Hasil untuk "Neurology. Diseases of the nervous system"

S. Liebner, R. Dijkhuizen, Y. Reiss et al.

The adult quiescent blood–brain barrier (BBB), a structure organised by endothelial cells through interactions with pericytes, astrocytes, neurons and microglia in the neurovascular unit, is highly regulated but fragile at the same time. In the past decade, there has been considerable progress in understanding not only the molecular pathways involved in BBB development, but also BBB breakdown in neurological diseases. Specifically, the Wnt/β-catenin, retinoic acid and sonic hedgehog pathways moved into the focus of BBB research. Moreover, angiopoietin/Tie2 signalling that is linked to angiogenic processes has gained attention in the BBB field. Blood vessels play an essential role in initiation and progression of many diseases, including inflammation outside the central nervous system (CNS). Therefore, the potential influence of CNS blood vessels in neurological diseases associated with BBB alterations or neuroinflammation has become a major focus of current research to understand their contribution to pathogenesis. Moreover, the BBB remains a major obstacle to pharmaceutical intervention in the CNS. The complications may either be expressed by inadequate therapeutic delivery like in brain tumours, or by poor delivery of the drug across the BBB and ineffective bioavailability. In this review, we initially describe the cellular and molecular components that contribute to the steady state of the healthy BBB. We then discuss BBB alterations in ischaemic stroke, primary and metastatic brain tumour, chronic inflammation and Alzheimer’s disease. Throughout the review, we highlight common mechanisms of BBB abnormalities among these diseases, in particular the contribution of neuroinflammation to BBB dysfunction and disease progression, and emphasise unique aspects of BBB alteration in certain diseases such as brain tumours. Moreover, this review highlights novel strategies to monitor BBB function by non-invasive imaging techniques focussing on ischaemic stroke, as well as novel ways to modulate BBB permeability and function to promote treatment of brain tumours, inflammation and Alzheimer’s disease. In conclusion, a deep understanding of signals that maintain the healthy BBB and promote fluctuations in BBB permeability in disease states will be key to elucidate disease mechanisms and to identify potential targets for diagnostics and therapeutic modulation of the BBB.

S. G. Sorboni, H. S. Moghaddam, Reza Jafarzadeh-Esfehani et al.

The human body is full of an extensive number of commensal microbes, consisting of bacteria, viruses, and fungi, collectively termed the human microbiome. The initial acquisition of microbiota occurs from both the external and maternal environments, and the vast majority of them colonize the gastrointestinal tract (GIT). SUMMARY The human body is full of an extensive number of commensal microbes, consisting of bacteria, viruses, and fungi, collectively termed the human microbiome. The initial acquisition of microbiota occurs from both the external and maternal environments, and the vast majority of them colonize the gastrointestinal tract (GIT). These microbial communities play a central role in the maturation and development of the immune system, the central nervous system, and the GIT system and are also responsible for essential metabolic pathways. Various factors, including host genetic predisposition, environmental factors, lifestyle, diet, antibiotic or nonantibiotic drug use, etc., affect the composition of the gut microbiota. Recent publications have highlighted that an imbalance in the gut microflora, known as dysbiosis, is associated with the onset and progression of neurological disorders. Moreover, characterization of the microbiome-host cross talk pathways provides insight into novel therapeutic strategies. Novel preclinical and clinical research on interventions related to the gut microbiome for treating neurological conditions, including autism spectrum disorders, Parkinson’s disease, schizophrenia, multiple sclerosis, Alzheimer’s disease, epilepsy, and stroke, hold significant promise. This review aims to present a comprehensive overview of the potential involvement of the human gut microbiome in the pathogenesis of neurological disorders, with a particular emphasis on the potential of microbe-based therapies and/or diagnostic microbial biomarkers. This review also discusses the potential health benefits of the administration of probiotics, prebiotics, postbiotics, and synbiotics and fecal microbiota transplantation in neurological disorders.

