Hasil untuk "Neurology. Diseases of the nervous system"

Oleksii S. Nekhlopochyn, Vadim V. Verbov, Ievgen V. Cheshuk et al.

Introduction: Burst fractures of the thoracolumbar junction (TLJ, T10–L2) are common spinal injuries associated with a high risk of neurological complications. Transpedicular fixation is one of the most effective treatment methods; however, the optimal choice of fixation configuration remains unresolved. This study aims to analyze the stress-strain state of various short-segment transpedicular fixation configurations for Th12 vertebra burst fractures under lateral flexion loading. Materials and methods: A finite element model of the Th9–L5 spinal segment with a simulated Th12 burst fracture was created. Four fixation configurations were considered: M1 – short screws in Th11 and L1 (without intermediate screws), M2 – long screws in Th11 and L1 (without intermediate screws), M3 – short screws in Th11 and L1 with intermediate screws in Th12, and M4 – long screws in Th11 and L1 with intermediate screws in Th12. The models were analyzed using CosmosM software, assessing equivalent von Mises stress at 18 control points. Loads simulated physiological lateral trunk bending. Results: Models with long screws (M2, M4) demonstrated lower maximum stresses in connecting rods (315.5–321.0 MPa) compared to short screws (324.8–324.9 MPa). The inclusion of intermediate screws (M3, M4) significantly reduced stress in the fractured Th12 vertebra (by up to 28%), in adjacent vertebral endplates (by 18–25%), and at screw entry points into vertebral arches (up to 28%). The lowest fixation screw stresses were observed in the model with long and intermediate screws (up to 38% lower compared to the baseline model M1). However, intermediate screws minimally influenced stresses in the connecting rods (up to 1.2%). Conclusions: The optimal short-segment transpedicular fixation configuration is the use of long screws in adjacent vertebrae combined with intermediate fixation in the fractured vertebra (M4). This approach provides optimal load distribution, reduces the risk of construct failure, and preserves mobility of adjacent segments. Long screws improve overall system stiffness, while intermediate screws effectively stabilize the damaged segment and significantly unload critical areas of the construct and adjacent anatomical structures.

Thibaut Burg, Ludo Van Den Bosch

Abstract Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease affecting the adult motor system, with no effective treatments available. Despite extensive research efforts, the exact pathological cascade leading to progressive motor neuron degeneration remains elusive. Recent evidence highlights significant modifications in lipid metabolism during ALS progression, even before the onset of motor symptoms. Glycerophospholipids, the primary components of cellular membranes, are frequently altered in ALS patients and models. These lipids not only play a structural role in membranes, but also contribute to cellular metabolism, signaling pathways, and cell type-specific processes such as neuronal transmission and muscle contraction. In this review, we discuss glycerophospholipid physiological functions in the motor system and review recent studies demonstrating their alterations and the possible underlying pathological mechanisms in ALS. Furthermore, we discuss challenges emerging from studying lipid alterations in neurodegeneration and evaluate the therapeutic potential of glycerophospholipids.

Guoguang Lv, Shiyu Zhang, Ting Zhang et al.

A 71 year-old male was diagnosed of epidermoid cyst located in diploe and cerebellum. The skull part was found firstly and kept steady for more than 5 years. The cerebellar part was found nearby when dizziness and vomit happened. The patient has gone through a traumatic brain injury 4 decades ago. All lesions were resected totally. Interestingly the dura mater was confirmed intact without any leakage into subdural space. Imaging and pathological materials are collected completely. Conclusion: We report a case that EC was found in both skull and cerebellum whereas the dural mater was intact. Epidermoid cell migration or infiltration are possible explanations. Gross total resection is advanced for better clinical outcome.

Austin Polanco, M. E. J. Newman

Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.

Yutao Jin, Haowen Xiao, Junyong Zhai et al.

Mild Cognitive Impairment (MCI) serves as a prodromal stage of Alzheimer's Disease (AD), where early identification and intervention can effectively slow the progression to dementia. However, diagnosing AD remains a significant challenge in neurology due to the confounders caused mainly by the selection bias of multi-modal data and the complex relationships between variables. To address these issues, we propose a novel visual-language causality-inspired framework named Cross-modal Causal Intervention with Mediator for Alzheimer's Disease Diagnosis (MediAD) for diagnostic assistance. Our MediAD employs Large Language Models (LLMs) to summarize clinical data under strict templates, therefore enriching textual inputs. The MediAD model utilizes Magnetic Resonance Imaging (MRI), clinical data, and textual data enriched by LLMs to classify participants into Cognitively Normal (CN), MCI, and AD categories. Because of the presence of confounders, such as cerebral vascular lesions and age-related biomarkers, non-causal models are likely to capture spurious input-output correlations, generating less reliable results. Our framework implicitly mitigates the effect of both observable and unobservable confounders through a unified causal intervention method. Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases, outperforming other methods in most evaluation metrics. The study showcases the potential of integrating causal reasoning with multi-modal learning for neurological disease diagnosis.

