Hasil untuk "Neurology. Diseases of the nervous system"

Dunja Mrdjen, Anto Pavlovic, F. Hartmann et al.

S Spudich, A. Nath

Description Neurological symptoms highlight the need to understand pathophysiologic mechanisms Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is considered a respiratory pathogen, myriad neurologic complications—including confusion, stroke, and neuromuscular disorders—manifest during acute COVID-19. Furthermore, maladies such as impaired concentration, headache, sensory disturbances, depression, and even psychosis may persist for months after infection, as part of a constellation of symptoms now called Long Covid. Even young people with mild initial disease can develop acute COVID-19 and Long Covid neuropsychiatric syndromes. The pathophysiological mechanisms are not well understood, although evidence primarily implicates immune dysfunction, including nonspecific neuroinflammation and antineural autoimmune dysregulation. It is uncertain whether unforeseen neurological consequences may develop years after initial infection. With millions of individuals affected, nervous system complications pose public health challenges for rehabilitation and recovery and for disruptions in the workforce due to loss of functional capacity. There is an urgent need to understand the pathophysiology of these disorders and develop disease-modifying therapies.

Amy F Lloyd, V. Miron

E. Wehrwein, H. Orer, S. Barman

Comprised of the sympathetic nervous system, parasympathetic nervous system, and enteric nervous system, the autonomic nervous system (ANS) provides the neural control of all parts of the body except for skeletal muscles. The ANS has the major responsibility to ensure that the physiological integrity of cells, tissues, and organs throughout the entire body is maintained (homeostasis) in the face of perturbations exerted by both the external and internal environments. Many commonly prescribed drugs, over‐the‐counter drugs, toxins, and toxicants function by altering transmission within the ANS. Autonomic dysfunction is a signature of many neurological diseases or disorders. Despite the physiological relevance of the ANS, most neuroscience textbooks offer very limited coverage of this portion of the nervous system. This review article provides both historical and current information about the anatomy, physiology, and pharmacology of the sympathetic and parasympathetic divisions of the ANS. The ultimate aim is for this article to be a valuable resource for those interested in learning the basics of these two components of the ANS and to appreciate its importance in both health and disease. Other resources should be consulted for a thorough understanding of the third division of the ANS, the enteric nervous system. © 2016 American Physiological Society. Compr Physiol 6:1239‐1278, 2016.

Brienne A. McKenzie, V. Dixit, C. Power

Pyroptosis ('fiery death') is an inflammatory type of regulated cell death (RCD), which occurs downstream of inflammasome activation. Pyroptosis is mediated directly by the recently identified family of pore-forming proteins known as gasdermins, the best characterized of which is gasdermin D (GSDMD). Recent investigations implicate pyroptosis in the pathogenesis of multiple neurological diseases. In this review, we discuss molecular mechanisms that drive pyroptosis, evidence for pyroptosis within the CNS, and emerging therapeutic strategies for its inhibition in the context of neurological disease.

Amy F Lloyd, C. L. Davies, Rebecca K. Holloway et al.

Failed regeneration of CNS myelin contributes to clinical decline in neuroinflammatory and neurodegenerative diseases, for which there is an unmet therapeutic need. Here we reveal that efficient remyelination requires death of proinflammatory microglia followed by repopulation to a pro-regenerative state. We propose that impaired microglia death and/or repopulation may underpin dysregulated microglia activation in neurological diseases, and we reveal therapeutic targets to promote white matter regeneration. Lloyd et al. find that regeneration of CNS myelin requires death of proinflammatory microglia followed by repopulation to a pro-regenerative state, revealing new therapeutic targets for neurodegenerative disease.

C. Pellegrini, M. Fornai, V. D’Antongiovanni et al.

The intestinal barrier, which primarily consists of a mucus layer, an epithelial barrier, and a gut vascular barrier, has a crucial role in health and disease by facilitating nutrient absorption and preventing the entry of pathogens. The intestinal barrier is in close contact with gut microbiota on its luminal side and with enteric neurons and glial cells on its tissue side. Mounting evidence now suggests that the intestinal barrier is compromised not only in digestive disorders, but also in disorders of the central nervous system (CNS), such as Parkinson's disease, autism spectrum disorder, depression, multiple sclerosis, and Alzheimer's disease. After providing an overview of the structure and functions of the intestinal barrier, we review existing preclinical and clinical studies supporting the notion that intestinal barrier dysfunction is present in neurological, neurodevelopmental, and psychiatric disorders. On the basis of this evidence, we discuss the mechanisms that possibly link gut barrier dysfunction and CNS disorders and the potential impact that evaluating enteric barriers in brain disorders could have on clinical practice, in terms of novel diagnostic and therapeutic strategies, in the near future.

