Hasil untuk "Neurology. Diseases of the nervous system"

Xiangliang Chen, S. Laurent, O. Onur et al.

To study the frequency of neurological symptoms and complications in COVID-19 patients in a systematic review of the literature. Relevant studies were identified through electronic explorations of PubMed, medRxiv, and bioRxiv. Besides, three Chinese databases were searched. A snowballing method searching the bibliographies of the retrieved references was applied to identify potentially relevant articles. Articles published within 1 year prior to April 20th, 2020, were screened with no language restriction imposed. Databases were searched for terms related to SARS-CoV-2/COVID-19 and neurological manifestations, using a pre-established protocol registered on the International Prospective Register of Systematic Reviews database (ID: CRD42020187994). A total of 2441 articles were screened for relevant content, of which 92 full-text publications were included in the analyses of neurological manifestations of COVID-19. Headache, dizziness, taste and smell dysfunctions, and impaired consciousness were the most frequently described neurological symptoms, the latter more often among patients with a severe or critical disease course. To date, only smaller cohort studies or single cases have reported cerebrovascular events, seizures, meningoencephalitis, and immune-mediated neurological diseases, not suitable for quantitative analysis. The most frequent neurological symptoms reported in association with COVID-19 are non-specific for the infection with SARS-CoV-2. Although SARS-CoV-2 may have the potential to gain direct access to the nervous system, so far, SARS-CoV-2 was detected in the cerebrospinal fluid in two cases only. Standardized international registries are needed to clarify the clinical relevance of the neuropathogenicity of SARS-CoV-2 and to elucidate a possible impact of SARS-CoV-2 infection on common neurological disease, such as Alzheimer’s, Parkinson’s disease or multiple sclerosis.

M. Heyes, Kuniaki Saito, J. Crowley et al.

Tom Danny S. Pedersen

We present CERES (Calibrated Early-warning and Risk Estimation System), an automated probabilistic forecasting system for acute food insecurity. CERES generates 90-day ahead probability estimates of IPC Phase 3+ (Crisis), Phase 4+ (Emergency), and Phase 5 (Famine) conditions for 43 high-risk countries globally, updated weekly. The system fuses six data streams, precipitation anomalies (CHIRPS), vegetation indices (MODIS NDVI), conflict events (ACLED), IPC classifications, food consumption scores (WFP), and cereal price indices (FAO/WFP) - through a logistic scoring model with author-specified initial coefficients and parametric input-perturbation intervals (n=2,000 draws). In historical back-validation against four IPC Phase 4-5 events selected for data completeness, CERES assigned TIER-1 classification in all four cases; these are in-sample sanity checks only, not prospective performance claims. All prospective predictions are timestamped, cryptographically identified, and archived for public verification against IPC outcome data at the T+90 horizon. To the author's knowledge, CERES is the first famine early warning system that is simultaneously: (1) probabilistic, (2) open-access, (3) continuously running, (4) machine-readable at prediction level, and (5) committed to public prospective verification of every prediction made.

Hye Won Cho, Hee Won Byeon, Seong Ok Park et al.

Abstract Neurotropic viruses, such as Japanese encephalitis virus (JEV), trigger central nervous system (CNS) inflammation primarily through disruption of the blood–brain barrier (BBB) and infiltration of peripheral immune cells. Although the gut microbiota is known to regulate diverse immunopathological processes, its contribution to CNS neuroinflammation and systemic immune responses during neurotropic viral infection remains poorly understood. Here, we show that depletion of gut microbiota by antibiotic cocktail treatment markedly increases susceptibility to CNS neuroinflammation following JEV infection. Loss of microbiota promoted viral dissemination into extraneural tissues, aggravated systemic inflammation and organ damage, and impaired tonic type I interferon (IFN-I) responses and hematopoietic differentiation during disease progression. Remarkably, fecal microbiota transplantation (FMT) restored resistance to CNS neuroinflammation, highlighting the protective role of the microbiota. Moreover, ampicillin-mediated depletion of specific gram-positive bacteria—including Bifidobacterium, Faecalibaculum, Ligilactobacillus, and Turicibacter—was associated with enhanced viral spread, systemic inflammation, and organ injury, accompanied by distinct shifts in fecal metabolites. Collectively, these findings demonstrate that gut microbiota–driven tonic IFN-I responses limit viral dissemination in extraneural tissues, thereby attenuating systemic inflammation and protecting against CNS neuroinflammation, particularly viral encephalitis.