A. Martínez Gimeno, J. Jiménez, R. Baviera et al.

M. Krupa, A. Balogh-Pécsi

Introduction Following the pandemic, we can find many new communication situations. Social relationships have changed a lot and are developing differently due to digital development, new lifestyles, and the effects of COVID-19. These components: social media, the transformation of interpersonal relationships, and the use of the platforms provided by the internet can lead to addictive disorders as risk factors. Objectives In this presentation, we review studies investigating the relationship between the new digital techniques, social connection, and communication development of adults with addictive disorders. We attempt to provide a summary of new theories and the areas currently being researched around the topic. Another aim of our research is to present the new drama-based therapy theories and methods in adults with addictive disorders. Methods To learn about recent international results, we conducted a literature search in 3 databases (PubMed, Medline, Web of Science) using the following keywords: drama therapy, addiction, emotion regulation, and adults, over the past 5 years. Empirical journal articles in English were used to prepare the literature review. Exclusion criteria were: the appearance publication before the year 2017 and the adolescent population. Results Changes in social behavior, emotion regulation, and addictive disorder were correlated. The studies examined social communications and loneliness in primarily cross-sectional studies design. The escapism from interpersonal relations and low self-esteem is the highest motivation to start regular videogame playing or using social media without control which becomes an addictive disorder. Conclusions Problematic social media use and changes in social connection threaten adults’ mental health. The diagnosis of emotion dysregulation, low self-esteem, and social disconnection is the detection of risk factors for addictive disorders. The new methods and tools of drama-based therapy are new prevention possibilities for these risk factors. In this way, it is a relevant issue in the field of education science. Disclosure of Interest None Declared

Xudong Wen, Pan Long, Pan Long

A. Ghenim, D. Brahim, I. Yaich et al.

Introduction Internet use can become uncontrollable, leading to physical and psychological suffering and what is known as cyberaddiction. Objectives To assess the frequency of cyberaddiction in a population of young doctors. Methods We conducted a cross-sectional, descriptive study of a population of young doctors. We collected socio-professional and medical data using a Google Forms self-questionnaire. The Young scale was recommended for screening for cyberaddiction. A score ≥5 indicates Internet addiction. The Hospital Anxiety and Depression Scale (HAD) was adopted to reveal anxiety-depressive disorders. Results A total of 45 physicians responded to our survey. The mean age was 29.93±4.8 years. The sex ratio (M/F) was 0.3. Participants were single in 69% of cases. Residents represented 64% of the population. Physicians were family medicine residents in 11% of cases. The mean Young’s score was 3.13±1.97/8. Cyberaddiction was noted in 24% of cases. A definite anxiety-depressive disorder was found in 6.7% and 13.3% of cases respectively. Internet addiction was significantly associated with female gender (p<0.05) and a positive HAD (A) score (p=0.03). Conclusions According to the results of our study, cyberaddiction is common among medical staff. A preventive strategy is needed to counter the harmful effects of this addiction. Disclosure of Interest None Declared

Benjamin Boecking, Eva Stoettner, Petra Brueggemann et al.

BackgroundAmongst “third-wave” cognitive behavioural therapies, schema therapy demonstrates encouraging efficacy across various mental health conditions. Within this field, clinical interest has begun to converge on the “schema-mode-model” – a conceptualization framework for affective, cognitive and behavioral states that guide individuals’ perceptions and behaviours at a given point in time. Schema mode expressions in patients with chronic tinnitus are as-yet unexamined.MethodsThe present study reports self-report data from N = 696 patients with chronic tinnitus who completed the Schema Mode- and Tinnitus Handicap Inventories alongside measures of perceived stress, anxiety and depression. The Schema Mode Inventory assesses so-called maladaptive “parent”, “child” and “coping” modes. Parent modes can be understood as self-states which are characterized by self-critical and hostile beliefs; child modes are characterized by biographically unmet emotional needs; and coping modes by inflexible attempts to regulate emotion and stabilize one’s sense of self. Descriptive, correlational and mediation analyses investigated schema mode expressions (1) in patients with chronic tinnitus, (2) as compared to published reference values from a healthy control sample, (3) in their relation to other psychological constructs, and (4) regarding their potential role in driving tinnitus-related distress.ResultsPatients reported mild-to-moderate levels of emotional distress. Compared to healthy controls, patients showed (1) high relative expressions of child-, detachment and compliant coping modes and (2) a conspicuously low relative expression of the ‘punitive parent’ mode. Correlational patterns suggested strong associations of (1) parent as well as angry child modes with perceived stress and anxiety, (2) the vulnerable child mode with all measured constructs and (3) emotional distress with - intrapersonally - emotional detachment as well as - interpersonally - alleged compliance. Mediation analyses demonstrated that tinnitus-related distress was driven by significant interactions between child and coping modes.ConclusionsThe study provides initial clinical evidence for the relevance and applicability of schema-mode based formulation and treatment planning in patients with chronic tinnitus.

