Hasil untuk "Neurology. Diseases of the nervous system"

Yeshun Wu, Xiaolin Xu, Zijun Chen et al.

Highlights • Coronoviruses not only affect the respiratory system, but also have deleterious effects on the central nervous system.• Most neurological diseases could be caused by coronoviruses invasion.• Coronoviruses cause nerve damage via diverse pathways.

I. Koralnik, K. Tyler

In less than 6 months, the severe acute respiratory syndrome‐coronavirus type 2 (SARS‐CoV‐2) has spread worldwide infecting nearly 6 million people and killing over 350,000. Initially thought to be restricted to the respiratory system, we now understand that coronavirus disease 2019 (COVID‐19) also involves multiple other organs, including the central and peripheral nervous system. The number of recognized neurologic manifestations of SARS‐CoV‐2 infection is rapidly accumulating. These may result from a variety of mechanisms, including virus‐induced hyperinflammatory and hypercoagulable states, direct virus infection of the central nervous system (CNS), and postinfectious immune mediated processes. Example of COVID‐19 CNS disease include encephalopathy, encephalitis, acute disseminated encephalomyelitis, meningitis, ischemic and hemorrhagic stroke, venous sinus thrombosis, and endothelialitis. In the peripheral nervous system, COVID‐19 is associated with dysfunction of smell and taste, muscle injury, the Guillain‐Barre syndrome, and its variants. Due to its worldwide distribution and multifactorial pathogenic mechanisms, COVID‐19 poses a global threat to the entire nervous system. Although our understanding of SARS‐CoV‐2 neuropathogenesis is still incomplete and our knowledge is evolving rapidly, we hope that this review will provide a useful framework and help neurologists in understanding the many neurologic facets of COVID‐19. ANN NEUROL 2020;88:1–11 ANN NEUROL 2020;88:1–11

C. Calderón-Ospina, M. O. Nava-Mesa

Neurotropic B vitamins play crucial roles as coenzymes and beyond in the nervous system. Particularly vitamin B1 (thiamine), B6 (pyridoxine), and B12 (cobalamin) contribute essentially to the maintenance of a healthy nervous system. Their importance is highlighted by many neurological diseases related to deficiencies in one or more of these vitamins, but they can improve certain neurological conditions even without a (proven) deficiency.

Sarah A. Kent, V. Miron

Weiting Chen, Weiting Chen, Xiaoshuang Jiang et al.

BackgroundTracheostomy is common in traumatic cervical spinal cord injury (TCSCI) because of respiratory complications, yet objective tools to estimate individual risk remain limited.MethodsIn this single-center retrospective cohort at the Second Affiliated Hospital, Zhejiang University School of Medicine, we enrolled 308 consecutive ICU admissions with TCSCI (January 2018–March 2023) and randomly split the cohort 7:3 (outcome-stratified) into training (n = 215) and validation (n = 93) sets. Candidate admission predictors were screened with Least Absolute Shrinkage and Selection Operator and then entered into multivariable logistic regression to construct a nomogram. Model performance included discrimination (AUC with bootstrap 95% CIs, 2,000 resamples), calibration (intercept, slope, Brier), and decision curve analysis (DCA). A prespecified clinical threshold of 0.30 was used to summarize sensitivity and specificity.ResultsFive independent predictors were retained—smoking history, thoracic injury, BMI ≥ 25 kg/m2, cervical dislocation, and ASIA grade (A vs. B-D). The model showed strong discrimination (AUC 0.844, 95% CI 0.788–0.896 in training; 0.903, 95% CI 0.823–0.966 in validation) and good calibration. At the 0.30 threshold, performance was Sensitivity 0.781/Specificity 0.725 (training) and Sensitivity 0.812/Specificity 0.852 (validation); DCA demonstrated greater net benefit than “treat all/none” across threshold 0.10–0.70.ConclusionA parsimonious, five-factor nomogram based on routine admission data provides accurate, clinically interpretable stratification of tracheostomy risk in TCSCI. Clear reporting of ASIA coding and a prespecified decision threshold enhance bedside usability. Prospective, multi-center external validation is warranted.

Xiaoduo Liu, Tao Wei, Bo Zhao et al.

