Hasil untuk "Neurology. Diseases of the nervous system"

Saja Al-Dabet, Sherzod Turaev, Nazar Zaki

Abnormal head movements (AHMs) manifest across a broad spectrum of neurological disorders; however, the absence of a multi-condition resource integrating kinematic measurements, clinical severity scores, and patient demographics constitutes a persistent barrier to the development of AI-driven diagnostic tools. To address this gap, this study introduces NeuroPose-AHM, a knowledge-based dataset of neurologically induced AHMs constructed through a multi-LLM extraction framework applied to 1,430 peer-reviewed publications. The dataset contains 2,756 patient-group-level records spanning 57 neurological conditions, derived from 846 AHM-relevant papers. Inter-LLM reliability analysis confirms robust extraction performance, with study-level classification achieving strong agreement (kappa = 0.822). To demonstrate the dataset's analytical utility, a four-task framework is applied to cervical dystonia (CD), the condition most directly defined by pathological head movement. First, Task 1 performs multi-label AHM type classification (F1 = 0.856). Task 2 constructs the Head-Neck Severity Index (HNSI), a unified metric that normalizes heterogeneous clinical rating scales. The clinical relevance of this index is then evaluated in Task 3, where HNSI is validated against real-world CD patient data, with aligned severe-band proportions (6.7%) providing a preliminary plausibility indication for index calibration within the high severity range. Finally, Task 4 performs bridge analysis between movement-type probabilities and HNSI scores, producing significant correlations (p less than 0.001). These results demonstrate the analytical utility of NeuroPose-AHM as a structured, knowledge-based resource for neurological AHM research. The NeuroPose-AHM dataset is publicly available on Zenodo (https://doi.org/10.5281/zenodo.19386862).

Yuksel Dede, Asli Koskderelioglu, Muhtesem Gedizlioglu

Aim: Myasthenia gravis (MG) is an autoimmune neuromuscular junction disease. Sleep quality and quality of life are often affected by MG. This study aimed to evaluate the impact of sleep quality on the quality of life in patients with MG, along with other associated factors. Materials and Methods: A total of 81 patients with MG were recruited. The Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, Beck Depression Inventory (BDI), Fatigue Severity Scale (FSS), and MG-specific 15-item quality of life scale (MG-QoL15) were administered to the patients. The results were statistically compared. Results: In the 81 patients with MG, the median duration of disease was 36 (range, 1–264) months. The mean PSQI score was 5.83 ± 3.51. A significant relationship was found between sleep quality and quality of life, depression, fatigue, and body mass index. Positive correlations were observed between MG-QoL15, BDI, and FSS scores. Female sex, the presence of depression, and obesity were found to be effective in predicting poor sleep quality. Discussion and Conclusion: In this cross-sectional study, we explored the potential relationships between sleep quality, depression, fatigue, and quality of life. Approximately 50% of the study participants experienced poor sleep quality. A significant relationship was found between poor sleep quality and the presence of depression, fatigue, and poor quality of life. Excessive daytime sleepiness was seldom observed. In conclusion, the presence of depression, female sex, and obesity are determinant factors in predicting poor sleep quality in MG.

Lina Ren

Abstract Background Psychiatric disorders impose a substantial burden on individuals and society, and current treatment exhibit limited efficacy. Emerging evidence indicates that blue light exposure can influence mood and psychiatric conditions, yet its underlying mechanisms are not fully understood. Since that systemic inflammation is regarded as an important factor in mental health, this study explores the potential relationships between blue light therapy, immune-related pathways, and psychiatric symptoms. Method In this single-center retrospective study, medical records from 270 hospitalized psychiatric patients were analyzed. Patients received either routine treatment alone or in combination with blue light therapy, and were further stratified by season, treatment duration, and primary diagnosis. Results Blue light therapy was related to significant changes in key inflammation markers and psychiatric symptoms. Notably, we observed seasonal variations in the relationship between immune markers and specific psychiatric symptoms following blue light therapy. Conclusion Blue light therapy may offer a promising adjunctive approach for psychiatric disorders potentially through its associations with systemic inflammation and related symptoms. More studies are needed to explore its pathology and potential applications in clinical settings.

Sushmitha Kota, Rakesh Jayantilal Shah, Hitesh Chandrakant Sheth

Background: Adverse childhood experiences (ACEs) are the potentially traumatic events that occur in childhood. The past literature has shown ACEs that have been linked with negative physical and mental health outcomes in adulthood that may influence on quality of life (QoL) in adulthood. This study assesses the association of various types of ACEs with QoL in adulthood among offspring of schizophrenia patients. Materials and Methods: The study was conducted at hospital for mental health, Vadodara, for 6 months using ACEs–International Questionnaires and World Health Organization–BREF QoL in 66 participants, and independent t-test and Mann–Whitney U-test were used to assess the association between domains of childhood adversities with domains of QoL using SPSS software version 20 for their analysis. Results: The overall childhood adversities noticed were 87.88% (n = 58), and the mean scores of physical domain, psychological domain, social domain, and environmental domain (domains of QoL) were 53.59 ± 13.38, 46.12 ± 9.29, 39.94 ± 19.98, and 49.41 ± 15.44, respectively. We found that there was a significant association of physical abuse, household treating violently, physical neglect, and bullying with physical domain and psychological domain; bullying and household treating violently with the social domain and physical abuse, physical neglect, household treating violently and bullying with environmental domain and bullying with health-related QoL. Conclusions: There is a negative correlation of childhood adversities faced by adult offsprings of schizophrenia patients with QoL. This emphasizes the significance of childhood adversities faced by children of schizophrenia patients.

J. Isern Cabrero, A. Pellisé Guinjoan, P. Rodríguez Parajua et al.

Ylvi Müller, Sebastian Bühner, Victoria Fincke et al.

Abstract Atypical teratoid rhabdoid tumors (AT/RT) are characterized by a poor prognosis and a manifestation within the first 2 years of life. Genetic hallmark of these tumors is the homozygous inactivation of SMARCB1 or, in some rare cases, of SMARCA4. While heterozygous pathogenic variants of SMARCA4 have been described, inter alia, in the context of other CNS malignancies such as medulloblastoma or glioblastoma, the co-occurrence of pathogenic variants in both, SMARCB1 and SMARCA4, in the same AT/RT has to our knowledge not been reported previously. Liquid biopsy, a rapidly developing and promising technique measuring cell-free DNA (cfDNA) in body fluids such as the cerebrospinal fluid (CSF), offers a minimally invasive method to assess disease status. It has yet to be established as a standard procedure in the diagnostic workup of CNS tumors. We present the case of a three-year-old male diagnosed with an AT/RT that exhibits both biallelic alterations of SMARCB1 due to a frameshift mutation and loss of heterozygosity as well as a heterozygous missense variant in SMARCA4 presenting with early disease progression. We employed liquid biopsy successfully to monitor disease progression throughout treatment and the subsequent relapse. We highlight the ramifications that simultaneous alterations in two chromatin-modifying genes may have for tumor biology and clinical course.

Yan Su, Qiulin Wu, Weizhen Li et al.

The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target diseases, and the U-Net segmentation model is used to accurately segment the key lesions of the disease. Finally, based on the ResNet-Agglomerative clustering algorithm, a clustering model is trained on a large number of sample data of similar diseases to obtain a approximate diagnosis of the target disease. Zero-Shot Learning of the target disease is then successfully achieved. To evaluate the validity of the model, we validated our method on a dataset of ophthalmic diseases in CFP modality. The external dataset was used to test its performance, and the accuracy=0.8395, precision=0.8094, recall=0.8463, F1 Score=0.8274, AUC=0.9226, which exceeded the indexes of most Few-Shot Learning and One-Shot Learning models. It proves that our method has great potential and reference value in the medical field, where annotation data is usually scarce and expensive to obtain.

Dingxin Lu, Shurui Wu, Xinyi Huang

With the rising global burden of chronic diseases and the multimodal and heterogeneous clinical data (medical imaging, free-text recordings, wearable sensor streams, etc.), there is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks. We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer. The system builds on the dual-stream architecture of existing visual-linguistic models (e.g., PaLM-E, LLaVA) with four key innovations: (i) pre-training with cross-modal comparison and fine-grained alignment of radiological images, fundus maps, and wearable device photos with corresponding clinical narratives using momentum update encoders and debiased InfoNCE losses; (ii) a time fusion block that integrates irregular visit sequences into the causal Transformer decoder through adaptive time interval position coding; (iii) a disease ontology map adapter that injects ICD-10 codes into visual and textual channels in layers and infers comorbid patterns with the help of a graph attention mechanism. On the MIMIC-IV longitudinal cohort, VL-RiskFormer achieved an average AUROC of 0.90 with an expected calibration error of 2.7 percent.