S. Pérez Oliveira, J. Castilla Silgado, C. Painous et al.

Eleonora Mancini, Ana Tanevska, Andrea Galassi et al.

Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research.

Liqiong Wang, Teng Jin, Jinyu Yang et al.

In the general domain, large multimodal models (LMMs) have achieved significant advancements, yet challenges persist in applying them to specific fields, especially agriculture. As the backbone of the global economy, agriculture confronts numerous challenges, with pests and diseases being particularly concerning due to their complexity, variability, rapid spread, and high resistance. This paper specifically addresses these issues. We construct the first multimodal instruction-following dataset in the agricultural domain, covering over 221 types of pests and diseases with approximately 400,000 data entries. This dataset aims to explore and address the unique challenges in pest and disease control. Based on this dataset, we propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system. To accelerate progress in this field and inspire more researchers to engage, we design a diverse and challenging evaluation benchmark for agricultural pests and diseases. Experimental results demonstrate that Agri-LLaVA excels in agricultural multimodal conversation and visual understanding, providing new insights and approaches to address agricultural pests and diseases. By open-sourcing our dataset and model, we aim to promote research and development in LMMs within the agricultural domain and make significant contributions to tackle the challenges of agricultural pests and diseases. All resources can be found at https://github.com/Kki2Eve/Agri-LLaVA.

Saurav Bharadwaj, Akshita Midha, Shikha Sharma et al.

Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.

Yidan Shi, Leilei Zeng, Mary E. Thompson et al.

Continuous-time multistate models are widely used for analyzing interval-censored data on disease progression over time. Sometimes, diseases manifest differently and what appears to be a coherent collection of symptoms is the expression of multiple distinct disease subtypes. To address this complexity, we propose a mixture hidden Markov model, where the observation process encompasses states representing common symptomatic stages across these diseases, and each underlying process corresponds to a distinct disease subtype. Our method models both the overall and the type-specific disease incidence/prevalence accounting for sampling conditions and exactly observed death times. Additionally, it can utilize partially available disease-type information, which offers insights into the pathway through specific hidden states in the disease process, to aid in the estimation. We present both a frequentist and a Bayesian way to obtain the estimates. The finite sample performance is evaluated through simulation studies. We demonstrate our method using the Nun Study and model the development and progression of dementia, encompassing both Alzheimer's disease (AD) and non-AD dementia.

Zhen Li, Rong Chen, Dachuan Liu et al.

Previous studies have demonstrated that low-intensity transcranial ultrasound stimulation (TUS) can eliminate hippocampal neural activity. However, until now, it has remained unclear how ultrasound modulates theta and gamma oscillations in the hippocampus under different behavioral states. In this study, we used ultrasound to stimulate the CA1 in mice in anesthesia, awake and running states, and we simultaneously recorded the local field potential of the stimulation location. We analyzed the power spectrum, phase-amplitude coupling (PAC) of theta and gamma oscillations, and their relationship with ultrasound intensity. The results showed that (i) TUS significantly enhanced the absolute power of theta and gamma oscillations under anesthesia and in the awake state. (ii) The PAC strength between theta and gamma oscillations is significantly enhanced under the anesthesia and awake states but is weakened under the running state with TUS. (iii) Under anesthesia, the relative power of theta decreases and that of gamma increases as ultrasound intensity increases, and the result under the awake state is opposite that under the anesthesia state. (iv) The PAC index between theta and gamma increases as ultrasound intensity increases under the anesthesia and awake states. The above results demonstrate that TUS can modulate theta and gamma oscillations in the CA1 and that the modulation effect depends on behavioral states. Our study provides guidance for the application of ultrasound in modulating hippocampal function.

Felipe José Corrêa de Oliveira

Asal Nesar Noubari, Wolfgang Wörndl

Studying human factors has gained a lot of interest in recommender systems research recently. User experience plays a vital role in tourism recommender systems since user satisfaction is the main factor that guarantees the success of such recommender systems. In this work, we have designed and implemented a destination recommender system in which the recommendations adapt instantly based on the user preferences. The recommendations can be explored on a world map with additional information. This interface addresses common visualization challenges in recommender systems, such as transparency, justification, controllability, explorability, the cold-start problem, and context awareness. We have conducted a user study to evaluate different aspects of this recommender system from the users' perspective.

Anish Mall, Sanchit Kabra, Ankur Lhila et al.

This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.

Yixin Wang, Zihao Lin, Haoyu Dong

Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases.