Hasil untuk "Neurology. Diseases of the nervous system"

M. Mattson, T. Shea

O. Lindvall, Z. Kokaia

K. Mizugishi, T. Yamashita, A. Olivera et al.

YUAN Qing-zhen, XU Xing-hua, GAN Zhi-chao et al.

Objective To evaluate the health economics differences of 3 minimally invasive surgical techniques about neuroendoscopic surgery, frameless stereotactic catheter drainage guided by imaging navigation (puncture drainage), and small bone window craniotomy over a 6-month follow-up period. Methods Total 651 patients with hypertensive intracerebral hemorrhage treated between July 2016 and June 2022 at 16 medical centers in China were included: neuroendoscopic surgery (n=219), puncture drainage (n=220), and small bone window craniotomy (n=212). Neurological outcomes at 6 months were assessed using the modified Rankin Scale (mRS), which was then mapped to health utility values to calculate quality adjusted life year (QALY). Cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) models were constructed to calculate the incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR), with one-way and probabilistic sensitivity analyses (PSA) performed to assess result robustness. Results Puncture drainage achieved the lowest hospitalization cost (77351 CNY) and favorable health utility (QALY=0.204 years), resulting in the most favorable CUA (379110 CNY/QALY), making the optimal surgical approach with the greatest health economics advantage. Although neuroendoscopic surgery yielded slightly higher QALY (0.218 years), its high consumable and equipment costs led to an ICUR of approximately 1059630 CNY/QALY, far exceeding China's willingness-to-pay threshold, limiting its broad application. Small bone window craniotomy showed the lowest benefit, with the lowest QALY (0.189 years) and the highest cost (100947 CNY), and was therefore deemed an economically inferior strategy. One-way and PSA showed that puncture drainage had the greatest health economics advantage and was the only surgical procedure that maintained its economics advantage across different parameter assumptions and willingness-to-pay thresholds. Conclusions Under the current healthcare cost structure and willingness-to-pay context, puncture drainage demonstrates the best performance in terms of cost, clinical effectiveness and health economics advantage, making it the preferred minimally invasive surgical approach for the treatment of hypertensive intracerebral hemorrhage. Neuroendoscopic surgery, despite higher health utility value, is better suited for individualized use in specific patient populations due to cost constraints. Small bone window craniotomy, with high cost and low benefit, is not recommended as a routine surgery option. This study provides systematic and quantitative evidence to support clinical decision, making regarding surgical pathway selection and the optimization of health insurance reimbursement policies.

Md. Ehsanul Haque, Md. Saymon Hosen Polash, Rakib Hasan Ovi et al.

Grapes are among the most economically and culturally significant fruits on a global scale, and table grapes and wine are produced in significant quantities in Europe and Asia. The production and quality of grapes are significantly impacted by grape diseases such as Bacterial Rot, Downy Mildew, and Powdery Mildew. Consequently, the sustainable management of a vineyard necessitates the early and precise identification of these diseases. Current automated methods, particularly those that are based on the YOLO framework, are often computationally costly and lack interpretability that makes them unsuitable for real-world scenarios. This study proposes grape leaf disease classification using Optimized DenseNet 121. Domain-specific preprocessing and extensive connectivity reveal disease-relevant characteristics, including veins, edges, and lesions. An extensive comparison with baseline CNN models, including ResNet18, VGG16, AlexNet, and SqueezeNet, demonstrates that the proposed model exhibits superior performance. It achieves an accuracy of 99.27%, an F1 score of 99.28%, a specificity of 99.71%, and a Kappa of 98.86%, with an inference time of 9 seconds. The cross-validation findings show a mean accuracy of 99.12%, indicating strength and generalizability across all classes. We also employ Grad-CAM to highlight disease-related regions to guarantee the model is highlighting physiologically relevant aspects and increase transparency and confidence. Model optimization reduces processing requirements for real-time deployment, while transfer learning ensures consistency on smaller and unbalanced samples. An effective architecture, domain-specific preprocessing, and interpretable outputs make the proposed framework scalable, precise, and computationally inexpensive for detecting grape leaf diseases.