Hanie Yavarpour-Bali, Maryam Ghasemi-Kasman, Marzieh Pirzadeh

Abstract Curcumin as a hydrophobic polyphenol is extracted from the rhizome of Curcuma longa. Curcumin is widely used as a dietary spice and a topical medication for the treatment of inflammatory disorders in Asia. This compound also possesses remarkable anti-inflammatory and neuroprotective effects with the ability to pass from the blood brain barrier. Based on several pharmacological activities of curcumin, it has been introduced as an ideal candidate for different neurological disorders. Despite the pleiotropic activities of curcumin, poor solubility, rapid clearance and low stability have limited its clinical application. In recent years, nano-based drug delivery system has effectively improved the aqueous solubility and bioavailability of curcumin. In this review article, the effects of curcumin nanoparticles and their possible mechanism/s of action has been elucidated in various central nervous system (CNS)-related diseases including Parkinson’s disease, Huntington disease, Alzheimer’s disease, Multiple sclerosis, epilepsy and Amyotrophic Lateral Sclerosis. Furthermore, recent evidences about administration of nano-curcumin in the clinical trial phase have been described in the present review article.

Mohammad Reza Shegarf Nakhaei, Parastoo Amiri, Esmat Davoudi Monfared et al.

Abstract Background Psychiatric comorbidities are frequent among hospitalized patients and can adversely affect treatment outcomes, yet referral rates for psychiatric consultation remain low. The COVID-19 pandemic created unique psychosocial and healthcare challenges that may have influenced consultation patterns, particularly in Iran, where cultural stigma often limits mental health care. This study investigated the causes and frequency of psychiatric consultation requests for hospitalized patients before and after the COVID-19 pandemic in Iran. Methods This study was a cross-sectional study of patients hospitalized during the summers of 2019 and 2020 at Vasei Hospital, Sabzevar, Iran. Eligible cases included patients with at least one documented psychiatric consultation. Demographics, referral reasons, DSM-5 diagnoses, and substance use history were extracted. Data were analyzed with chi-square, t-tests, and logistic regression in SPSS 22. Results Psychiatric consultations were requested for 161 out of 3,622 admissions in 2019 (4.4%) and for 147 out of 2,694 admissions in 2020 (5.4%, p = 0.083). Suicide attempts were the primary reason for referrals, but their proportion declined during the COVID-19 pandemic, while incidents of agitation and anxiety increased. Logistic regression analysis revealed that a history of substance use (odds ratio [OR] = 1.9, 95% confidence interval [CI] = 1.2–2.8, P = 0.004) and the presence of delirium (OR = 2.4, 95% CI = 1.1–5.3, P = 0.031) were independent predictors of psychiatric consultation during the pandemic period. Conclusion Overall consultation rates remained stable, but diagnostic changes reflected the indirect mental health burden of COVID-19. Strengthening consultation-liaison psychiatry, routine screening, and culturally tailored interventions are essential for future pandemic preparedness. Clinical trial number Not applicable.

J. Berger

A pandemic due to novel coronavirus arose in mid-December 2019 in Wuhan, China, and in 3 months’ time swept the world. The disease has been referred to as COVID-19, and the causative agent has been labelled SARS-CoV-2 due to its genetic similarities to the virus (SARS-CoV-1) responsible for the severe acute respiratory syndrome (SARS) epidemic nearly 20 years earlier. The spike proteins of both viruses dictate tissue tropism using the angiotensin-converting enzyme type 2 (ACE-2) receptor to bind to cells. The ACE-2 receptor can be found in nervous system tissue and endothelial cells among the tissues of many other organs. Neurological complications have been observed with COVID-19. Myalgia and headache are relatively common, but serious neurological disease appears to be rare. No part of the neuraxis is spared. The neurological disorders occurring with COVID-19 may have many pathophysiological underpinnings. Some appear to be the consequence of direct viral invasion of the nervous system tissue, others arise as a postviral autoimmune process, and still others are the result of metabolic and systemic complications due to the associated critical illness. This review addresses the preliminary observations regarding the neurological disorders reported with COVID-19 to date and describes some of the disorders that are anticipated from prior experience with similar coronaviruses.

Lawrence T. Lam, Lawrence T. Lam, Lawrence T. Lam et al.