Abstract Background Sleep is essential for brain homeostasis, in part by supporting glymphatic clearance through sleep-related oscillations. However, the relationship between putative glymphatic metrics and coupled sleep rhythm disruption, and their combined role in Alzheimer’s disease (AD) progression, remains poorly understood. Methods We analyzed data from 75 individuals, 54 with AD and 21 cognitively normal (CN) controls, including sleep electroencephalography (EEG), magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) AD biomarkers, and two-year longitudinal cognitive assessments. Putative glymphatic metrics was evaluated using choroid plexus (CP) volume, perivascular spaces (PVSs), diffusion tensor imaging along the perivascular space (DTI-ALPS) index, and blood oxygen level-dependent signal coupled to CSF signal (BOLD-CSF coupling). Coupled sleep rhythm was assessed via slow oscillation (SO)-theta and SO-spindle couplings. Correlation and mediation analyses explored associations between these MRI-derived indices and coupled sleep oscillations, and least absolute shrinkage and selection operator (LASSO) regression was used to predict AD progression. Results Compared to CN controls, individuals with AD had reduced DTI-ALPS index and BOLD-CSF coupling (p < 0.05), along with disrupted SO-spindle coupling (p = 0.029). Across all participants, lower global BOLD-CSF coupling correlated with misaligned SO-theta burst coupling (r = 0.311, p = 0.018), and reduced DTI-ALPS was associated with misaligned SO-spindle coupling (r = 0.370, p = 0.008). In the AD group, DTI-ALPS remained correlated with SO-spindle misalignment (r = 0.376, p = 0.028). Mediation analysis revealed that SO-spindle misalignment contributed to cognitive decline through its effect on DTI-ALPS. Importantly, combining putative glymphatic and sleep EEG metrics effectively predicted AD progression. Conclusions Our findings suggest that disruptions in surrogates marker of glymphatic clearance and coupled sleep rhythms are jointly associated with AD-related cognitive decline. These metrics offer a promising framework for predicting disease progression and understanding neurodegenerative mechanisms in AD. Graphical Abstract

Yuhong He, Ke Wang, Niri Su et al.

Along with mounting evidence that gut microbiota and their metabolites migrate endogenously to distal organs, the ‘gut–lung axis,’ ‘gut–brain axis,’ ‘gut–liver axis’ and ‘gut–renal axis’ have been established. Multiple animal recent studies have demonstrated gut microbiota may also be a key susceptibility factor for neurological disorders such as Alzheimer's disease, Parkinson's disease and autism. The gastrointestinal tract is innervated by the extrinsic sympathetic and vagal nerves and the intrinsic enteric nervous system, and the gut microbiota interacts with the nervous system to maintain homeostatic balance in the host gut. A total of 1507 publications on the interactions between the gut microbiota, the gut–brain axis and neurological disorders are retrieved from the Web of Science to investigate the interactions between the gut microbiota and the nervous system and the underlying mechanisms involved in normal and disease states. We provide a comprehensive overview of the effects of the gut microbiota and its metabolites on nervous system function and neurotransmitter secretion, as well as alterations in the gut microbiota in neurological disorders, to provide a basis for the possibility of targeting the gut microbiota as a therapeutic agent for neurological disorders.

Chanh Nguyen, Erik Elmroth, Monowar Bhuyan

Serverless computing has redefined cloud application deployment by abstracting infrastructure and enabling on-demand, event-driven execution, thereby enhancing developer agility and scalability. However, maintaining consistent application performance in serverless environments remains a significant challenge. The dynamic and transient nature of serverless functions makes it difficult to distinguish between benign and anomalous behavior, which in turn undermines the effectiveness of traditional anomaly detection methods. These conventional approaches, designed for stateful and long-running services, struggle in serverless settings where executions are short-lived, functions are isolated, and observability is limited. In this first comprehensive vision paper on anomaly detection for serverless systems, we systematically explore the unique challenges posed by this paradigm, including the absence of persistent state, inconsistent monitoring granularity, and the difficulty of correlating behaviors across distributed functions. We further examine a range of threats that manifest as anomalies, from classical Denial-of-Service (DoS) attacks to serverless-specific threats such as Denial-of-Wallet (DoW) and cold start amplification. Building on these observations, we articulate a research agenda for next-generation detection frameworks that address the need for context-aware, multi-source data fusion, real-time, lightweight, privacy-preserving, and edge-cloud adaptive capabilities. Through the identification of key research directions and design principles, we aim to lay the foundation for the next generation of anomaly detection in cloud-native, serverless ecosystems.

Xinyang Zhou, Yongyong Ren, Qianqian Zhao et al.