Hugh Palmer

This paper uses the Tarot arc of the Devil, the Tower, the Star, and the Fool to explore systemic collapse and the logics of masculinised power. Drawing on archetypal imagery, ecological systems theory, posthumanist feminism, and lived experience, it argues that the panmorphic crisis (Simon, 2021) of climate change, technological acceleration, and political instability are not merely failures of implementation. They reflect a deeper failure of imagination. The Tower is falling because it was built on the ideology of the Devil, to deny relationship, vulnerability, and feedback.  In its place, the Star offers a different kind of intelligence: attentive, embodied, and quietly relational. Figures such as Trump and Musk are read not as aberrations but as expressions of a system that rewards shamelessness and disconnection. The paper invites readers into an ethic of reorientation, recognising even those we most oppose as part of our systemic kin. The Fool, traditionally male, is reclaimed as a post-binary, post-certainty figure who gestures toward a different way of going on, a journey that is uncertain, attentive, and deeply relational.

Guibo Qi, Han Tang, Jianian Hu et al.

Tanycytes, specialized ependymal cells located in the hypothalamus, play a crucial role in the generation of new neurons that contribute to the neural circuits responsible for regulating the systemic energy balance. The precise coordination of the gene networks controlling neurogenesis in naive and mature tanycytes is essential for maintaining homeostasis in adulthood. However, our understanding of the molecular mechanisms and signaling pathways that govern the proliferation and differentiation of tanycytes into neurons remains limited. This article aims to review the recent advancements in research into the mechanisms and functions of tanycyte-derived neurogenesis. Studies employing lineage-tracing techniques have revealed that the neurogenesis specifically originating from tanycytes in the hypothalamus has a compensatory role in neuronal loss and helps maintain energy homeostasis during metabolic diseases. Intriguingly, metabolic disorders are considered early biomarkers of Alzheimer’s disease. Furthermore, the neurogenic potential of tanycytes and the state of newborn neurons derived from tanycytes heavily depend on the maintenance of mild microenvironments, which may be disrupted in Alzheimer’s disease due to the impaired blood–brain barrier function. However, the specific alterations and regulatory mechanisms governing tanycyte-derived neurogenesis in Alzheimer’s disease remain unclear. Accumulating evidence suggests that tanycyte-derived neurogenesis might be impaired in Alzheimer’s disease, exacerbating neurodegeneration. Confirming this hypothesis, however, poses a challenge because of the lack of long-term tracing and nucleus-specific analyses of newborn neurons in the hypothalamus of patients with Alzheimer’s disease. Further research into the molecular mechanisms underlying tanycyte-derived neurogenesis holds promise for identifying small molecules capable of restoring tanycyte proliferation in neurodegenerative diseases. This line of investigation could provide valuable insights into potential therapeutic strategies for Alzheimer’s disease and related conditions.

Léa Chauveau, Julie Gonneaud, Géraldine Poisnel et al.

Abstract Background Cortico-hippocampal functional networks, specifically the anterior-temporal (AT) and posterior-medial (PM) systems, are crucial for memory and highly vulnerable to aging and Alzheimer’s disease (AD). While modifiable cardiovascular risk factors may offer prevention opportunities to preserve brain aging, their effects on AT/PM functional connectivity remain unknown. This study aims to investigate these associations in older adults, considering major risk categories and exploring potential interactions with protective lifestyle habits and AD risk factors. Methods One hundred thirty-one community-dwelling cognitively unimpaired adults aged 65 + were selected from the Age-Well trial, a French monocentric population-based study conducted from 2016 to 2020. Resting-state fMRI and cardiovascular risk assessments were performed at baseline and 18-month follow-up. Functional connectivity within the AT and PM networks was derived from seed-based analyses using the perirhinal and parahippocampal cortices as individual seeds, respectively. Generalized additive and linear mixed models assessed the effects of cardiovascular risk factors on AT/PM functional connectivity, including interactions with protective lifestyle habits and AD risk factors. Results Baseline mean age was 69 (65–84) years, with 63.5% women. Higher abdominal fat (95% CI: -0.00118, -0.00005; F = 5.39; P =.02), higher LDL cholesterol (95% CI: -0.01642, -0.00345; F = 10.40; P =.001), longer smoking duration (95% CI: NA; F = 3.89; P =.03) and greater alcohol consumption (95% CI: -0.01134, -0.00045; F = 4.66; P =.02) were consistently associated with lower PM connectivity, collectively explaining 11.4% of the variance. However, only LDL cholesterol survived multiple comparisons, possibly reflecting a more direct involvement in cardiovascular mechanisms affecting functional connectivity. No association was found with AT connectivity. Exploratory analyses showed that these relationships were independent of cerebral Aβ-positivity or APOE-ε4 carrier status and were unaffected by physical activity and Mediterranean diet when considered separately. Discussion This study highlights converging associations between higher cardiovascular risk factors and lower functional connectivity in cognitively unimpaired older adults, specifically affecting the PM—but not AT—network, and independent of AD risk. Targeting these specific modifiable factors may prevent age-related network alterations to promote cognitive health in aging. Trial registration information The Age-Well trial was registered with ClinicalTrials.gov on November 25, 2016 (identifier: NCT02977819).

