LL. D., Professor of the Diseases of the Mind and Nervous System, and of Medical Jurisprudence, Missouri Medical College, St. Louis ; late Physician-in-Chief to St. Vincent’s Institution for the Insane ; Corresponding Member of the New York Society of Neurology and Electrology ; formerly Consulting Physician of the St. Louis County Lunatic Asylum ; Member of the New York Medico-Legal Society, etc. Second edition. Philadelphia : J. B. Lippincott Co. 1892.
Millions of children worldwide are affected by severe rare Mendelian disorders, yet exome and genome sequencing still fail to provide a definitive molecular diagnosis for a large fraction of patients, prolonging the diagnostic odyssey. Bridging this gap increasingly requires transitioning from DNA-only interpretation to multi-modal diagnostic reasoning that combines genomic data, transcriptomic sequencing (RNA-seq), and phenotype information; however, computational frameworks that coherently integrate these signals remain limited. Here we present RareCollab, an agentic diagnostic framework that pairs a stable quantitative Diagnostic Engine with Large Language Model (LLM)-based specialist modules that produce high-resolution, interpretable assessments from transcriptomic signals, phenotypes, variant databases, and the literature to prioritize potential diagnostic variants. In a rigorously curated benchmark of Undiagnosed Diseases Network (UDN) patients with paired genomic and transcriptomic data, RareCollab achieved 77% top-5 diagnostic accuracy and improved top-1 to top-5 accuracy by ~20% over widely used variant-prioritization approaches. RareCollab illustrates how modular artificial intelligence (AI) can operationalize multi-modal evidence for accurate, scalable rare disease diagnosis, offering a promising path toward reducing the diagnostic odyssey for affected families.
We aim to report an extremely rare case of a primary thoracic intramedullary cavernoma with Brown-Sequard syndrome (BSS), its transcranial magnetic stimulation (TMS)/somatosensory evoked potential (SSEP) neurophysiology tests, and their localizing value. A 53-year-old Chinese male with a history of multiple arteriovenous malformations (AVMs) presented with an intermittent 3-year history of the left lower limb weakness with recent worsening and findings of dissociated sensory loss. Neurophysiological testing showed prolonged central motor conduction time to his left lower limb on TMS while tibial SSEP showed prolonged P37 latencies. Magnetic resonance imaging spine showed a T4-5 intramedullary expansile enhancing cord lesion, suggestive of a thoracic cavernoma, with surrounding acute hematomyelia and cord edema from C7 to T6. A spinal angiogram did not reveal any vascular malformation. He was conservatively treated for possible T4-5 cavernoma with hematomyelia. Repeat imaging showed complete resolution of edema with a T3-5 internal T2-weighted hyperintensity and residual susceptibility focus likely representing a cavernoma that had bled with no evidence of AVM. A repeat tibial SSEP still showed prolonged tibial SSEPs, but TMS was now normal. Primary thoracic intramedullary cavernomas may be a rare cause of BSS. TMS and SSEP may have a role in the diagnostic evaluation of BSS.
BackgroundChronic subdural hematoma (CSDH) has high postoperative recurrence rates. This study investigates the effects of hyperbaric oxygen therapy (HBOT) combined with Medical-Psychosocial-Nursing Functional Support (MPNFS) on functional recovery and recurrence prevention in CSDH patients, and establishes a recurrence prediction model.MethodsA total of 184 CSDH patients undergoing burr hole drainage were randomized into a control group and an observation group (HBOT + MPNFS). Neurological (NIHSS), motor (Fugl-Meyer Assessment), and quality-of-life (SF-36) outcomes were assessed preoperatively and at 1-month postoperatively. Complications and 6-month recurrence rates were recorded. Univariate/multivariate logistic regression identified recurrence risk factors, with ROC analysis evaluating predictive accuracy.ResultsThe observation group showed superior 1-month outcomes: lower NIHSS scores (t = 4.94, p < 0.001), higher FMA and SF-36 scores (p < 0.01). Complication and recurrence rates were significantly reduced (p < 0.05). Independent recurrence predictors included brain atrophy (OR = 2.877), poor brain reexpansion (OR = 3.165), preoperative hematoma width ≥ 20 mm (OR = 2.782), and absence of combined intervention (OR = 2.842). The multifactorial model achieved an AUC of 0.7862, indicating robust predictive efficacy.ConclusionHyperbaric oxygen therapy combined with MPNFS enhances neurological/motor recovery, improves quality of life, and reduces complications/recurrence in postoperative CSDH patients.
Diego D. Díaz-Guerra, Marena de la C. Hernández-Lugo, Yunier Broche-Pérez
et al.
IntroductionEvaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed.ObjectivesThis paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms.MethodologyThe proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers.Anticipated resultsThe AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies.ConclusionsBy integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.
BackgroundCompared to single-shell diffusion tensor imaging (DTI), free water (FW) and neurite orientation dispersion and density imaging (NODDI) offer a more comprehensive evaluation of microstructural alterations in cerebral white matter (WM), particularly in detecting crossing fibers. However, research utilizing multi-shell diffusion imaging to investigate thyroid-associated ophthalmopathy (TAO) remains limited. This study employs FW and NODDI to investigate microstructural changes in the white matter of the visual pathways in patients with TAO.MethodsMulti-shell diffusion magnetic resonance imaging (dMRI) scans were performed on 45 patients with TAO and 31 age- and sex-matched healthy controls (HC). Tract-based spatial statistics (TBSS) analysis was conducted using eight FW and NODDI-derived metrics to identify group differences in white matter microstructure. Furthermore, correlations between these microstructural changes and clinical measures were examined.ResultsTBSS analysis revealed that, compared to HC, patients with TAO exhibited lower free-water corrected fractional anisotropy (fwFA) and free-water corrected axial diffusivity (fwAD), while free-water corrected mean diffusivity (fwMD), free-water corrected radial diffusivity (fwRD), and orientation dispersion index (ODI) were significantly increased (p < 0.05, FWE). Notably, ODI demonstrated the highest area under the curve (AUC) among these metrics. Furthermore, fwFA, fwAD, fwMD, fwRD, and ODI showed significant correlations with the Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), and the Graves’ Orbitopathy Quality of Life Questionnaire (GO-QOL2) scores.ConclusionThis study suggests that abnormalities in the white matter microstructure of TAO patients can be detected through the complementary use of FW and NODDI metrics, and it is revealed that these changes may have an impact on mental health.
Emily Kaczmarek, Justin Szeto, Brennan Nichyporuk
et al.
3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurological conditions, including neurodegenerative disorders and stroke. While deep learning models have shown promising results analyzing 3D MRI across a number of brain imaging tasks, most are highly tailored for specific tasks with limited labeled data, and are not able to generalize across tasks and/or populations. The development of self-supervised learning (SSL) has enabled the creation of large medical foundation models that leverage diverse, unlabeled datasets ranging from healthy to diseased data, showing significant success in 2D medical imaging applications. However, even the very few foundation models for 3D brain MRI that have been developed remain limited in resolution, scope, or accessibility. In this work, we present a general, high-resolution SimCLR-based SSL foundation model for 3D brain structural MRI, pre-trained on 18,759 patients (44,958 scans) from 11 publicly available datasets spanning diverse neurological diseases. We compare our model to Masked Autoencoders (MAE), as well as two supervised baselines, on four diverse downstream prediction tasks in both in-distribution and out-of-distribution settings. Our fine-tuned SimCLR model outperforms all other models across all tasks. Notably, our model still achieves superior performance when fine-tuned using only 20% of labeled training samples for predicting Alzheimer's disease. We use publicly available code and data, and release our trained model at https://github.com/emilykaczmarek/3D-Neuro-SimCLR, contributing a broadly applicable and accessible foundation model for clinical brain MRI analysis.
Introduction
Sexual dysfunction (SD) is common in psychotic illness including schizophrenia, occurring in 30-82% of patients. It negatively impacts wellbeing and antipsychotic compliance, resulting in higher risk of relapse and hospitalisation. Due to over-reliance on spontaneous reports from patients, SD is typically under-identified which prevents investigation and treatment.
