Fifty two studies- both randomized controlled and non-controlled studies were included for a series of random-effects meta-analyses. Pre-post intervention effects of rTMS efficacy were estimated in “active only” rTMS treatment arms/groups and “active vs sham” (sham-controlled) studies using pooled Standardized Mean Differences (SMDs). The outcomes were ‘any depression’, depression in unipolar/bipolar depressive disorder, depression in obsessive compulsive disorder (OCD), depression in schizophrenia, schizophrenia symptoms (positive, negative, total psychopathology, auditory hallucinations and cognitive deficits), obsessive compulsive symptoms of OCD, mania, craving/compulsion in substance use disorders (SUDs) and migraine (headache severity and frequency). Frequencies and odds ratios (OR) for adverse events were calculated. Methodological quality of included studies, publication bias and sensitivity assessment for each meta-analyses was conducted. Meta-analyses of “active only” studies suggested a significant effect of rTMS for all outcomes, with moderate to large effect sizes, at both end of treatment as well as at follow-up. However, except for migraine (headache severity and frequency) with large effect sizes at end of treatment only and craving in alcohol dependence where moderate effect size at follow-up only, rTMS was not found to be effective for any outcome in the series of “active vs sham” meta-analyses. Significant heterogeneity was seen. Serious adverse events were rare. Publication bias was common and the sham controlled positive results lost significance in sensitivity analysis. We conclude that rTMS is safe and shows positive results in ‘only active’ treatment groups for all the studied neuropsychiatric conditions. However, the sham-controlled evidence for efficacy is negative from India.
Conclusion
rTMS is safe and shows positive results in “only active” treatment groups for all the studied neuropsychiatric conditions. However, the sham-controlled evidence for efficacy is negative from India.
Abstract Introduction This study aimed to investigate the long-term prognostic effects of different alteplase doses on patients with acute ischemic stroke (AIS). Methods In this cohort study, we enrolled 501 patients with AIS treated with intravenous thrombolysis with alteplase, with the primary endpoint event of recurrence of ischemic stroke and the secondary endpoint event of death. The effects of different doses of alteplase on recurrence of ischemic stroke and death were analyzed using a Cox proportional risk model. Results Among 501 patients with AIS treated with thrombolysis, 295 patients (58.9%) and 206 patients (41.1%) were treated with low-dose and standard-dose alteplase, respectively. During the study period, 61 patients (12.2%) had a confirmed recurrence of ischemic stroke. Multivariate Cox proportional risk analysis showed that standard-dose alteplase thrombolysis (HR 0.511, 95% CI 0.288–0.905, P = 0.021) was significantly associated with a reduced risk of long-term recurrence of AIS, whereas atrial fibrillation was associated with an increased risk of long-term recurrence of AIS. Thirty-nine (7.8%) patients died during the study period. Multivariate Cox proportional risk analysis showed that age, baseline National Institutes of Health Stroke Scale (NIHSS) score, and symptomatic steno-occlusion were associated with an increased long-term risk of death from AIS. The alteplase dose was not associated with the risk of death from AIS. Conclusions Standard-dose alteplase treatment reduced the risk of long-term recurrence of AIS after hospital discharge and the alteplase dose was not associated with the long-term risk of death from AIS.
Mao-Qiang Man, Mao-Qiang Man, Mao-Qiang Man
et al.
Autism spectrum disorder (ASD) is a common neurological disorder. Although the etiologies of ASD have been widely speculated, evidence also supports the pathogenic role of cutaneous inflammation in autism. The prevalence of ASD is higher in individuals with inflammatory dermatoses than in those without inflammatory diseases. Anti-inflammation therapy alleviates symptoms of ASD. Recent studies suggest a link between epidermal dysfunction and ASD. In the murine model, mice with ASD display epidermal dysfunction, accompanied by increased expression levels of proinflammatory cytokines in both the skin and the brain. Children with ASD, which develops in their early lifetime, also exhibit altered epidermal function. Interestingly, improvement in epidermal function alleviates some symptoms of ASD. This line of evidence suggests a pathogenic role of cutaneous dysfunction in ASD. Either an improvement in epidermal function or effective treatment of inflammatory dermatoses can be an alternative approach to the management of ASD. We summarize here the current evidence of the association between the skin and ASD.
Introduction
Poor sleep quality is a major health problem worldwide. University students tend to suffer from problems of sleep regularity, quantity and quality, which can affect their academic performance, and have a serious impact on their psychological and physical well-being.
Objectives
The aim of this study was to assess the prevalence of insomnia among Tunisian university students, and to identify its associated factors.
Methods
We conducted a cross-sectional web-based study among university students from several Tunisian faculties. Data were collected using a questionnaire spread throughout social media (Facebook), using the Google Forms® platform, during September and October 2022.
We used the “Insomnia Severity Index” (ISI) to assess the severity of insomnia.
Results
A total of 144 students completed the questionnaire. Their mean age was 23.38±3.27 years, with a sex-ration (F/M) of 2.8. Among them, 70.1% were single and 68.8% lived with family. Among our participants, 10.4% were followed for chronic somatic disease, 11.1% for chronic mental disease, while 29.2% have already been diagnosed and treated for sleep disturbances.
ISI showed that 72.2% of students suffered from insomnia: 45.1% Subthreshold insomnia, 19.4% moderate clinical insomnia and 7.6% severe clinical insomnia. Insomnia was significantly more frequent among psychoactive substances users (75.7% vs 57.6%; p=0.043). ISI scores were significantly higher among cannabis users (17.4 vs 11.06; p=0.025).
Conclusions
Our study highlighted that insomnia is prevalent within the university student population, and psychoactive substances consumption seems to worsen it. Thus, when designing interventions to improve sleep quality among students, the main determinants need to be taken into consideration.
Disclosure of Interest
None Declared