Samir Abu-Rumeileh, A. Abdelhak, M. Foschi et al.

Abstract The advancing validation and exploitation of cerebrospinal fluid and blood neurofilament light chain protein as a biomarker of neuroaxonal damage has deeply changed the current diagnostic and prognostic approach to neurological diseases. Further, recent studies have provided evidence of potential new applications of this biomarker also in non-primary neurological diseases. In the present review we summarise the current evidence, future perspectives, but also limitations, of neurofilament light chain protein as a cerebrospinal fluid and blood biomarker in several medical fields, including intensive care, surgery, internal medicine and psychiatry. In particular, neurofilament light chain protein is associated with the degree of neurologic impairment and outcome in patients admitted to intensive care units or in the perioperative phase and it seems to be highly interconnected with cardiovascular risk factors. Beyond that, interesting diagnostic and prognostic insights have been provided by the investigation of neurofilament light chain protein in psychiatric disorders as well as in the current coronavirus disease 19 (COVID-19) pandemic and in normal aging. Altogether, current data outline a multifaceted applicability of cerebrospinal fluid and blood neurofilament light chain protein ranging from the critical clinical setting to the development of precision medicine models suggesting a strict interplay between the nervous system pathophysiology and the health-illness continuum.

Asmaa Younis Elsary, Naglaa A. El-Sherbiny

Abstract Background Coronavirus (COVID-19) pandemic is a public health emergency. During the outbreak, a broad range of psychological disorders affected people at the individual, community, and international levels. This study aimed to assess the role of stress-coping strategies in relieving perceived stress among university students during the COVID-19 pandemic. Methods This interventional study was nested on a cross-sectional design and involved students at Faiyum University in 2022. Results Out of a sample of 2640 students, 2176 (82.4%) experienced moderate perceived stress, while 56 (2.1%) had more severe levels. Being female, nonmedical students, and rural inhabitants having a low socioeconomic status were associated with scores for severe and moderate levels of perceived stress. Among the interventional group, Modified Perceived Stress Scale scores significantly decreased after the implementation of the stress-coping program, with a p value < 0.001. Improvements in perceived stress levels were observed among male, medical, and high-socioeconomic-status students. Conclusion Perceived stress levels were associated with being female, engaging in nonmedical study, and having low socioeconomic status and decreased after a stress-coping program was implemented. These findings assert the need to develop regular campaigns to provide psychological support and stress-coping strategies that may help students overcome different stressors.

Y. Denysov, G. Putyatin, S. Moroz et al.

Introduction Recent studies have supported that Lactobacillus plantarum can reduce the severity of anxiety and depression. However, previous studies did not focus on the sleep quality. This study determines whether Lactobacillus Plantarum CECT7485 and Lactobacillus Brevis CECT7480 reduce the severity of insomnia, and improves sleep quality in patients who comorbidity of depression and anxiety disorders. Objectives An assessment of insomniac effects a probiotic supplement containing Lactobacillus Plantarum CECT7485 and Lactobacillus Brevis CECT7480 (PLANTARUM) in patients with anxiety and depression comorbidity undergoing treatment with selective serotonin reuptake inhibitors (SSRI) antidepressants. Methods Sixty patients with mixed anxiety and depressive disorder (according to ICD-10 diagnostic criteria F41.2) were included in an 8-week open label study. Thirty participants received either SSRI antidepressants with PLANTARUM at a dose of 1.0 × 109 CFU once per day and thirty patients received SSRI antidepressants only. The severity of insomnia was assessed using Insomnia Severity Index (ISI). The severity of depressive symptoms was rated using Hamilton Depressive Rating Scale (HDRS). The severity of anxiety symptoms was assessed using Hamilton Anxiety Rating Scale (HAM-A) and General Anxiety Disorder Scale (GAD-7). Results After 8 weeks intervention, a significant reduction of ISI total score (from 22,1±2,8 to 14,1±2,1) was detected in patients with anxiety and depression who prescribed SSRI antidepressants and PLANTARUM (p˂0,05), compared with participants who didn’t receive probiotics (p>0,05). Also, we detected a significant improve sleep quality of insomniac patients with comorbidity of anxiety and depressive symptoms (p˂0,05) who received SSRI antidepressants and probiotic supplement Lactobacillus Plantarum CECT7485/Lactobacillus Brevis CECT7480. Conclusions The present data illustrated that probiotic supplement Lactobacillus Plantarum CECT7485 and Lactobacillus Brevis CECT7480 is a feasible for adjunctive to SSRI antidepressants intervention for insomniac patients with anxiety and depressive comorbidity Disclosure of Interest None Declared