Miranda Olff, Irma Hein, Ananda B. Amstadter et al.

To mark 15 years of the European Journal of Psychotraumatology, editors reviewed the past 15-year years of research on trauma exposure and its consequences, as well as developments in (early) psychological, pharmacological and complementary interventions. In all sections of this paper, we provide perspectives on sex/gender aspects, life course trends, and cross-cultural/global and systemic societal contexts. Globally, the majority of people experience stressful events that may be characterized as traumatic. However, definitions of what is traumatic are not necessarily straightforward or universal. Traumatic events may have a wide range of transdiagnostic mental and physical health consequences, not limited to posttraumatic stress disorder (PTSD). Research on genetic, molecular, and neurobiological influences show promise for further understanding underlying risk and resilience for trauma-related consequences. Symptom presentation, prevalence, and course, in response to traumatic experiences, differ depending on individuals’ age and developmental phase, sex/gender, sociocultural and environmental contexts, and systemic socio-political forces. Early interventions have the potential to prevent acute posttraumatic stress reactions from escalating to a PTSD diagnosis whether delivered in the golden hours or weeks after trauma. However, research on prevention is still scarce compared to treatment research where several evidence-based psychological, pharmacological and complementary/ integrative interventions exist, and novel forms of delivery have become available. Here, we focus on how best to address the range of negative health outcomes following trauma, how to serve individuals across the age spectrum, including the very young and old, and include considerations of sex/gender, ethnicity, and culture in diverse contexts, beyond Western, Educated, Industrialized, Rich, and Democratic (WEIRD) countries. We conclude with providing directions for future research aimed at improving the well-being of all people impacted by trauma around the world. The 15 years EJPT webinar provides a 90-minute summary of this paper and can be downloaded here [http://bit.ly/4jdtx6k].

M. Rybarova, J. Štulík

She Xutong

Alzheimer's disease (AD) persists as a paramount challenge in neurological research, characterized by the pathological hallmarks of amyloid-$β$ (A$β$) plaques and neurofibrillary tangles composed of hyperphosphorylated tau. This review synthesizes the evolving understanding of AD pathogenesis, moving beyond the linear amyloid cascade hypothesis to conceptualize the disease as a cross-talk of intricately interacting pathologies, encompassing A$β$, tau, and neuroinflammation as the foundation of phase-adapted pathological network model. This evolving pathophysiological understanding parallels a transformation in diagnostic paradigms, where biomarker-based strategies such as the AT(N) framework enable early disease detection during preclinical or prodromal stages. Within this new landscape, while anti-A$β$ monoclonal antibodies (e.g., lecanemab, donanemab), represent a breakthrough as the first disease-modifying therapies, their modest efficacy underscores the limitation of single-target approaches. Therefore, I explore the compelling rationale for combination therapies that simultaneously target A$β$ pathology, aberrant tau, and neuroinflammation. Looking forward, I emphasize emerging technological platforms such as gene editing and biophysical neuromodulation in advancing precision medicine. Ultimately, the integration of early biomarker detection, multi-target therapeutic strategies, and AI-driven patient stratification charts a promising roadmap toward fundamentally altering the trajectory of AD. The future of AD management will be defined by preemptive, biomarker-guided, and personalized combination interventions. Keywords: Alzheimer's disease, amyloid-$β$, tau pathology, neuroinflammation, combination therapy, multi-target therapy, precision medicine, biomarkers

K. A. Muthukumar, Dhruva Nandi, Priya Ranjan et al.

Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover's Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT's spectral insights and EMD's capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large.

Deependra Singh, Saksham Agarwal, Subhankar Mishra

Our research is motivated by the urgent global issue of a large population affected by retinal diseases, which are evenly distributed but underserved by specialized medical expertise, particularly in non-urban areas. Our primary objective is to bridge this healthcare gap by developing a comprehensive diagnostic system capable of accurately predicting retinal diseases solely from fundus images. However, we faced significant challenges due to limited, diverse datasets and imbalanced class distributions. To overcome these issues, we have devised innovative strategies. Our research introduces novel approaches, utilizing hybrid models combining deeper Convolutional Neural Networks (CNNs), Transformer encoders, and ensemble architectures sequentially and in parallel to classify retinal fundus images into 20 disease labels. Our overarching goal is to assess these advanced models' potential in practical applications, with a strong focus on enhancing retinal disease diagnosis accuracy across a broader spectrum of conditions. Importantly, our efforts have surpassed baseline model results, with the C-Tran ensemble model emerging as the leader, achieving a remarkable model score of 0.9166, surpassing the baseline score of 0.9. Additionally, experiments with the IEViT model showcased equally promising outcomes with improved computational efficiency. We've also demonstrated the effectiveness of dynamic patch extraction and the integration of domain knowledge in computer vision tasks. In summary, our research strives to contribute significantly to retinal disease diagnosis, addressing the critical need for accessible healthcare solutions in underserved regions while aiming for comprehensive and accurate disease prediction.

Zhaoxi Zhang, Xiaomei Zhang, Yanjun Zhang et al.

Large Language Model (LLM) watermarking embeds detectable signals into generated text for copyright protection, misuse prevention, and content detection. While prior studies evaluate robustness using watermark removal attacks, these methods are often suboptimal, creating the misconception that effective removal requires large perturbations or powerful adversaries. To bridge the gap, we first formalize the system model for LLM watermark, and characterize two realistic threat models constrained on limited access to the watermark detector. We then analyze how different types of perturbation vary in their attack range, i.e., the number of tokens they can affect with a single edit. We observe that character-level perturbations (e.g., typos, swaps, deletions, homoglyphs) can influence multiple tokens simultaneously by disrupting the tokenization process. We demonstrate that character-level perturbations are significantly more effective for watermark removal under the most restrictive threat model. We further propose guided removal attacks based on the Genetic Algorithm (GA) that uses a reference detector for optimization. Under a practical threat model with limited black-box queries to the watermark detector, our method demonstrates strong removal performance. Experiments confirm the superiority of character-level perturbations and the effectiveness of the GA in removing watermarks under realistic constraints. Additionally, we argue there is an adversarial dilemma when considering potential defenses: any fixed defense can be bypassed by a suitable perturbation strategy. Motivated by this principle, we propose an adaptive compound character-level attack. Experimental results show that this approach can effectively defeat the defenses. Our findings highlight significant vulnerabilities in existing LLM watermark schemes and underline the urgency for the development of new robust mechanisms.

MD Mehraz Hosen, Md. Hasibul Islam

Tomato crop health plays a critical role in ensuring agricultural productivity and food security. Timely and accurate detection of diseases affecting tomato plants is vital for effective disease management. In this study, we propose a deep learning-based approach for Tomato Leaf Disease Detection using two well-established convolutional neural networks (CNNs), namely VGG19 and Inception v3. The experiment is conducted on the Tomato Villages Dataset, encompassing images of both healthy tomato leaves and leaves afflicted by various diseases. The VGG19 model is augmented with fully connected layers, while the Inception v3 model is modified to incorporate a global average pooling layer and a dense classification layer. Both models are trained on the prepared dataset, and their performances are evaluated on a separate test set. This research employs VGG19 and Inception v3 models on the Tomato Villages dataset (4525 images) for tomato leaf disease detection. The models' accuracy of 93.93% with dropout layers demonstrates their usefulness for crop health monitoring. The paper suggests a deep learning-based strategy that includes normalization, resizing, dataset preparation, and unique model architectures. During training, VGG19 and Inception v3 serve as feature extractors, with possible data augmentation and fine-tuning. Metrics like accuracy, precision, recall, and F1 score are obtained through evaluation on a test set and offer important insights into the strengths and shortcomings of the model. The method has the potential for practical use in precision agriculture and could help tomato crops prevent illness early on.

Enqiang Zhu, Xiang Li, Chanjuan Liu et al.

The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks without considering the potential inter-relationships between the feature spaces of drugs and diseases. This can lead to inaccurate encoding, resulting in biased mined drug-disease association information. To address this limitation, we propose a new model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows the mining of two features (similarity and association) from the drug-disease biomedical networks to encode drugs and diseases. A self-attention mechanism is utilized to extract neighbor feature information. It incorporates two dual-feature extraction modules: the single-domain dual-feature extraction (SDDFE) module for extracting features within a single domain (drugs or diseases) and the cross-domain dual-feature extraction (CDDFE) module for extracting features across domains. By utilizing these modules, we ensure more appropriate encoding of drugs and diseases. A cross-dual-domain decoder is also designed to predict drug-disease associations in both domains. Our proposed DFDRNN model outperforms six state-of-the-art methods on four benchmark datasets, achieving an average AUROC of 0.946 and an average AUPR of 0.597. Case studies on two diseases show that the proposed DFDRNN model can be applied in real-world scenarios, demonstrating its significant potential in drug repositioning.

Fatemah Alharthi, Dhruvil Solanki, Ishmael Apachigawo et al.

Parkinsons disease (PD) is considered one of the most frequent neurological diseases in the world. There is a need to study the early and efficient biomarkers of Parkinsons, such as changes in structural disorders like DNA and chromatin, especially at the subcellular level in the human brain. We used two techniques, Partial wave spectroscopy (PWS) and Inverse Participation Ratio (IPR), to detect the changes in structural disorder in the human brain tissue samples. It was observed from the PWS experiment that there was an increase in structural disorder in Parkinsons disease tissues and cells when compared to normal tissues and cells using mesoscopic light transport theory. Furthermore, the IPR experiment also showed DNA and chromatin structural alterations that have the same trend and support the PWS results. The increase in mass density in the nuclei components, such as DNA and chromatin, can be linked to the aggregation of alpha-synuclein in the substantia nigra of the brain. This protein deposition is considered a significant cause of neuronal death in the brains of PD patients. We also did a histological analysis of brain tissues, which supports our results from dual photonics techniques. The results show that this dual technique is a powerful approach to detect the changes. Our results highlight the potential of the parameter, related to the structural disorder strength, as an efficient biomarker for PD progress, paving the way for research into early disease detection.

Alessandro Viani, Boris A Gutman, Emile d'Angremont et al.

Modelling the progression of Degenerative Diseases (DD) is essential for detection, prevention, and treatment, yet it remains challenging due to the heterogeneity in disease trajectories among individuals. Factors such as demographics, genetic conditions, and lifestyle contribute to diverse phenotypical manifestations, necessitating patient stratification based on these variations. Recent methods like Subtype and Stage Inference (SuStaIn) have advanced unsupervised stratification of disease trajectories, but they face potential limitations in robustness, interpretability, and temporal granularity. To address these challenges, we introduce Disease Progression Modelling and Stratification (DP-MoSt), a novel probabilistic method that optimises clusters of continuous trajectories over a long-term disease time-axis while estimating the confidence of trajectory sub-types for each biomarker. We validate DP-MoSt using both synthetic and real-world data from the Parkinson's Progression Markers Initiative (PPMI). Our results demonstrate that DP-MoSt effectively identifies both sub-trajectories and subpopulations, and is a promising alternative to current state-of-the-art models.

Yumeng Yang, Ashley Gilliam, Ethan B Ludmir et al.

Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment. This study aims to evaluate the generalizability of eligibility classification across a broad spectrum of clinical trials. Starting with phase 3 cancer trials, annotated with seven eligibility exclusions, then to determine how well models can generalize to non-cancer and non-phase 3 trials. To assess this, we have compiled eligibility criteria data for five types of trials: (1) additional phase 3 cancer trials, (2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes trials, and (5) observational trials for any disease, comprising 2,490 annotated eligibility criteria across seven exclusion types. Our results show that models trained on the extensive cancer dataset can effectively handle criteria commonly found in non-cancer trials, such as autoimmune diseases. However, they struggle with criteria disproportionately prevalent in cancer trials, like prior malignancy. We also experiment with few-shot learning, demonstrating that a limited number of disease-specific examples can partially overcome this performance gap. We are releasing this new dataset of annotated eligibility statements to promote the development of cross-disease generalization in clinical trial classification.