Accurate diagnosis of Mendelian diseases is crucial for precision therapy and assistance in preimplantation genetic diagnosis. However, existing methods often fall short of clinical standards or depend on extensive datasets to build pretrained machine learning models. To address this, we introduce an innovative LLM-Driven multi-agent debate system (MD2GPS) with natural language explanations of the diagnostic results. It utilizes a language model to transform results from data-driven and knowledge-driven agents into natural language, then fostering a debate between these two specialized agents. This system has been tested on 1,185 samples across four independent datasets, enhancing the TOP1 accuracy from 42.9% to 66% on average. Additionally, in a challenging cohort of 72 cases, MD2GPS identified potential pathogenic genes in 12 patients, reducing the diagnostic time by 90%. The methods within each module of this multi-agent debate system are also replaceable, facilitating its adaptation for diagnosing and researching other complex diseases.

Marta Girona Alarcón, Willy Kuo, Mattia Humbel et al.

Current approaches to in vivo imaging of the mouse central nervous system (CNS) do not offer a combination of micrometer resolution and a whole-brain field of view. To address this limitation, we introduce an approach based on synchrotron radiation-based hard X-ray micro computed tomography (SR$μ$CT). We performed intravital SR$μ$CT acquisitions of mouse CNS fluid spaces at three synchrotron radiation facilities. Imaging was conducted on both anesthetized free-breathing and ventilated animals, with and without retrospective cardiac gating. We achieved whole-brain imaging at 6.3 $μ$m uniform voxel size, observed the distribution of cerebrospinal fluid (CSF) contrast agent over time and quantified choroid plexus movement. SR$μ$CT bridges the gap between multiphoton microscopy and magnetic resonance imaging, offering dynamic imaging with micrometer-scale resolution and whole-organ field of view. Intravital SR$μ$CT will play a crucial role in validating and integrating hypotheses on CSF dynamics and solute transport by providing unique data that cannot be acquired otherwise.

Weike Zhao, Chaoyi Wu, Yanjie Fan et al.

Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exceeding five years, marked by repeated referrals, misdiagnoses, and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burdens. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support powered by large language models, integrating over 40 specialized tools and up-to-date knowledge sources. DeepRare processes heterogeneous clinical inputs, including free-text descriptions, structured Human Phenotype Ontology terms, and genetic testing results, to generate ranked diagnostic hypotheses with transparent reasoning linked to verifiable medical evidence. Evaluated across nine datasets from literature, case reports and clinical centres across Asia, North America and Europe spanning 14 medical specialties, DeepRare demonstrates exceptional performance on 3,134 diseases. In human-phenotype-ontology-based tasks, it achieves an average Recall@1 of 57.18%, outperforming the next-best method by 23.79%; in multi-modal tests, it reaches 69.1% compared with Exomiser's 55.9% on 168 cases. Expert review achieved 95.4% agreement on its reasoning chains, confirming their validity and traceability. Our work not only advances rare disease diagnosis but also demonstrates how the latest powerful large-language-model-driven agentic systems can reshape current clinical workflows.

Tharaka Fonseka, Buddhi Wijenayake, Athulya Ratnayake et al.

Understanding the relationship between population dynamics and disease-specific mortality is central to evidence-based health policy. This study introduces two novel metrics, PoPDivergence and PoPStat, one to quantify the difference between population pyramids and the other to assess the strength and nature of their association with the mortality of a given disease. PoPDivergence, based on Kullback-Leibler divergence, measures deviations between a countrys population pyramid and a reference pyramid. PoPStat is the correlation between these deviations and the log form of disease-specific mortality rates. The reference population is selected by a brute-force optimization that maximizes this correlation. Utilizing mortality data from the Global Burden of Disease 2021 and population statistics from the United Nations, we applied these metrics to 371 diseases across 204 countries. Results reveal that PoPStat outperforms traditional indicators such as median age, GDP per capita, and Human Development Index in explaining the mortality of most diseases. Noncommunicable diseases (NCDs) like neurological disorders and cancers, communicable diseases (CDs) like neglected tropical diseases, and maternal and neonatal diseases were tightly bound to the underlying demographic attributes whereas NCDs like diabetes, CDs like respiratory infections and injuries including self-harm and interpersonal violence were weakly associated with population pyramid shapes. Notably, except for diabetes, the NCD mortality burden was shared by constrictive population pyramids, while mortality of communicable diseases, maternal and neonatal causes and injuries were largely borne by expansive pyramids. Therefore, PoPStat provides insights into demographic determinants of health and empirical support for models on epidemiological transition. Code and scripts: https://github.com/Buddhi19/DevisingPoPStat.git