Shubham Saini, Neha Bagri

Cerebrotendinous xanthomatosis is a rare autosomal recessive disorder that occurs due to defective bile acid biosynthesis, causing unusual cholesterol and cholestanol deposition in multiple soft tissues. It is manifested by various neurological and non-neurological symptoms. The characteristic imaging features and clinical symptoms can help to make an early diagnosis and induce timely treatment to prevent neurological sequelae. The authors present two adults with differing clinical symptoms, whose imaging provided pivotal cues in diagnosing cerebrotendinous xanthomatosis.

M. Marvá, E. Venturino, M. C. Vera

This work presents a model combining the simplest communicable and non-communicable disease models. The latter is, by far, the leading cause of sickness and death in the World, and introduces basal heterogeneity in populations where communicable diseases evolve. The model can be interpreted as a risk-structured model, another way of accounting for population heterogeneity. Our results show that considering the non-communicable disease (in the end, heterogeneous populations) allows the communicable disease to become endemic even if the basic reproduction number is less than $1$. This feature is known as subcritical bifurcation. Furthermore, ignoring the non-communicable disease dynamics results in overestimating the reproduction number and, thus, giving wrong information about the actual number of infected individuals. We calculate sensitivity indices and derive interesting epidemic-control information.

Yujie Gao, Malay Banerjee, Ton Viet Ta

This paper introduces an innovative model for infectious diseases in predator-prey populations. We not only prove the existence of global non-negative solutions but also establish essential criteria for the system's decline and sustainability. Furthermore, we demonstrate the presence of a Borel invariant measure, adding a new dimension to our understanding of the system. To illustrate the practical implications of our findings, we present numerical results. With our model's comprehensive approach, we aim to provide valuable insights into the dynamics of infectious diseases and their impact on predator-prey populations.

Muhammad Shoaib Farooq, Kiran Amjad

Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.

Md. Hamjajul Ashmafee, Tasnim Ahmed, Sabbir Ahmed et al.

Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.

Wanting Su, Dongwei Liu, Feng Tan et al.

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.

Brynjar Sævarsson, Hjörtur Jóhannsson, Spyros Chatzivasileiadis

This paper introduces the first quantum computing framework for Stochastic Quantum Power Flow (SQPF) analysis in power systems. The proposed method leverages quantum states to encode power flow distributions, enabling the use of Quantum Monte Carlo (QMC) sampling to efficiently assess the probability of line overloads. Our approach significantly reduces the required sample size compared to traditional Monte Carlo methods, making it particularly suited for risk assessments in scenarios involving high uncertainty, such as renewable energy integration. We validate the method on two test systems, demonstrating the computational advantage of quantum algorithms in reducing sample complexity while maintaining accuracy. This work represents a foundational step toward scalable quantum power flow analysis, with potential applications in future power system operations and planning. The results show promising computational speedups, underscoring the potential of quantum computing in addressing the increasing uncertainty in modern power grids.

Sarasadat Foroughipoor, Kimia Moradi, Hamidreza Bolhasani

The most frequent kind of dementia of the nervous system, Alzheimer's disease, weakens several brain processes (such as memory) and eventually results in death. The clinical study uses magnetic resonance imaging to diagnose AD. Deep learning algorithms are capable of pattern recognition and feature extraction from the inputted raw data. As early diagnosis and stage detection are the most crucial elements in enhancing patient care and treatment outcomes, deep learning algorithms for MRI images have recently allowed for diagnosing a medical condition at the beginning stage and identifying particular symptoms of Alzheimer's disease. As a result, we aimed to analyze five specific studies focused on AD diagnosis using MRI-based deep learning algorithms between 2021 and 2023 in this study. To completely illustrate the differences between these techniques and comprehend how deep learning algorithms function, we attempted to explore selected approaches in depth.

Sourav R. Choudhury, E. Hudry, C. Maguire et al.