Aditya Raj, Golrokh Mirzaei

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases.

Jianshu She, Wenhao Zheng, Zhengzhong Liu et al.

The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer faster decoding and lower resource consumption but often suffer from degraded response quality and heightened susceptibility to hallucinations. To address this trade-off, collaborative decoding, in which a large model assists in generating critical tokens, has emerged as a promising solution. This paradigm leverages the strengths of both model types by enabling high-quality inference through selective intervention of the large model, while maintaining the speed and efficiency of the smaller model. In this work, we present a novel collaborative decoding inference system that allows small models to perform on-device inference while selectively consulting a cloud-based large model for critical token generation. Remarkably, the system achieves a 60% performance gain on CommonsenseQA using only a 0.5B model on an M1 MacBook, with under 7% of tokens generation uploaded to the large model in the cloud.

Li-Chin Chen, Ji-Tian Sheu, Yuh-Jue Chuang

Demographic attributes are universally present in electronic health records. They are the most widespread information across populations and diseases, and serve as vital predictors in clinical risk stratification and treatment decisions. Despite their significance, these attributes are often treated as auxiliaries in model design, with limited attention being paid to learning their representations. This study explored the development of a General Demographic Pre-trained (GDP) model as a foundational model tailored to demographic attributes, focusing on age and gender. The model is pre-trained and evaluated using datasets with diverse diseases and populations compositions from different geographic regions. The composition of GDP architecture was explored through examining combinations of ordering approaches and encoding methods to transform tabular demographic inputs into effective latent embeddings. Results demonstrate the feasibility of GDP to generalize across task, diseases, and populations. In detailed composition, the sequential ordering substantially improves model performance in discrimination, calibration, and the corresponding information gain at each decision tree split, particularly in diseases where age and gender contribute significantly to risk stratification. Even in datasets where demographic attributes hold relatively low predictive value, GDP enhances the representational importance, increasing their influence in downstream gradient boosting models. The findings suggest that foundation models for tabular demographic attributes offer a promising direction for improving predictive performance in healthcare applications.

Clara Tourbez, Claude Semay, Cyrille Chevalier

When particles interact via two-body short-range central potential wells, binding can occur for some critical values of the coupling constants. Using the envelope theory, upper bounds for critical coupling constants are computed for quantum nonrelativistic systems containing identical particles and systems containing identical particles plus a different one.

I. N. Schaik, J. Léger, E. Nobile‐Orazio et al.

Youcef Ferdi

The advances in computer vision made possible by deep learning technology are increasingly being used in precision agriculture to automate the detection and classification of plant diseases. Symptoms of plant diseases are often seen on their leaves. The leaf images in existing datasets have been collected either under controlled conditions or in the field. The majority of previous studies have focused on identifying leaf diseases using images captured in controlled laboratory settings, often achieving high performance. However, methods aimed at detecting and classifying leaf diseases in field images have generally exhibited lower performance. The objective of this study is to evaluate the impact of a data augmentation approach that involves removing complex backgrounds from leaf images on the classification performance of apple leaf diseases in images captured under real world conditions. To achieve this objective, the lightweight pre-trained MobileNetV2 deep learning model was fine-tuned and subsequently used to evaluate the impact of expanding the training dataset with background-removed images on classification performance. Experimental results show that this augmentation strategy enhances classification accuracy. Specifically, using the Adam optimizer, the proposed method achieved a classification accuracy of 98.71% on the Plant Pathology database, representing an approximately 3% improvement and outperforming state-of-the-art methods. This demonstrates the effectiveness of background removal as a data augmentation technique for improving the robustness of disease classification models in real-world conditions.

Daniel A. M. Villela

The emergence or adaptation of pathogens may lead to epidemics, highlighting the need for a thorough understanding of pathogen evolution. The tradeoff hypothesis suggests that virulence evolves to reach an optimal transmission intensity relative to the mortality caused by the disease. This study introduces a mathematical model that incorporates key factors such as recovery times and mortality rates, focusing on the diminishing effects of parasite growth on transmission, with a focus on vector-borne diseases. The analysis reveals conditions under which heightened virulence occurs in hosts, indicating that these factors can support vector-host transmission of a pathogen, even if the host-only component is insufficient for sustainable transmission. This insight helps explain the significant presence of pathogens with high fatality rates, such as those in vector-borne diseases. The findings underscore an elevated risk for future outbreaks involving such diseases. Enhanced surveillance of mortality rates and techniques to monitor pathogen evolution are vital to effectively control future epidemics. This study provides essential insights for epidemic preparedness and highlights the need for ongoing research into pathogen evolution.