Objectives
To establish whether SD is under-identified in patients with psychosis in a general adult community mental health team; to elicit whether the Arizona Sexual Experience Scale (ASEX) improves identification; to investigate and manage identified cases of SD; to make recommendations about identification and monitoring of SD in this patient population.
Methods
A 12-month retrospective audit of patients with psychosis prescribed a long-acting injectable (LAI) antipsychotic (n=36) to identify sexual symptoms was completed. The ASEX was subsequently issued to screen for SD.
Results
Audit: 3/36 (8%) patients had documented sexual symptoms. Of the 18/36 patients that completed the ASEX: 10 (56%) exhibited SD. 4 consented to further investigation. 5 patients experienced significant difficulties with the language used in the ASEX. At the end of the project we revised the ASEX with simpler, colloquial language.
Conclusions
Implementation of the ASEX results in clear improvements in identification and monitoring of SD. Maudsley Practice Guidelines can inform investigation and management of SD. We suggest a review of NICE guidance to incorporate the above into clinical practice. Further work is needed to establish whether the revised ASEX can be developed and validated.
Disclosure of Interest
None Declared
The system architecture controlling a group of robots is generally set before deployment and can be either centralized or decentralized. This dichotomy is highly constraining, because decentralized systems are typically fully self-organized and therefore difficult to design analytically, whereas centralized systems have single points of failure and limited scalability. To address this dichotomy, we present the Self-organizing Nervous System (SoNS), a novel robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multi-level system architectures. For example, a robot swarm consisting of $n$ independent robots could transform into a single $n$-robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy. Leveraging the SoNS approach, we show that sensing, actuation, and decision-making can be coordinated in a locally centralized way, without sacrificing the benefits of scalability, flexibility, and fault tolerance, for which swarm robotics is usually studied. In several proof-of-concept robot missions -- including binary decision-making and search-and-rescue -- we demonstrate that the capabilities of the SoNS approach greatly advance the state of the art in swarm robotics. The missions are conducted with a real heterogeneous aerial-ground robot swarm, using a custom-developed quadrotor platform. We also demonstrate the scalability of the SoNS approach in swarms of up to 250 robots in a physics-based simulator, and demonstrate several types of system fault tolerance in simulation and reality.
Roman Serrat, Alexandre Oliveira-Pinto, Giovanni Marsicano
et al.
Mitochondrial calcium handling is a particularly active research area in the neuroscience field, as it plays key roles in the regulation of several functions of the central nervous system, such as synaptic transmission and plasticity, astrocyte calcium signaling, neuronal activity{\ldots} In the last few decades, a panel of techniques have been developed to measure mitochondrial calcium dynamics, relying mostly on photonic microscopy, and including synthetic sensors, hybrid sensors and genetically encoded calcium sensors. The goal of this review is to endow the reader with a deep knowledge of the historical and latest tools to monitor mitochondrial calcium events in the brain, as well as a comprehensive overview of the current state of the art in brain mitochondrial calcium signaling. We will discuss the main calcium probes used in the field, their mitochondrial targeting strategies, their key properties and major drawbacks. In addition, we will detail the main roles of mitochondrial calcium handling in neuronal tissues through an extended report of the recent studies using mitochondrial targeted calcium sensors in neuronal and astroglial cells, in vitro and in vivo.
Elizaveta Lavrova, Henry C. Woodruff, Hamza Khan
et al.
Medical imaging technologies have undergone extensive development, enabling non-invasive visualization of clinical information. The traditional review of medical images by clinicians remains subjective, time-consuming, and prone to human error. With the recent availability of medical imaging data, quantification have become important goals in the field. Radiomics, a methodology aimed at extracting quantitative information from imaging data, has emerged as a promising approach to uncover hidden biological information and support decision-making in clinical practice. This paper presents a review of the radiomic pipeline from the clinical neuroimaging perspective, providing a detailed overview of each step with practical advice. It discusses the application of handcrafted and deep radiomics in neuroimaging, stratified by neurological diagnosis. Although radiomics shows great potential for increasing diagnostic precision and improving treatment quality in neurology, several limitations hinder its clinical implementation. Addressing these challenges requires collaborative efforts, advancements in image harmonization methods, and the establishment of reproducible and standardized pipelines with transparent reporting. By overcoming these obstacles, radiomics can significantly impact clinical neurology and enhance patient care.