Nir Kaplan, Gadi Zerach, Yossi Levi-Belz

IntroductionExposure to potentially morally injurious events (PMIEs) has been found to contribute to mental health difficulties (MHD). However, research on PMIE exposure and its consequences among teachers is scant. In this study, we aimed to narrow this gap by examining the associations between teachers’ exposure to PMIEs and measures of depression, anxiety, burnout, and intention to leave the profession. Furthermore, we examined the mediating role of expressions of moral injury (i.e., shame and guilt) in these associations.MethodA sample of 253 Israeli teachers (186 female, 73%) aged 23-66 (Mage = 44, SD = 10.36) completed validated self-report questionnaires assessing the study variables.ResultsThe findings demonstrated that exposure to PMIEs contributed significantly to depression, anxiety, burnout, and intention to leave the profession. Through structural equation model analysis, we found that expressions of moral injury mediated the association between PMIEs and MHD.DiscussionThis study underscores the need to address moral injury among teachers as an essential factor for maintaining their mental health, as well as the overall sustainability of the educational system. Early screening and interventions are needed to identify and treat teachers at risk for MHD stemming from moral injury.

Shengzhang Su, Ian N. Levasseur, Kimberly M. Alonge

Md. Adil Faizan, V. Murali Krishna, Tialam Gautham et al.

Background: Mental illness continues to be a significant public health challenge, with stigma acting as a barrier to seeking care and improving outcomes. Healthcare students, particularly medical and nursing students, play an influential role in shaping future societal attitudes toward mental health. Their attitudes and perceptions toward mental illness can directly impact the care patients receive and influence how mental health issues are addressed within the healthcare system. Stigma among healthcare students can undermine the quality of patient care, discourage individuals from seeking help, and perpetuate harmful stereotypes that affect wider societal views. This study compares the stigma toward mental illness between medical and nursing students from two educational institutions in Khammam and Warangal, located in Telangana. Telangana was chosen as the study location due to its unique cultural and educational context, which may provide valuable insights into regional variations in stigma and perceptions of mental health. Materials and Methods: A cross-sectional study was conducted with 827 students from private medical college (Khammam) and government medical college (Warangal). The Mental Illness Clinicians’ Attitudes-2 (MICA-2) scale was used for medical students, while the modified MICA-4 scale was employed for nursing students to measure attitudes toward mental illness. In addition, sociodemographic data, including gender, previous contact with individuals with mental illness, and academic semester, were collected. Results: A total of 827 students participated in the study, with 57.4% of medical students and 42.6% of nursing students. Medical students exhibited significantly higher stigma scores (41.07 ± 6.74) compared to nursing students (38.07 ± 7.44, P < 0.001). Male students had higher stigma levels (41.37 ± 7.06) than female students (36.57 ± 7.33, P < 0.001). Students with prior contact with individuals suffering from mental illness showed lower stigma scores (35.42 ± 8.91) compared to those without prior contact (39.35 ± 6.14, P < 0.001). Students from Warangal had lower stigma scores (38.13 ± 7.03) compared to those from Khammam (39.15 ± 6.54, P < 0.05). Post hoc analysis revealed that medical students from private medical college, Khammam (MedKh) had the highest stigma, followed by nursing students from the same institution. Students from government medical college, Warangal (MedWar) exhibited intermediate stigma, with Government nursing college, Warangal (NurWar) showing the lowest stigma levels. Conclusion: This study highlights significant differences in stigma levels between students from various institutions and regions, emphasizing the importance of addressing stigma in healthcare education. To reduce stigma, it is crucial to integrate anti-stigma programs into medical and nursing curricula, with a focus on mental health awareness. In addition, increasing clinical exposure to mental health settings and fostering direct interaction with individuals experiencing mental illness can help reduce prejudice and promote more compassionate care. These actionable steps can support the development of a more empathetic and stigma-free healthcare workforce.

Michelle Chang

Luciana Cristina Mancio Balico, Gabrielle Siota Schramm, Eduarda Taube Rotta et al.

ABSTRACT Introduction This study explores the associations between Adverse Childhood Experiences (ACEs) and child health in Brazil using data from the PEARLS-BR study. It aims to assess the prevalence and impact of ACEs in a Brazilian cultural context and their relationship with health outcomes. Methods A cross-sectional study was conducted at a Multidisciplinary Health Care Clinical Center and a General Hospital - Reference Center for Child and Adolescent Care, involving 202 caregivers of children and teens aged 0 to 18 years. The PEARLS-BR instrument was used to document the frequency and distribution of ACEs and related life events and their association with health outcomes. Results Caregivers participants reported a median of 2 (IQR 1-5) adversities of their child, with 78.2% reporting at least one adversity. Higher PEARLS-BR scores were significantly associated with poorer physical health (OR: 1.18, 95% CI: 1.01–1.38) and mental health (OR: 1.50, 95% CI: 1.33–1.71), ADHD symptoms (OR: 1.21, 95% CI: 1.09–1.37), infections (OR: 1.13, 95% CI: 1.02–1.26), gastrointestinal disorders (OR: 1.26, 95% CI: 1.12–1.43), and headaches/migraines (OR: 1.22, 95% CI: 1.11–1.35). Related life events were linked to higher odds of obesity (OR: 1.37, 95% CI: 1.02–1.88) and atopic conditions (OR: 1.28, 95% CI: 1.01–1.63). Conclusions The PEARLS-BR score identifies children at risk for various adverse health outcomes. The study highlights the need for targeted interventions and comprehensive strategies to address the impact of childhood adversities on health, providing valuable insights for public health strategies and clinical practices in Brazil.