Tess Watt, Christos Chrysoulas, Peter J Barclay

Image classification usually requires connectivity and access to the cloud which is often limited in many parts of the world, including hard to reach rural areas. TinyML aims to solve this problem by hosting AI assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without internet or cloud access. This pilot study explores the use of tinyML to provide healthcare support with low spec devices in low connectivity environments, focusing on diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, 10,000 images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without internet access. It was found that the developed prototype achieved a test accuracy of 78% and a test loss of 1.08.

Christina Beainy, Chadia Haddad, Feten Fekih-Romdhane et al.

Abstract Background There are, to date, limited and inconsistent findings concerning the relationship between insight and psychotic symptoms, despite some evidence in favor of the clinical and therapeutic relevance of the insight construct. We aimed to add to the pool of the available data in this area, by examining the correlations between the severity of insight and positive psychotic symptoms (delusions and auditory hallucinations), while accounting for self-stigma and attitudes towards medication, in a sample of long-stay inpatients with schizophrenia. Methods A cross-sectional study was conducted at the Psychiatric Hospital of the Cross, between July and October 2021. A total of 82 patients diagnosed with schizophrenia (aged 55.55 ± 10.21 years, 54.9% males) were enrolled. The semi-structured psychotic symptom rating scales, the Birchwood Insight Scale, the Belief About Medicine Questionnaire, and the Internalized Stigma of Mental Illness were used. Results The mean duration of illness in years was 30.15 ± 11.73, and the mean duration of hospitalization in years was 17.56 ± 9.24. Sixteen out of the 82 patients (19.5%) were considered as having poor insight. Bivariate analyses showed that higher chlorpromazine equivalent dose was significantly associated with more delusions, whereas higher insight was significantly associated with lower delusions. Multivariable analyses revealed that Higher chlorpromazine equivalent dose (Beta = 0.004) was significantly associated with more delusions, whereas higher insight (Beta = − 0.89) was significantly associated with less delusions. No significant associations were found between insight, self-stigma and hallucinations. Conclusion Our results imply that more impaired insight is associated with greater severity of delusions, above and beyond the effects of self-stigma and medication doses. These findings are valuable to aid clinicians and researchers improve their understanding of the relationship insight-psychotic symptoms, and could help personalize prevention and early intervention strategies in schizophrenia.

Faheem Sheriff, Jonathan Lavezo, Ryan Floresca et al.

Background Acute ischemic stroke secondary to large vessel occlusion is among the most serious complications associated with COVID‐19 infection resulting in worse morbidity and mortality. We sought to study the association between COVID‐19 infection and large vessel occlusion thrombus pathology to better define the etiopathogenesis of this atypical cause of stroke. Methods Thrombi were collected during mechanical thrombectomy and stained using hematoxylin and eosin. Blinded analysis of pathology was prospectively performed by a board‐certified neuropathologist. Red blood cell, fibrin, and white blood cell predominance was ascertained. Concomitant peripheral blood counts and clinical and imaging data were collected and analyzed. All samples underwent performance of reverse transcription polymerase chain reaction for SARS‐CoV2. Results Between January 2020 and February 2022, a total of 952 acute ischemic stroke admissions were seen at the University Medical Center of El Paso, TX. Of these, 195 patients (20.5%) had large vessel occlusions and underwent mechanical thrombectomy and 53 patients had thrombus collected and analyzed. Seven patients (3.6%) tested positive for SARS‐CoV2. COVID‐19 positive patients were more likely to be younger (mean 57.4 years; P=0.07), male (85.7%; P=0.03), and have red blood cell predominant thrombi (85.7%; P=0.03). There was a statistically significant association between peripheral neutrophil count and white blood cell lysis in the overall cohort (P=0.015), who did not differ according to COVID‐19 status. Conclusion Thrombi retrieved from patients who were COVID‐19 positive and had stroke demonstrated red blood cell predominance. This finding requires further investigation using appropriate immunohistochemical techniques in a larger cohort of patients.

Gerrit van Schalkwyk

Chunyan Ao, Zhichao Xiao, Lixin Guan et al.

In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle, and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNAdisease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrixbased algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we delve into the present challenges and future prospects concerning drug-disease associations.