Yikai Shou, Yilu Huang, Xiaozheng Zhu et al.

PM2.5 particles in air pollution have been widely considered associated with respiratory and cardiovascular diseases. Recent studies have shown that PM2.5 can also cause central nervous system (CNS) diseases, including a variety of neurodegenerative diseases, such as Alzheimer's disease (AD). Activation of microglia in the central nervous system can lead to inflammatory and neurological damage. PM2.5 will reduce the methylation level of DNA and affect epigenetics. PM2.5 enters the human body through a variety of pathways to have pathological effects on CNS. For example, PM2.5 can destroy the integrity of the blood-brain barrier (BBB), so peripheral systemic inflammation easily crosses BBB and reaches CNS. The olfactory nerve is another way for PM2.5 particles to enter the brain. Surprisingly, PM2.5 can also enter the gastrointestinal tract, causing imbalances in the intestinal microecology to affect central nervous system diseases. The current work collected and discuss the mechanisms of PM2.5-induced CNS damage and PM2.5-induced neurodegenerative diseases.

Nicole D. Schartz, A. Tenner

The complement cascade is a critical effector mechanism of the innate immune system that contributes to the rapid clearance of pathogens and dead or dying cells, as well as contributing to the extent and limit of the inflammatory immune response. In addition, some of the early components of this cascade have been clearly shown to play a beneficial role in synapse elimination during the development of the nervous system, although excessive complement-mediated synaptic pruning in the adult or injured brain may be detrimental in multiple neurogenerative disorders. While many of these later studies have been in mouse models, observations consistent with this notion have been reported in human postmortem examination of brain tissue. Increasing awareness of distinct roles of C1q, the initial recognition component of the classical complement pathway, that are independent of the rest of the complement cascade, as well as the relationship with other signaling pathways of inflammation (in the periphery as well as the central nervous system), highlights the need for a thorough understanding of these molecular entities and pathways to facilitate successful therapeutic design, including target identification, disease stage for treatment, and delivery in specific neurologic disorders. Here, we review the evidence for both beneficial and detrimental effects of complement components and activation products in multiple neurodegenerative disorders. Evidence for requisite co-factors for the diverse consequences are reviewed, as well as the recent studies that support the possibility of successful pharmacological approaches to suppress excessive and detrimental complement-mediated chronic inflammation, while preserving beneficial effects of complement components, to slow the progression of neurodegenerative disease.

B. M. Shahria Alam, Md. Nasim Ahmed

Plant disease diagnosis is essential to farmers' management choices because plant diseases frequently lower crop yield and product quality. For harvests to flourish and agricultural productivity to boost, grape leaf disease detection is important. The plant disease dataset contains grape leaf diseases total of 9,032 images of four classes, among them three classes are leaf diseases, and the other one is healthy leaves. After rigorous pre-processing dataset was split (70% training, 20% validation, 10% testing), and two pre-trained models were deployed: InceptionV3 and Xception. Xception shows a promising result of 96.23% accuracy, which is remarkable than InceptionV3. Adversarial Training is used for robustness, along with more transparency. Grad-CAM is integrated to confirm the leaf disease. Finally deployed a web application using Streamlit with a heatmap visualization and prediction with confidence level for robust grape leaf disease classification.

Zhi Qin Tan, Xiatian Zhu, Owen Addison et al.

Diagnosing dental diseases from radiographs is time-consuming and challenging due to the subtle nature of diagnostic evidence. Existing methods, which rely on object detection models designed for natural images with more distinct target patterns, struggle to detect dental diseases that present with far less visual support. To address this challenge, we propose {\bf DentalX}, a novel context-aware dental disease detection approach that leverages oral structure information to mitigate the visual ambiguity inherent in radiographs. Specifically, we introduce a structural context extraction module that learns an auxiliary task: semantic segmentation of dental anatomy. The module extracts meaningful structural context and integrates it into the primary disease detection task to enhance the detection of subtle dental diseases. Extensive experiments on a dedicated benchmark demonstrate that DentalX significantly outperforms prior methods in both tasks. This mutual benefit arises naturally during model optimization, as the correlation between the two tasks is effectively captured. Our code is available at https://github.com/zhiqin1998/DentYOLOX.

Mutong Liu, Yang Liu, Jiming Liu

Reinforcement learning (RL), owing to its adaptability to various dynamic systems in many real-world scenarios and the capability of maximizing long-term outcomes under different constraints, has been used in infectious disease control to optimize the intervention strategies for controlling infectious disease spread and responding to outbreaks in recent years. The potential of RL for assisting public health sectors in preventing and controlling infectious diseases is gradually emerging and being explored by rapidly increasing publications relevant to COVID-19 and other infectious diseases. However, few surveys exclusively discuss this topic, that is, the development and application of RL approaches for optimizing strategies of non-pharmaceutical and pharmaceutical interventions of public health. Therefore, this paper aims to provide a concise review and discussion of the latest literature on how RL approaches have been used to assist in controlling the spread and outbreaks of infectious diseases, covering several critical topics addressing public health demands: resource allocation, balancing between lives and livelihoods, mixed policy of multiple interventions, and inter-regional coordinated control. Finally, we conclude the paper with a discussion of several potential directions for future research.

G. Zamponi

M. Herath, S. Hosie, J. Bornstein et al.

Mucus is integral to gut health and its properties may be affected in neurological disease. Mucus comprises a hydrated network of polymers including glycosylated mucin proteins. We propose that factors that influence the nervous system may also affect the volume, viscosity, porosity of mucus composition and subsequently, gastrointestinal (GI) microbial populations. The gut has its own intrinsic neuronal network, the enteric nervous system, which extends the length of the GI tract and innervates the mucosal epithelium. The ENS regulates gut function including mucus secretion and renewal. Both dysbiosis and gut dysfunction are commonly reported in several neurological disorders such as Parkinson's and Alzheimer's disease as well in patients with neurodevelopmental disorders including autism. Since some microbes use mucus as a prominent energy source, changes in mucus properties could alter, and even exacerbate, dysbiosis-related gut symptoms in neurological disorders. This review summarizes existing knowledge of the structure and function of the mucus of the GI tract and highlights areas to be addressed in future research to better understand how intestinal homeostasis is impacted in neurological disorders.

Lu Yu, Xueying Wang, Tri H. Doan et al.

Abstract Background Alzheimer's disease (AD) is characterized by amyloid-beta (Aβ) accumulation, leading to the formation of neurotoxic soluble oligomers (AβOs) that impair calcium homeostasis in neurons and astrocytes. Aducanumab, a fully human monoclonal antibody targeting aggregated Aβ, has been approved for AD treatment due to its ability to reduce amyloid plaque burden. However, its specificity toward different AβO species and its functional impact on calcium homeostasis remain unclear. Methods We investigated aducanumab's ability to recognize and immunodeplete low-molecular-weight (LMW) and high-molecular-weight (HMW) AβOs using three Aβ preparations: (1) transgenic conditioned media (TgCM) from cultured Tg2576 neurons, (2) synthetic Aβ42-derived diffusible ligands (ADDLs), and (3) TBS-soluble fractions from aged Tg2576 mouse brain. Size exclusion chromatography and ELISA were used to characterize AβO species. Multiphoton calcium imaging of neuron-astrocyte co-cultures was performed to assess the impact of aducanumab on AβO-induced calcium overload. Results Aducanumab preferentially bound and immunodepleted HMW AβOs in ADDLs and the TBS-soluble fraction of Tg2576 mouse brain extracts but did not recognize LMW AβOs in TgCM. In calcium imaging experiments, all three AβO preparations induced calcium overload in neuron-astrocyte co-cultures. Immunodepletion with aducanumab prevented calcium overload in cultures exposed to ADDLs and Tg2576 brain extracts but not in those treated with immunodepleted TgCM, indicating that aducanumab selectively neutralizes HMW AβOs. Conclusions Our findings demonstrate that aducanumab specifically targets HMW AβOs, mitigating their neurotoxic effects by restoring intracellular calcium homeostasis. These results provide mechanistic insight into aducanumab’s therapeutic action and support its potential role in modifying AD pathology by selectively neutralizing Aβ species.

Osamudiamen S. Ogbeide, Madeleine K. Nowak, Madeleine K. Nowak et al.

IntroductionThis study examines the capability of detecting neurological changes caused by subconcussive head impacts by analyzing the blink reflex of an individual when they encounter puffs of air as a stimulus.MethodsFollowing attrition and technical issues, 26 participants (15 females, 11 males: age ± SD; 21.3 ± 2.11 years) with at least 5 years of soccer heading experience were included in the final analysis. Participants performed 10 soccer headers with soccer balls projected at a speed of 30 mph. Parameters related to blink reflex, including blink latency, differential latency, number of oscillations, delta 30, and excursions, were assessed by the EyeStat device at pre-heading baseline, and 2-h and 24-h post-heading.ResultsSignificant declines in blink reflex parameters were observed at specific post-heading timepoints compared to baseline. At 24-h post-heading, significant reductions were detected in the overall blink latency (p = 0.0255), the blink latencies of the right eye (p = 0.0411), ipsilateral latency (p = 0.0314) and contralateral latency (p = 0.0434). At 2-h post-heading, significant declines were observed in the overall delta 30 value (p = 0.0053) and delta 30 of the right eye (p = 0.0260). Both delta 30 values returned to baseline by the 24-h post-heading timepoint. No significant changes in the differential latency, number of oscillations, and excursion of the eye were found.DiscussionThese findings suggest changes in the latency and delta 30 of a blink reflex is a viable measure of detection for neurological changes when monitoring subconcussive head impacts.

Romain Reboux, Silvia Napuri

Abstract Dravet syndrome (DS) is a rare and severe form of epilepsy, characterised by recurrent seizures that begin during the first year of life, leading to motor, cognitive and behavioural impairments. This article provides the perspectives of a parent of a child with DS (‘Ethan’) and the treating neuropaediatrician. Ethan’s seizures began when he was 9 months old, and were a mixture of focal seizures and status epilepticus. Numerous treatments were tried, including standard anti-seizure medications (such as levetiracetam, clobazam and fenfluramine), other medications (cannabidiol) and nonpharmacological approaches (ketogenic diet), with little success. When Ethan was 3 years old, a prolonged episode of status epilepticus precipitated by coronavirus disease 2019 (COVID-19) led to brain damage. Rehabilitation allowed Ethan to regain some of his previous functioning and, at the age of 38 months, combination therapy with clobazam, sodium valproate and stiripentol was begun and has successfully controlled Ethan’s seizures. Ethan’s father describes the stress that the diagnosis of DS, interactions with the healthcare system, and the search for effective treatment imposed on the family. Since Ethan’s seizures have been better controlled, the family has been able to lead a more normal life, and is now focused on supporting Ethan and looking to the future. Ethan’s neuropaediatrician outlines the approach she takes to the diagnosis and management of DS, including the importance of the clinician–parent relationship in imparting the diagnosis and making initial and ongoing treatment decisions. The preferred first-line treatment is sodium valproate, which is followed by sodium valproate–clobazam–stiripentol combination therapy, topiramate or a ketogenic diet as second-line options. In children > 2 years, cannabidiol and fenfluramine can also be considered. The aim of maintenance treatment (which will invariably be polytherapy) is to reduce the number of seizures, particularly status epilepticus, given the significant impact of this seizure type on patients and caregivers.

Boyang Su, Zhengqing He, Luyao Shi et al.

Abstract Background Myasthenia gravis (MG) is an autoimmune disorder of the neuromuscular junction. Increasing evidence has suggested inflammation is involved in the pathogenesis of MG, but whether it is the cause or a downstream effect remains unclear. In this study, a two-sample Mendelian randomization (TSMR) analysis was performed to explore the causal relationship between 91 circulating inflammatory cytokines and MG. Method In this study, the data of 91 circulating inflammatory cytokines from 4824 Europeans and the largest GWAS database of MG (1873 patients and 36370 controls) were used to screen instrumental variables (IVs). Inverse variance weighting (IVW), Bayesian weighted MR (BWMR), MR-Egger regression, weighted median (WM), simple mode and weighted mode were used to evaluate the association between MG and inflammatory cytokines. The MR-Egger intercept test and Cochran’s Q test were used to test the pleiotropy and heterogeneity of IVs. Result Our results showed that adenosine deaminase (ADA) and CD40 Ligand‌ (CD40L) are positively associated with the risk of MG (OR = 1.16, 95%CI: 1.00-1.33, P = 0.041; OR = 1.20, 95%CI: 1.02–1.40, P = 0.025), while interleukin-1-alpha (IL-1α), glial-cell-line-derived neurotrophic factor (GDNF), Osteoprotegerin (OPG) and tumor necrosis factor-beta (TNF-β) are negatively associated with the risk of MG (OR = 0.80, 95% CI: 0.64 ~ 0.99, P = 0.042; OR = 0.74, 95%CI:0.58 ~ 0.0.96, P = 0.022; OR = 0.76, 95% CI: 0.61 ~ 0.94, P = 0.013; OR = 0.76, 95% CI: 0.61 ~ 0.94, P = 0.012; OR = 0.80, 95% CI: 0.68 ~ 0.93, P = 0.006). In addition, genetically predicted MG affected the expression of seven cytokines. Sensitivity analysis showed no horizontal pleiotropy and significant heterogeneity of all results. Conclusions Our results provided promising clues for the treatment of MG. We evaluated the association between inflammatory cytokines and the disease by genetic informatics approach, which may help to better understand the underlying mechanisms of MG.

Ha Young Kim, Jun Li, Ana Beatriz Solana et al.

Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists frequently consult case reports and literature when confronted with unfamiliar findings. Following this line of reasoning, we introduce RADAR, Retrieval Augmented Diagnostic Reasoning Agents, an agentic system for rare disease detection in brain MRI. Our approach uses AI agents with access to external medical knowledge by embedding both case reports and literature using sentence transformers and indexing them with FAISS to enable efficient similarity search. The agent retrieves clinically relevant evidence to guide diagnostic decision making on unseen diseases, without the need of additional training. Designed as a model-agnostic reasoning module, RADAR can be seamlessly integrated with diverse large language models, consistently improving their rare pathology recognition and interpretability. On the NOVA dataset comprising 280 distinct rare diseases, RADAR achieves up to a 10.2% performance gain, with the strongest improvements observed for open source models such as DeepSeek. Beyond accuracy, the retrieved examples provide interpretable, literature grounded explanations, highlighting retrieval-augmented reasoning as a powerful paradigm for low-prevalence conditions in medical imaging.

Alice Tang, Maria Wei, Anna Haemel et al.

Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learning methods can advance the field through deep phenotyping, disease heterogeneity characterization, drug development, personalized medicine, and clinical care. We discuss the promises and challenges of these technologies and present a vision for their integration into clinical practice.

Anthony Hevia, Sanjana Chintalapati, Veronica Ka Wai Lai et al.

We present ROBOTO2, an open-source, web-based platform for large language model (LLM)-assisted risk of bias (ROB) assessment of clinical trials. ROBOTO2 streamlines the traditionally labor-intensive ROB v2 (ROB2) annotation process via an interactive interface that combines PDF parsing, retrieval-augmented LLM prompting, and human-in-the-loop review. Users can upload clinical trial reports, receive preliminary answers and supporting evidence for ROB2 signaling questions, and provide real-time feedback or corrections to system suggestions. ROBOTO2 is publicly available at https://roboto2.vercel.app/, with code and data released to foster reproducibility and adoption. We construct and release a dataset of 521 pediatric clinical trial reports (8954 signaling questions with 1202 evidence passages), annotated using both manually and LLM-assisted methods, serving as a benchmark and enabling future research. Using this dataset, we benchmark ROB2 performance for 4 LLMs and provide an analysis into current model capabilities and ongoing challenges in automating this critical aspect of systematic review.

Irina Kareva

Alzheimer's disease is not the outcome of a single cause but the convergence of many. This review reframes dementia as a systemic failure, where amyloid plaques and tau tangles are not root causes but late-stage byproducts of the underlying metabolic collapse. We begin by tracing the historical merger of early- and late-onset Alzheimer's into a single disease category, a conceptual error that may have misdirected decades of research. We then synthesize evidence pointing to metabolic dysfunction - especially mitochondrial damage - as a more likely initiating event. Through this lens, we examine diverse contributing factors including type 2 diabetes, hyperglycemia-induced oxidative stress, infections and neuroinflammation. Finally, we assess current treatment limitations and argue that prevention, grounded in early metabolic and vascular interventions, holds the most promise for altering the course of this complex disease.

Jin-Xing Liu, Wen-Yu Xi, Ling-Yun Dai et al.

The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs.

Boa Jang, Youngbin Ahn, Eun Kyung Choe et al.

Artificial intelligence applied to retinal images offers significant potential for recognizing signs and symptoms of retinal conditions and expediting the diagnosis of eye diseases and systemic disorders. However, developing generalized artificial intelligence models for medical data often requires a large number of labeled images representing various disease signs, and most models are typically task-specific, focusing on major retinal diseases. In this study, we developed a Fundus-Specific Pretrained Model (Image+Fundus), a supervised artificial intelligence model trained to detect abnormalities in fundus images. A total of 57,803 images were used to develop this pretrained model, which achieved superior performance across various downstream tasks, indicating that our proposed model outperforms other general methods. Our Image+Fundus model offers a generalized approach to improve model performance while reducing the number of labeled datasets required. Additionally, it provides more disease-specific insights into fundus images, with visualizations generated by our model. These disease-specific foundation models are invaluable in enhancing the performance and efficiency of deep learning models in the field of fundus imaging.

Abdelmalik Ouamane, Ammar Chouchane, Yassine Himeur et al.

Machine learning has revolutionized the field of agricultural science, particularly in the early detection and management of plant diseases, which are crucial for maintaining crop health and productivity. Leveraging advanced algorithms and imaging technologies, researchers are now able to identify and classify plant diseases with unprecedented accuracy and speed. Effective management of tomato diseases is crucial for enhancing agricultural productivity. The development and application of tomato disease classification methods are central to this objective. This paper introduces a cutting-edge technique for the detection and classification of tomato leaf diseases, utilizing insights from the latest pre-trained Convolutional Neural Network (CNN) models. We propose a sophisticated approach within the domain of tensor subspace learning, known as Higher-Order Whitened Singular Value Decomposition (HOWSVD), designed to boost the discriminatory power of the system. Our approach to Tensor Subspace Learning is methodically executed in two phases, beginning with HOWSVD and culminating in Multilinear Discriminant Analysis (MDA). The efficacy of this innovative method was rigorously tested through comprehensive experiments on two distinct datasets, namely PlantVillage and the Taiwan dataset. The findings reveal that HOWSVD-MDA outperforms existing methods, underscoring its capability to markedly enhance the precision and dependability of diagnosing tomato leaf diseases. For instance, up to 98.36\% and 89.39\% accuracy scores have been achieved under PlantVillage and the Taiwan datasets, respectively.