Hasil untuk "Neurology. Diseases of the nervous system"

M. Rao, M. Gershon

Maria Lamarca, Maria Lamarca, Maria Lamarca et al.

BackgroundWhile psychological interventions are effective at improving symptoms of psychosis, accessible, cost- and time-efficient treatments remain limited. Personalized medicine has emerged as a promising approach, tailoring interventions to individual needs. Metacognitive Training (MCT), with its established efficacy and adaptable format, is well-suited for personalization. The PERMEPSY project (Towards a Personalized Medicine Approach to Psychological Treatment for Psychosis) aims to deliver tailored MCT intervention for individuals with psychosis.MethodsPERMEPSY is an international study funded by ERAPerMed (JTC2022) involving five clinical partners (Spain, Chile, France, Germany, Poland) and one technological partner (Spain). The project involves a proof-of-concept clinical trial recruiting 51 participants from each center for a total of 255 adult participants with psychosis in a prospective study (Registration: NCT06603922, 19-09-2024). The trial will test the efficacy of a Machine Learning (ML)-derived platform at predicting clinical and functional outcomes from baseline scores and compare a personalized MCT (P-MCT) to a classical MCT based on the platform’s predictions.AimsPERMEPSY seeks to (1) develop and test the predictive power of an algorithm that could support decision-making, and (2) ascertain whether P-MCT is more effective than MCT at improving key symptoms and cognitive impairments associated to psychosis.ResultsA harmonized retrospective database enabled the development of a predictive ML algorithm, integrated into an innovative platform. This platform provides clinicians with the information needed to deliver P-MCT. Predictions include changes in positive symptoms (e.g., delusions), insight, self-esteem, and treatment adherence.DiscussionBy integrating diverse data types and innovative technology, PERMEPSY addresses the need for personalized, effective treatment in psychosis, aiming to reduce individual and systemic burdens while supporting clinicians in their decision-making.

Rowzatul Zannat, Abdullah Al Shafi, Abdul Muntakim

Increased access to reliable health information is essential for non-English-speaking populations, yet resources in Bangla for disease prediction remain limited. This study addresses this gap by developing a comprehensive Bangla symptoms-disease dataset containing 758 unique symptom-disease relationships spanning 85 diseases. To ensure transparency and reproducibility, we also make our dataset publicly available. The dataset enables the prediction of diseases based on Bangla symptom inputs, supporting healthcare accessibility for Bengali-speaking populations. Using this dataset, we evaluated multiple machine learning models to predict diseases based on symptoms provided in Bangla and analyzed their performance on our dataset. Both soft and hard voting ensemble approaches combining top-performing models achieved 98\% accuracy, demonstrating superior robustness and generalization. Our work establishes a foundational resource for disease prediction in Bangla, paving the way for future advancements in localized health informatics and diagnostic tools. This contribution aims to enhance equitable access to health information for Bangla-speaking communities, particularly for early disease detection and healthcare interventions.

Jing Jin, Yun Wang, Sixiang Liang et al.

Objective: Repetitive transcranial magnetic stimulation (rTMS) has been shown to alleviate depressive and anxiety symptoms in patients with major depressive disorder (MDD), typically by targeting the dorsolateral (DLPFC) or dorsomedial prefrontal cortex (DMPFC). Based on a pre-registered randomized controlled trial, this study presents an exploratory neuroimaging analysis investigating the impact of rTMS targeting the DLPFC versus the DMPFC on functional connectivity with the default mode network (DMN) and frontal-parietal network (FPN) in patients with MDD. Methods: Sixty-four MDD patients were randomly assigned to DLPFC-rTMS (n = 36) or DMPFC-rTMS (n = 28) groups for a 21-day intervention. Symptoms were evaluated with Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA). Changes in individualized functional connectivity (inFC) between individualized targets and DMN/FPN were assessed and correlated with symptom improvements. As a control analysis, FC was evaluated based on the group-based seeds of DLPFC or DMPFC. Additionally, symptom-specific circuit map comparisons were conducted. Results: Both groups showed symptom improvements and changes in inFC with the DMN and FPN, but the specific connectivity profiles differ. In the DMN, the DLPFC-rTMS group showed decreased negative connectivity between left DLPFC and precuneus (t = -2.39, p = 0.022), while the DMPFC-rTMS group showed increased positive inFC between DMPFC and precuneus (t = -2.78, p = 0.01, FDR adjusted p = 0.034) and PCC (t = -3.15, p = 0.004, FDR adjusted p = 0.028). In the FPN, the DLPFC group showed decreased negative inFC with medial superior frontal gyrus (t = -2.35, p = 0.024) and decreased positive inFC with inferior parietal lobule (t = 2.3, p = 0.028). The DMPFC group showed increased positive connectivity with inferior frontal gyrus (t = -3.65, p = 0.001, FDR adjusted p = 0.019) and su pplementary motor area (t = -2.24, p = 0.033), and decreased negative connectivity with middle cingulate cortex (t = 2.27, p = 0.032). Canonical correlation analysis revealed a strong association between inFC changes and depression symptom improvement in the DMPFC-rTMS group (r = 0.57). Group seed-based FC changes were limited to the FPN and correlated with depressive improvement in the DLPFC-rTMS group (r = 0.52). Symptom-specific circuit maps linked to depression and anxiety were consistent across targets. Conclusion: Both DLPFC and DMPFC rTMS alleviate depressive and anxiety symptoms, displaying similar overall circuit patterns but distinct connectivity changes specific to their targets.