BackgroundThis study aims to validate the Chinese version of the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS) by employing both Classical Test Theory (CTT) and Item Response Theory (IRT) approaches.MethodsData were gathered through a population-based, cross-sectional health survey using an online self-reported questionnaire. The scale underwent Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Measurement invariance by gender was assessed using standard procedures. The Grade Response Model (GRM) of the IRT analysis was applied to the data, estimating the discrimination and difficulty parameters at different thresholds. The results were analyzed both graphically and through parameter values.ResultsFactor analyses confirmed that a single-factor model of the scale fit the data well, with an overall Eigenvalue of 4.55, explaining 65.0% of the total variance. Model fit statistics were slightly better for males than for females. Measurement invariance examinations also yielded satisfactory Goodness-of-Fit statistics (CFI = 0.940, TFI = 0.910, RMSEA < 0.001) with minimal changes in item loadings and indicator threshold patterns across groups. The IRT results demonstrated high discrimination parameters, ranging from 2.17 to 3.67, and nearly evenly distributed difficulty parameters, ranging from -2.23 to 1.77. Graphical examinations indicated good performance of the scale across the latent trait continuum.ConclusionsThe results indicated that, as a single-factor scale, the instrument exhibits good quality at both the scale and item levels. It has high discriminative power and an adequate response set for assessing a full range of the latent trait, namely mental well-being.

Chuqin Geng, Anqi Xing, Li Zhang et al.

Rule-based explanation methods offer rigorous and globally interpretable insights into neural network behavior. However, existing approaches are mostly limited to small fully connected networks and depend on costly layerwise rule extraction and substitution processes. These limitations hinder their generalization to more complex architectures such as Transformers. Moreover, existing methods produce shallow, decision-tree-like rules that fail to capture rich, high-level abstractions in complex domains like computer vision and natural language processing. To address these challenges, we propose NEUROLOGIC, a novel framework that extracts interpretable logical rules directly from deep neural networks. Unlike previous methods, NEUROLOGIC can construct logic rules over hidden predicates derived from neural representations at any chosen layer, in contrast to costly layerwise extraction and rewriting. This flexibility enables broader architectural compatibility and improved scalability. Furthermore, NEUROLOGIC supports richer logical constructs and can incorporate human prior knowledge to ground hidden predicates back to the input space, enhancing interpretability. We validate NEUROLOGIC on Transformer-based sentiment analysis, demonstrating its ability to extract meaningful, interpretable logic rules and provide deeper insights-tasks where existing methods struggle to scale.

Jiahe Li, Xin Chen, Fanqi Shen et al.

Neurological disorders pose major global health challenges, driving advances in brain signal analysis. Scalp electroencephalography (EEG) and intracranial EEG (iEEG) are widely used for diagnosis and monitoring. However, dataset heterogeneity and task variations hinder the development of robust deep learning solutions. This review systematically examines recent advances in deep learning approaches for EEG/iEEG-based neurological diagnostics, focusing on applications across 7 neurological conditions using 46 datasets. For each condition, we review representative methods and their quantitative results, integrating performance comparisons with analyses of data usage, model design, and task-specific adaptations, while highlighting the role of pre-trained multi-task models in achieving scalable, generalizable solutions. Finally, we propose a standardized benchmark to evaluate models across diverse datasets and improve reproducibility, emphasizing how recent innovations are transforming neurological diagnostics toward intelligent, adaptable healthcare systems.

R. Ratan

Over the past five decades, thanatology has come to include the study of how individual cells in our bodies die appropriately and inappropriately in response to physiological and pathological stimuli. Morphological and biochemical criteria have been painstakingly established to create clarity around definitions of distinct types of cell death and mechanisms for their activation. Among these, ferroptosis has emerged as a unique, oxidative stress-induced cell death pathway with implications for diseases as diverse as traumatic brain injury, hemorrhagic stroke, Alzheimer's disease, cancer, renal ischemia, and heat stress in plants. In this review, I highlight some of the formative studies that fostered its recognition in the nervous system and describe how chemical biological tools have been essential in defining events necessary for its execution. Finally, I discuss emerging opportunities for antiferroptotic agents as therapeutic agents in neurological diseases.