Hina Shaheen, Roderick Melnik, The Alzheimer's Disease Neuroimaging Initiative

Alzheimer's disease (AD) is a prominent, worldwide, age-related neurodegenerative disease that currently has no systemic treatment. Strong evidence suggests that permeable amyloid-beta peptide (Abeta) oligomers, astrogliosis and reactive astrocytosis cause neuronal damage in AD. A large amount of Abeta is secreted by astrocytes, which contributes to the total Abeta deposition in the brain. This suggests that astrocytes may also play a role in AD, leading to increased attention to their dynamics and associated mechanisms. Therefore, in the present study, we developed and evaluated novel stochastic models for Abeta growth using ADNI data to predict the effect of astrocytes on AD progression in a clinical trial. In the AD case, accurate prediction is required for a successful clinical treatment plan. Given that AD studies are observational in nature and involve routine patient visits, stochastic models provide a suitable framework for modelling AD. Using the approximate Bayesian computation (ABC) approach, the AD etiology may be modelled as a multi-state disease process. As a result, we use this approach to examine the weak and strong influence of astrocytes at multiple disease progression stages using ADNI data from the baseline to 2-year visits for AD patients whose ages ranged from 50 to 90 years. Based on ADNI data, we discovered that the strong astrocyte effect (i.e., a higher concentration of astrocytes as compared to Abeta) could help to lower or clear the growth of Abeta, which is a key to slowing down AD progression.

Shobhit Aggarwal, Arnab Purkayastha

The COVID-19 pandemic has highlighted the need for innovative solutions to monitor and control the spread of infectious diseases. With the potential for future pandemics and the risk of outbreaks particularly in academic institutions, there is a pressing need for effective approaches to monitor and manage such diseases. Contact tracing using Global Positioning Systems (GPS) has been found to be the most prevalent method to detect and tackle the extent of outbreaks during the pandemic. However, these services suffer from the inherent problems of infringement of data privacy that creates hindrance in adoption of the technology. Non-cellular wireless technologies on the other hand are well-suited to provide secure contact tracing methods. Such approaches integrated with the Internet of Things (IoT) have a great potential to aid in the fight against any type of infectious diseases. In response, we present a unique approach that utilizes an IoT based generic framework to identify individuals who may have been exposed to the virus, using contact tracing methods, without compromising the privacy aspect. We develop the architecture of our platform, including both the frontend and backend components, and demonstrate its effectiveness in identifying potential COVID-19 exposures (as a test case) through a proof-of-concept implementation. We also implement and verify a prototype of the device. Our framework is easily deployable and can be scaled up as needed with the existing infrastructure.

Hanie Yavarpour-Bali, Maryam Ghasemi-Kasman

Novel coronavirus (severe acute respiratory syndrome coronavirus-2: SARS-CoV-2) has a high homology with other cousin of coronaviruses such as SARS and Middle East respiratory syndrome-related coronavirus (MERS). After outbreak of the SARS-CoV-2 in China, it has spread so fast around the world. The main complication of coronavirus disease 2019 (COVID-19) is respiratory failure, but several patients have also been admitted to the hospital with neurological symptoms. Direct invasion, hematogenic rout, retrograde and anterograde transport along peripheral nerves are considered as main neuroinvasion mechanisms of SARS-CoV-2. In the present study, we describe the possible routes for entering of SARS-CoV-2 into the nervous system. Then, the neurological manifestations of the SARS-CoV-2 infection in the central nervous system (CNS) and peripheral nervous system (PNS) are reviewed. Furthermore, the neuropathology of the virus and its impacts on other neurological disorders are discussed.

P. Delaney

Hu Shaohua

The aim of this study is to explore the diagnostic strategies and options for treatment-resistant depression （TRD）. Despite the well-established efficacy of antidepressants， 20%~30% of depressive patients in the clinic fail to respond or respond poorly to normative treatment with antidepressants. Patients with TRD are forced to bear a heavy burden of medical costs and disease. Therefore， this article discusses the TRD in terms of the definition， prevalence， disease burden， etiological mechanism， risk factors， assessment grading， highlighting different treatment strategies and options to inform clinical practice and scientific research on TRD.