Many sexually mature females suffer from premenstrual syndrome (PMS), but effective coping methods for PMS are limited due to the complexity of symptoms and unclear pathogenesis. Awareness has shown promise in alleviating PMS symptoms but faces challenges in long-term recording and consistency. Our research goal is to establish a convenient and simple method to make individual female aware of their own psychological, and autonomic conditions. In previous research, we demonstrated that participants could be classified into non-PMS and PMS groups based on mood scores obtained during the follicular phase. However, the properties of neurophysiological activity in the participants classified by mood scores have not been elucidated. This study aimed to classify participants based on their scores on a mood questionnaire during the follicular phase and to evaluate their autonomic nervous system (ANS) activity using a simple device that measures pulse waves from the earlobe. Participants were grouped into Cluster I (high positive mood) and Cluster II (low mood). Cluster II participants showed reduced parasympathetic nervous system activity from the follicular to the menstrual phase, indicating potential PMS symptoms. The study demonstrates the feasibility of using mood scores to classify individuals into PMS and non-PMS groups and monitor ANS changes across menstrual phases. Despite limitations such as sample size and device variability, the findings highlight a promising avenue for convenient PMS self-monitoring.
Introduction
The increasing global suicide rates pose a considerable strain on healthcare professionals. Subsequently, their attitudes toward suicide prevention may influence suicide risk and management, affecting the quality of care.
Objectives
To investigate the attitudes of Pakistani medical students toward suicide and its comparison with different sociodemographic factors.
Methods
A total of 1392 undergraduate medical students belonging to all five years took part in the cross-sectional study conducted in September 2022. In addition to socio-demographic factors, participants were asked about their attitudes toward suicide on a 5-point Likert scale using the ATTS (Attitudes towards suicide) questionnaire. Questions explored competence, religion, experience, and views on suicidal behavior and its treatment. Data were analyzed by using SPSS 26.
Results
The majority of respondents had no prior experience of looking after patients with suicide attempts (88.9%), the experience of having known someone who died by suicide (67.1%), or participation in suicide workshops (94.3%). Statistically significant items showed that males believed more strongly that suicide could be used to end suffering and would consider the possibility of doing it, revenge is the major driving factor, talking about suicide lessens its incidence, and people should have the right to take their own lives. Females more strongly believed that loneliness is the major driving factor, and that suicide is preventable. Preclinical students more strongly believed thought suicide was less justified, especially among young people, not a solution to end incurable illnesses, and that people should not have the right to take their own lives. 996 (71.6%) of respondents expressed their willingness to participate in workshops regarding suicide.
Conclusions
Our study suggests that medical students have little experience in handling suicidal patients and vastly differ in their attitudes. There is a need for suicide management training and further study data to support these findings.
Disclosure of Interest
None Declared
Introduction
The BFI-2-S assesses the domain level of the Big Five with three prototypical facets of each domain capturing approximately 91% of the total variance in the full BFI-2 domain scales and approximately 89% of the predictive power of the BFI-2 facets in German adaptations and their original American versions.
Objectives
The study aims to investigate the psychometric properties of the Arabic adaptation of the BFI-2 short form.
Methods
The Arabic version of the BFI-2-S a 30-item with 15 and NEO Five-Factor Inventory (NEO–PI-R) were administered to 1560 (576 males, 984 females) Kuwait University undergraduates with a mean age = 22.75 ± 3.81. The internal consistency reliability, factor structure, and convergent validity of the BFI-2-S with NEO–PI-R were assessed.
Results
Cronbach’s alpha was satisfactory for N (0.79), E (0.73), O (0.73), A (0.76) and C (0.77). Results revealed significant gender differences in O, C & E with a favor for males and in N a favor with females. PCA showed that BFI-2-S five factors explains 64.38% of the total variance. However, the high mean correlations between the BFI-2-S and NEO–PI-R scales, with coefficients of (0.67) for the N, (0.66) for the E, (0.56) for the C, (0.61) for the A, and (0.58) for the C. The convergence between each BFI-2-S domain correlated substantially with the relevant NEO-PI-R domain scales, with the average correlation being .62.
Conclusions
The findings support the psychometric properties of the Arabic adaptations of the BFI-2-S as useful instruments for assessing the Big Five.
Disclosure
No significant relationships.
Parkinson's Disease (PD) is a progressive nervous system disorder that has affected more than 5.8 million people, especially the elderly. Due to the complexity of its symptoms and its similarity to other neurological disorders, early detection requires neurologists or PD specialists to be involved, which is not accessible to most old people. Therefore, we integrate smart mobile devices with AI technologies. In this paper, we introduce the framework of our developed PD early detection system which combines different tasks evaluating both motor and non-motor symptoms. With the developed model, we help users detect PD punctually in non-clinical settings and figure out their most severe symptoms. The results are expected to be further used for PD rehabilitation guidance and detection of other neurological disorders.
This paper deals with the problem of designing a sampled-data state feedback control law for continuous-time linear control systems subject to uniform input quantization. The sampled-data state feedback is designed to ensure the uniform global asymptotic stability (UGAS) of an attractor surrounding the origin. The closed-loop system is rewritten as a hybrid dynamical system. To do this, an auxiliary clock variable triggering the occurrence of sampling events is introduced. A numerically tractable algorithm with feasibility guarantees, based on concave-convex decomposition, is then proposed allowing to minimize the size of the attractor. Theoretical results are illustrated in a numerical example.
Aneta D. Krakowski, Peter Szatmari, Peter Szatmari
et al.
Background: Many phenotypic studies have estimated the degree of comorbidity between Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), but few have examined the latent, or unobserved, structure of combined ASD and ADHD symptoms. This is an important perquisite toward better understanding the overlap between ASD and ADHD.Methods: We conducted a scoping review of studies that examined the factor or latent class structure of ASD and ADHD symptoms within the same clinical or general population sample.Results: Eight studies met final inclusion criteria. Four factor analysis studies found that ASD and ADHD domains loaded separately and one found that some ASD and ADHD domains loaded together. In the three latent class studies, there were evidence of profiles with high levels of co-occurring ASD and ADHD symptoms.Conclusions: Our scoping review provides some evidence of phenotypic overlap between ASD and ADHD at the latent, or unobserved, level, particularly when using a “person-centered” (latent class analysis) vs. a “variable-centered” (factor analysis) approach.
Abstract Background Previous research using whole-brain neuroimaging techniques has revealed structural differences of grey matter (GM) in alcohol use disorder (AUD) patients. However, some of the findings diverge from other neuroimaging studies and require further replication. The quantity of relevant research has, thus far, been limited and the association between GM and abstinence duration of AUD patients has not yet been systematically reviewed. Methods The present research conducted a meta-analysis of voxel-based GM studies in AUD patients published before Jan 2021. The study utilised a whole brain-based d-mapping approach to explore GM changes in AUD patients, and further analysed the relationship between GM deficits, abstinence duration and individual differences. Results The current research included 23 studies with a sample size of 846 AUD patients and 878 controls. The d-mapping approach identified lower GM in brain regions including the right cingulate gyrus, right insula and left middle frontal gyrus in AUD patients compared to controls. Meta-regression analyses found increasing GM atrophy in the right insula associated with the longer mean abstinence duration of the samples in the studies in our analysis. GM atrophy was also found positively correlated with the mean age of the samples in the right insula, and positively correlated with male ratio in the left middle frontal gyrus. Conclusions GM atrophy was found in the cingulate gyrus and insula in AUD patients. These findings align with published meta-analyses, suggesting they are potential deficits for AUD patients. Abstinence duration, age and gender also affect GM atrophy in AUD patients. This research provides some evidence of the underlying neuroanatomical nature of AUD.