N. Jha, S. Ojha, S. Jha et al.

The coronavirus disease 2019 (COVID-19) pandemic is an issue of global significance that has taken the lives of many across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus responsible for its pathogenesis. The pulmonary manifestations of COVID-19 have been well described in the literature. Initially, it was thought to be limited to the respiratory system; however, we now recognize that COVID-19 also affects several other organs, including the nervous system. Two similar human coronaviruses (CoV) that cause severe acute respiratory syndrome (SARS-CoV-1) and Middle East respiratory syndrome (MERS-CoV) are also known to cause disease in the nervous system. The neurological manifestations of SARS-CoV-2 infection are growing rapidly, as evidenced by several reports. There are several mechanisms responsible for such manifestations in the nervous system. For instance, post-infectious immune-mediated processes, direct virus infection of the central nervous system (CNS), and virus-induced hyperinflammatory and hypercoagulable states are commonly involved. Guillain-Barré syndrome (GBS) and its variants, dysfunction of taste and smell, and muscle injury are numerous examples of COVID-19 PNS (peripheral nervous system) disease. Likewise, hemorrhagic and ischemic stroke, encephalitis, meningitis, encephalopathy acute disseminated encephalomyelitis, endothelialitis, and venous sinus thrombosis are some instances of COVID-19 CNS disease. Due to multifactorial and complicated pathogenic mechanisms, COVID-19 poses a large-scale threat to the whole nervous system. A complete understanding of SARS-CoV-2 neurological impairments is still lacking, but our knowledge base is rapidly expanding. Therefore, we anticipate that this comprehensive review will provide valuable insights and facilitate the work of neuroscientists in unfolding different neurological dimensions of COVID-19 and other CoV associated abnormalities.

D.E. Ogolo, E.C. Ajare, O. Okwuoma et al.

Background and objectives: While various pathologies affecting the foramen magnum region can have severe consequences, little research has been conducted on the unique morphological patterns in the West African subregion. The study aimed to assess these patterns and their implications for surgeries, comparing them with global standards. Methods: A descriptive study was conducted on 315 patients over a two-year period, excluding those with specific abnormalities. Measurements obtained from cranial 1.5T MRI scans included anteroposterior and transverse diameters of the foramen magnum. From these, the foramen magnum area and index were calculated. The data was analyzed by inferential, comparative and descriptive statistics, and a p value < 0.05 was regarded as statistically significant. Results: On average, the transverse and anteroposterior diameters were 28.51 mm and 33.02 mm for males and 28.39 mm and 33.47 mm for females, with a slightly smaller foramen magnum area in males (7.42 cm²) compared to females (7.47 cm²). Despite these differences, the variations were not statistically significant. However, the foramen magnum indices indicated medium size configuration for females and large size configuration for males, aligning with global trends. Conclusion: The study concluded that West Africans exhibited lower foramen magnum area and indices compared to other regions, with minor differences between sexes. Females tended to have a medium size configuration, while males tended to have a larger size configuration. These findings provide valuable insights for clinicians, highlighting the importance of considering ethno-regional variations in surgical approaches and interventions related to the craniocervical junction.

Chongyun Wu, Tao Wu, Luodan Yang

Bimarsha Khanal, Paras Poudel, Anish Chapagai et al.

Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.

Wenhui Cui, Haleh Akrami, Anand A. Joshi et al.

Despite the impressive advances achieved using deep learning for functional brain activity analysis, the heterogeneity of functional patterns and the scarcity of imaging data still pose challenges in tasks such as identifying neurological disorders. For functional Magnetic Resonance Imaging (fMRI), while data may be abundantly available from healthy controls, clinical data is often scarce, especially for rare diseases, limiting the ability of models to identify clinically-relevant features. We overcome this limitation by introducing a novel representation learning strategy integrating meta-learning with self-supervised learning to improve the generalization from normal to clinical features. This approach enables generalization to challenging clinical tasks featuring scarce training data. We achieve this by leveraging self-supervised learning on the control dataset to focus on inherent features that are not limited to a particular supervised task and incorporating meta-learning to improve the generalization across domains. To explore the generalizability of the learned representations to unseen clinical applications, we apply the model to four distinct clinical datasets featuring scarce and heterogeneous data for neurological disorder classification. Results demonstrate the superiority of our representation learning strategy on diverse clinically-relevant tasks. Code is publicly available at https://github.com/wenhui0206/MeTSK/tree/main

Cécile Trottet, Manuel Schürch, Ahmed Allam et al.

We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the underlying generative process that explain the observed patient disease trajectories in an interpretable and comprehensive way. To enhance the interpretability of these latent temporal processes, we develop a semi-supervised approach for disentangling the latent space using established medical knowledge. By combining the generative approach with medical definitions of different characteristics of SSc, we facilitate the discovery of new aspects of the disease. We show that the learned temporal latent processes can be utilized for further data analysis and clinical hypothesis testing, including finding similar patients and clustering SSc patient trajectories into novel sub-types. Moreover, our method enables personalized online monitoring and prediction of multivariate time series with uncertainty quantification.