Mingxia Wei, Jincheng Li, Tongyao You et al.

Abstract Background Cognitive impairment (CI) poses a major global health challenge. In China, neuropsychological scales, regarded as the gold standard for cognitive diagnosis, are largely inaccessible in resource-limited communities. The Mobile Eye-Tracking Application (m-ETA), which captures and quantifies eye movement features, has emerged as a promising tool for CI screening. Methods We developed a tablet-based m-ETA using a two-step approach. First, a logistic regression (LR) model was trained to discriminate dementia based on six oculometric features in a hospital cohort (N = 204), and regression analyses were conducted to validate the biological relevance of these features with Alzheimer’s Disease–related phenotypes in an exploratory dataset (N = 101). Second, the generalizability and accuracy of the LR model were externally validated in a community-based cohort (N = 433) and further evaluated in two real-world community populations (N = 2,685). Model performance was assessed using sensitivity, specificity, negative predictive value (NPV), and area under the ROC curve (AUC). Results m-ETA achieved high diagnostic accuracy for dementia (AUC = 0.99). Regression analyses confirmed that the m-ETA-derived oculometric features were significantly associated with cognitive performance, brain atrophy, and tau deposition in the exploratory dataset (all P < 0.05). m-ETA accurately detected CI (AUC = 0.80), with excellent negative predictive value for ruling out CI, and identified individuals with lower cognition performance across diverse communities. Conclusions m-ETA offers a low-cost, non-invasive, and efficient tool for large-scale CI screening, particularly suited to underserved and low-literacy communities in China.

Sebastian Crutch, Claire Waddington, Emma Harding et al.

Rarer dementias are associated with atypical symptoms and younger onset, which result in a higher burden of care. We provide a review of the global literature on longitudinal decline in activities of daily living (ADLs) in dementias that account for less than 10% of dementia diagnoses. Published studies were identified through searches conducted in Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Excerpta Medica Care (Emcare), PsycINFO, and Cumulative Index in Nursing and Allied Health Literature (CINAHL). The search criteria included terms related to ‘rarer dementias’, ‘activities of daily living’ and ‘longitudinal or cross-sectional studies’ following a predefined protocol registered. Studies were screened, and those that met the criteria were citation searched. Quality assessments were performed, and relevant data were extracted. 20 articles were selected, of which 19 focused on dementias within the frontotemporal dementia/primary progressive aphasia spectrum, while one addressed posterior cortical atrophy. Four studies were cross-sectional and 16 studies were longitudinal, with a median duration of 2.2 years. The Disability Assessment for Dementia was used to measure decline in 8 of the 20 studies. The varied sequences of ADL decline reported in the literature reflect variation in diagnostic specificity between studies and within-syndrome heterogeneity. Most studies used Alzheimer’s disease staging scales to measure decline, which cannot capture variant-specific symptoms. To enhance care provision in dementia, ADL scales could be deployed postdiagnosis to aid treatment and planning. This necessitates staging scales that are variant-specific and span the disease course from diagnosis to end of life. PROSPERO registration number: CRD42021283302.

Ke Feng, Martin Haenggi

Physical contact or proximity is often a necessary condition for the spread of infectious diseases. Common destinations, typically referred to as hubs or points of interest, are arguably the most effective spots for the type of disease spread via airborne transmission. In this work, we model the locations of individuals (agents) and common destinations (hubs) by random spatial point processes in $\mathbb{R}^d$ and focus on disease propagation through agents visiting common hubs. The probability of an agent visiting a hub depends on their distance through a connection function $f$. The system is represented by a random bipartite geometric (RBG) graph. We study the degrees and percolation of the RBG graph for general connection functions. We show that the critical density of hubs for percolation is dictated by the support of the connection function $f$, which reveals the critical role of long-distance travel (or its restrictions) in disease spreading.

Yu Chao, Siyu Lin, xiaorong wang et al.

We introduce LLM x MapReduce-V3, a hierarchically modular agent system designed for long-form survey generation. Building on the prior work, LLM x MapReduce-V2, this version incorporates a multi-agent architecture where individual functional components, such as skeleton initialization, digest construction, and skeleton refinement, are implemented as independent model-context-protocol (MCP) servers. These atomic servers can be aggregated into higher-level servers, creating a hierarchically structured system. A high-level planner agent dynamically orchestrates the workflow by selecting appropriate modules based on their MCP tool descriptions and the execution history. This modular decomposition facilitates human-in-the-loop intervention, affording users greater control and customization over the research process. Through a multi-turn interaction, the system precisely captures the intended research perspectives to generate a comprehensive skeleton, which is then developed into an in-depth survey. Human evaluations demonstrate that our system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

S. Dimauro, E. Schon

C. García Sánchez, I. Martín Galledo, A. Guerra Huelves et al.

Aleksandra Beric, Yichen Sun, Santiago Sanchez et al.

Abstract To identify circRNAs associated with Parkinson’s disease (PD) we leveraged two of the largest publicly available studies with longitudinal clinical and blood transcriptomic data. We performed a cross-sectional study utilizing the last visit of each participant (N = 1848), and a longitudinal analysis that included 1166 participants with at least two time points. We identified 192 differentially expressed circRNAs, with effects that were sustained during disease, in mutation carriers, and diverse ancestry. The 192 circRNAs were leveraged to distinguish between PD and healthy participants with a ROC AUC of 0.797. Further, 71 circRNAs were sufficient to distinguish between genetic PD (AUC71 = 0.954) and, at-risk participants (AUC71 = 0.929) and healthy controls, supporting that circRNAs have the potential to aid the diagnosis of PD. Finally, we identified five circRNAs highly correlated with symptom severity. Overall, we demonstrated that circRNAs play an important role in PD and can be clinically relevant to improve diagnostic and monitoring.

Robert J. Goddard, Wim P. Krijnen, Vincent Roelfsema et al.

Introduction: Bruxism is a repetitive masticatory muscle activity that may cause substantial morbidity and reduce the quality of life in children with profound intellectual and multiple disabilities. Assessment methods most commonly used were caregiver reporting and dental examination, This systematic review with meta-analysis aims to determine the prevalence of bruxism in children with profound intellectual and multiple disabilities and to describe the currently used assessment methods for bruxism in this population. Methods: We conducted a systematic review and meta-analysis using a multi-component search strategy. We used a random effects model to calculate the prevalence and 95 % confidence intervals for each study, for all studies combined, and specifically for Rett syndrome (RS), cerebral palsy (CP), Down syndrome (DS), and “other disorders (primarily Angelman syndrome and Prader–Willi syndrome).” Results: The prevalence for the entire group based on a random effects model was found to be 49 % (95 %CI 41–57 %) with high heterogeneity (I2 = 93 %, p < 0.01), for RS 74 % (95 %CI 53–88 %, I2 = 84 %, p < 0.01), CP 48 % (95 %CI 38–57 %, I2 = 86 %, p < 0.01), DS 40 % (95 %CI 33–47 %, I2 = 60 %, p < 0.01) and “other disorders” 40 % (95 %CI 18–67 %, I2 = 98 %, p < 0.01). The group prevalences were not equal, indicating a significant difference (P-value = 0.03), with a notably higher likelihood of RS. Conclusion: We observed a five-fold increased likelihood of bruxism in children with profound intellectual and multiple disabilities. The disorder with the highest prevalence was Rett syndrome, with a seven-fold increased likelihood of bruxism. The increased likelihood of bruxism in this vulnerable group of children demands clinicians pay heed to this substantial morbidity.

Xuanzhong Chen, Ye Jin, Xiaohao Mao et al.