Zifan Geng, Yan Zhu, Quan-Lin Li et al.

The gastrointestinal (GI) tract plays an essential role in food digestion, absorption, and the mucosal immune system; it is also inhabited by a huge range of microbes. The GI tract is densely innervated by a network of 200–600 million neurons that comprise the enteric nervous system (ENS). This system cooperates with intestinal microbes, the intestinal immune system, and endocrine systems; it forms a complex network that is required to maintain a stable intestinal microenvironment. Understanding how gut microbes influence the ENS and central nervous system (CNS) has been a significant research subject over the past decade. Moreover, accumulating evidence from animal and clinical studies has revealed that gut microbiota play important roles in various neurological diseases. However, the causal relationship between microbial changes and neurological disorders currently remains unproven. This review aims to summarize the possible contributions of GI microbiota to the ENS and CNS. It also provides new insights into furthering our current understanding of neurological disorders.

Bushra F. Alsaqer, Alaa F. Alsaqer, Amna Asif

The use of Electronic Health Records (EHRs) has increased dramatically in the past 15 years, as, it is considered an important source of managing data od patients. The EHRs are primary sources of disease diagnosis and demographic data of patients worldwide. Therefore, the data can be utilized for secondary tasks such as research. This paper aims to make such data usable for research activities such as monitoring disease statistics for a specific population. As a result, the researchers can detect the disease causes for the behavior and lifestyle of the target group. One of the limitations of EHRs systems is that the data is not available in the standard format but in various forms. Therefore, it is required to first convert the names of the diseases and demographics data into one standardized form to make it usable for research activities. There is a large amount of EHRs available, and solving the standardizing issues requires some optimized techniques. We used a first-hand EHR dataset extracted from EHR systems. Our application uploads the dataset from the EHRs and converts it to the ICD-10 coding system to solve the standardization problem. So, we first apply the steps of pre-processing, annotation, and transforming the data to convert it into the standard form. The data pre-processing is applied to normalize demographic formats. In the annotation step, a machine learning model is used to recognize the diseases from the text. Furthermore, the transforming step converts the disease name to the ICD-10 coding format. The model was evaluated manually by comparing its performance in terms of disease recognition with an available dictionary-based system (MetaMap). The accuracy of the proposed machine learning model is 81%, that outperformed MetaMap accuracy of 67%. This paper contributed to system modelling for EHR data extraction to support research activities.

Hilal Abboud, F. Z. Abboud, H. Kharbouch et al.

Background Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-Cov-2, resulting in severe acute respiratory syndrome, with high potential of spreading and infecting humans worldwide. Since December 2019, when the virus was identified in humans, the literature on COVID-19 has grown exponentially and extrarespiratory symptoms including neurologic symptoms are increasingly highlighted. Methods Given the high and increasing number of publications reporting neurologic involvements of SARS-Cov-2, we thought that providing an update for neurologic complications of COVID-19 would be useful for physicians and especially young trainees in neurology and neurosurgery. Indeed, in this review we discuss several neurologic aspects reported in the literature to date including the evidence and pathways of neuroinvasion in COVID-19 and the main neurologic disorders reported in the literature to date, as well as future perspectives and the potential long-term consequence of current neuroinfection in COVID-19 patients. Results Currently, there is convincing evidence that SARS-CoV-2, the etiologic agent of COVID-19, can affect the nervous system, with damage and neurologic alterations. These neurologic disorders are grouped into several categories, ranging from nonspecific and moderate symptoms such as headache, myalgia, and hyposmia to severe symptoms including cerebrovascular disease and intracranial infections. Severe neurologic symptoms such as acute cerebrovascular disease occur only in a minority of patients with usual risk factors and are associated with poor outcome. However, most COVID-19 patients exhibit only minor or mild neurologic symptoms. Conclusions Management of COVID-19 patients should include early clinical, radiologic, and laboratory neurologic assessment, with a close follow-up, especially in severe forms. Future studies should assess late and long-term consequences of current COVID-19 patients with neurologic involvement.