Rare diseases, despite their low individual incidence, collectively impact around 300 million people worldwide due to the vast number of diseases. The involvement of multiple organs and systems, and the shortage of specialized doctors with relevant experience, make diagnosing and treating rare diseases more challenging than common diseases. Recently, agents powered by large language models (LLMs) have demonstrated notable applications across various domains. In the medical field, some agent methods have outperformed direct prompts in question-answering tasks from medical examinations. However, current agent frameworks are not well-adapted to real-world clinical scenarios, especially those involving the complex demands of rare diseases. To bridge this gap, we introduce RareAgents, the first LLM-driven multi-disciplinary team decision-support tool designed specifically for the complex clinical context of rare diseases. RareAgents integrates advanced Multidisciplinary Team (MDT) coordination, memory mechanisms, and medical tools utilization, leveraging Llama-3.1-8B/70B as the base model. Experimental results show that RareAgents outperforms state-of-the-art domain-specific models, GPT-4o, and current agent frameworks in diagnosis and treatment for rare diseases. Furthermore, we contribute a novel rare disease dataset, MIMIC-IV-Ext-Rare, to facilitate further research in this field.

Tianqi Wei, Zhi Chen, Zi Huang et al.

Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degrades in classifying in-the-wild images. Furthermore, we observed that in-the-wild plant images may exhibit similar appearances across various diseases (i.e., small inter-class discrepancy) while the same diseases may look quite different (i.e., large intra-class variance). Motivated by this observation, we propose an in-the-wild multimodal plant disease recognition dataset that contains the largest number of disease classes but also text-based descriptions for each disease. Particularly, the newly provided text descriptions are introduced to provide rich information in textual modality and facilitate in-the-wild disease classification with small inter-class discrepancy and large intra-class variance issues. Therefore, our proposed dataset can be regarded as an ideal testbed for evaluating disease recognition methods in the real world. In addition, we further present a strong yet versatile baseline that models text descriptions and visual data through multiple prototypes for a given class. By fusing the contributions of multimodal prototypes in classification, our baseline can effectively address the small inter-class discrepancy and large intra-class variance issues. Remarkably, our baseline model can not only classify diseases but also recognize diseases in few-shot or training-free scenarios. Extensive benchmarking results demonstrate that our proposed in-the-wild multimodal dataset sets many new challenges to the plant disease recognition task and there is a large space to improve for future works.

Anirudh Mazumder, Jianguo Liu

Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06% on a label-level when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.

Akua Sekyiwaa Osei-Nkwantabisa, Redeemer Ntumy

Heart disease is a serious worldwide health issue because it claims the lives of many people who might have been treated if the disease had been identified earlier. The leading cause of death in the world is cardiovascular disease, usually referred to as heart disease. Creating reliable, effective, and precise predictions for these diseases is one of the biggest issues facing the medical world today. Although there are tools for predicting heart diseases, they are either expensive or challenging to apply for determining a patient's risk. The best classifier for foretelling and spotting heart disease was the aim of this research. This experiment examined a range of machine learning approaches, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks, to determine which machine learning algorithm was most effective at predicting heart diseases. One of the most often utilized data sets for this purpose, the UCI heart disease repository provided the data set for this study. The K-Nearest Neighbor technique was shown to be the most effective machine learning algorithm for determining whether a patient has heart disease. It will be beneficial to conduct further studies on the application of additional machine learning algorithms for heart disease prediction.

Chenxin Miao, Xiaoning Li, Yishu Zhang

In recent years, the understanding of the mechanisms of acupuncture in the treatment of neurological disorders has deepened, and considerable progress has been made in basic and clinical research on acupuncture, but the relationship between acupuncture treatment mechanisms and brain-derived neurotrophic factor (BDNF) has not yet been elucidated. A wealth of evidence has shown that acupuncture exhibits a dual regulatory function of activating or inhibiting different BDNF pathways. This review focuses on recent research advances on the effect of acupuncture on BDNF and downstream signaling pathways in several neurological disorders. Firstly, the signaling pathways of BDNF and its function in regulating plasticity are outlined. Furthermore, this review discusses explicitly the regulation of BDNF by acupuncture in several nervous system diseases, including neuropathic pain, Parkinson’s disease, cerebral ischemia, depression, spinal cord injury, and other diseases. The underlying mechanisms of BDNF regulation by acupuncture are also discussed. This review aims to improve the theoretical system of the mechanism of acupuncture action through further elucidation of the mechanism of acupuncture modulation of BDNF in the treatment of neurological diseases and to provide evidence to support the wide application of acupuncture in clinical practice.

Ali Motahharynia, Ahmad Pourmohammadi, Armin Adibi et al.

Working memory (WM) is one of the most affected cognitive domains in multiple sclerosis (MS), which is mainly studied by the previously established binary model for information storage (slot model). However, recent observations based on the continuous reproduction paradigms have shown that assuming dynamic allocation of WM resources (resource model) instead of the binary hypothesis will give more accurate predictions in WM assessment. Moreover, continuous reproduction paradigms allow for assessing the distribution of error in recalling information, providing new insights into the organization of the WM system. Hence, by utilizing two continuous reproduction paradigms, memory-guided localization (MGL) and analog recall task with sequential presentation, we investigated WM dysfunction in MS. Our results demonstrated an overall increase in recall error and decreased recall precision in MS. While sequential paradigms were better in distinguishing healthy control from relapsing-remitting MS, MGL were more accurate in discriminating MS subtypes (relapsing-remitting from secondary progressive), providing evidence about the underlying mechanisms of WM deficit in progressive states of the disease. Furthermore, computational modeling of the results from the sequential paradigm determined that imprecision in decoding information and swap error (mistakenly reporting the feature of other presented items) were responsible for WM dysfunction in MS. Overall, this study offered a sensitive measure for assessing WM deficit and provided new insight into the organization of the WM system in MS population.

Ximena Palacios-Espinosa, Leonardo Palacios Sánchez

En el 2008, la FDA (Food and Drug Administration) advirtió que los medicamentos antiepilépticos podían desencadenar conductas suicidas en los pacientes con epilepsia. Esto generó diversas reacciones entre académicos, investigadores y clínicos. La presente revisión de tema tuvo como objetivo analizar la situación actual del conocimiento sobre la conducta suicida en las personas con epilepsia, identificar la prevalencia de ésta y los factores de riesgo asociados. Esto se realizó con base en los artículos científicos publicados en bases de datos internacionales. Se encontró que la prevalencia de conductas suicidas en el paciente con epilepsia es diversa, pero ciertamente mayor que en la población general. Dentro de los factores de riesgo médicos, los medicamentos antiepilépticos y el tipo de epilepsia han sido ampliamente identificados como predictores de estas conductas. Entre los factores de riesgo psicológicos están los antecedentes psiquiátricos, especialmente comorbilidad con ansiedad, depresión y antecedentes de suicidio. En cambio, los factores de riesgo sociocultural son escasos y su asociación con la conducta suicida, es aún controvertida. Sin embargo, la edad y el género han sido los factores más asociados con el riesgo suicida. En conclusión, la evidencia confirma la presencia de conducta suicida entre los pacientes con epilepsia; por lo tanto, debe ser objeto de interés y atención por parte de los profesionales del equipo de salud tratante.

Laetitia Lebrun, Lara Absil, Myriam Remmelink et al.

Abstract Introduction COVID-19-infected patients harbour neurological symptoms such as stroke and anosmia, leading to the hypothesis that there is direct invasion of the central nervous system (CNS) by SARS-CoV-2. Several studies have reported the neuropathological examination of brain samples from patients who died from COVID-19. However, there is still sparse evidence of virus replication in the human brain, suggesting that neurologic symptoms could be related to mechanisms other than CNS infection by the virus. Our objective was to provide an extensive review of the literature on the neuropathological findings of postmortem brain samples from patients who died from COVID-19 and to report our own experience with 18 postmortem brain samples. Material and methods We used microscopic examination, immunohistochemistry (using two different antibodies) and PCR-based techniques to describe the neuropathological findings and the presence of SARS-CoV-2 virus in postmortem brain samples. For comparison, similar techniques (IHC and PCR) were applied to the lung tissue samples for each patient from our cohort. The systematic literature review was conducted from the beginning of the pandemic in 2019 until June 1st, 2022. Results In our cohort, the most common neuropathological findings were perivascular haemosiderin-laden macrophages and hypoxic-ischaemic changes in neurons, which were found in all cases (n = 18). Only one brain tissue sample harboured SARS-CoV-2 viral spike and nucleocapsid protein expression, while all brain cases harboured SARS-CoV-2 RNA positivity by PCR. A colocalization immunohistochemistry study revealed that SARS-CoV-2 antigens could be located in brain perivascular macrophages. The literature review highlighted that the most frequent neuropathological findings were ischaemic and haemorrhagic lesions, including hypoxic/ischaemic alterations. However, few studies have confirmed the presence of SARS-CoV-2 antigens in brain tissue samples. Conclusion This study highlighted the lack of specific neuropathological alterations in COVID-19-infected patients. There is still no evidence of neurotropism for SARS-CoV-2 in our cohort or in the literature.