Hasil untuk "Neurology. Diseases of the nervous system"

W. Wasserstrom, J. Glass, J. Posner

D. Dickson, Sunhee C. Lee, L. Mattiace et al.

J. Feldmann, A. Prieur, P. Quartier et al.

Chronic infantile neurological cutaneous and articular (CINCA) syndrome is a severe chronic inflammatory disease of early onset, characterized by cutaneous symptoms, central-nervous-system involvement, and arthropathy. In the present study, we report, in seven unrelated patients with CINCA syndrome, distinct missense mutations within the nucleotide-binding site of CIAS1, a gene encoding cryopyrin and previously shown to cause Muckle-Wells syndrome and familial cold urticaria. Because of the severe cartilage overgrowth observed in some patients with CINCA syndrome and the implications of polymorphonuclear cell infiltration in the cutaneous and neurological manifestations of this syndrome, the tissue-specific expression of CIAS1 was evaluated. A high level of expression of CIAS1 was found to be restricted to polymorphonuclear cells and chondrocytes. These findings demonstrate that CIAS1 missense mutations can result in distinct phenotypes with only a few overlapping symptoms and suggest that this gene may function as a potential inducer of apoptosis.

Ye Zhang, B. Barres

Kejin Lu, Jianfa Bai, Qingqiu Li et al.

Accurate classification of lung diseases from chest CT scans plays an important role in computer-aided diagnosis systems. However, medical imaging datasets often suffer from severe class imbalance, which may significantly degrade the performance of deep learning models, especially for minority disease categories. To address this issue, we propose a gender-aware two-stage lung disease classification framework. The proposed approach explicitly incorporates gender information into the disease recognition pipeline. In the first stage, a gender classifier is trained to predict the patient's gender from CT scans. In the second stage, the input CT image is routed to a corresponding gender-specific disease classifier to perform final disease prediction. This design enables the model to better capture gender-related imaging characteristics and alleviate the influence of imbalanced data distribution. Experimental results demonstrate that the proposed method improves the recognition performance for minority disease categories, particularly squamous cell carcinoma, while maintaining competitive performance on other classes.

Dr. Sergei Makarov, Dr. Antony Nazarenko, Dr. Alexander Kuleshov et al.

Dania Dahmash, Andrea Anastassiou, Sarah Glover et al.

Question What are the experiences and needs of parents of children and young people (CYP) aged 5–18 with diagnosed mental health difficulties, particularly in relation to the parents’ own well-being?Study selection and analysis A systematic review with thematic meta-synthesis was conducted, including qualitative studies published in English. Seven databases were searched (MEDLINE, PsycINFO, CINAHL Ultimate, AMED, EMBASE, Web of Science and Cochrane Library) from inception to September 2024. Studies focused on parents of CYP aged 5–18 years, where the CYP had a confirmed mental health diagnosis.Findings Of 75 862 screened studies, 46 met inclusion criteria. Six overarching themes were identified: support needs and gaps; impact on everyday life; altered family dynamics; parental worries and fears; emotional experience of caregivers and self-care paradox. Parents face significant challenges, including unmet support needs from healthcare and education systems, substantial impacts on daily life and altered family dynamics. Emotional experiences such as worry, guilt and stigma were pervasive, compounded by systemic gaps in information and resources. Parents often prioritise their child’s needs over their own, creating barriers to self-care. These challenges were consistent across diagnoses but heightened in cases of life-threatening conditions like eating disorders and depression.Conclusions The findings highlight support needs for parents of CYP with mental health difficulties. Tailored interventions, better professional training and family centred care are needed. Future research should focus on developing theoretical models of parental distress to guide interventions and inform support mechanisms that mitigate these broad impacts on parents’ well-being.

Lina Xiang, Hongwei Wan, Yu Zhu

Abstract Background Psychological resilience refers to maintaining or regaining psychological well-being after experiencing adversity, trauma, or stress. There is evidence suggesting that cognitive behavioral therapy (CBT) can significantly enhance an individual’s coping skills. However, the overall effectiveness of CBT on resilience among cancer patients remains unclear. Therefore, this study systematically evaluated the impact of CBT on resilience among cancer patients. Methods The PubMed, PsycINFO, Cochrane Library, CINAHL, and Embase databases were searched using keywords. Two researchers independently conducted a rigorous evaluation of the quality of the evidence using the GRADE system and independently performed data extraction. A meta-analysis was conducted to calculate the experimental group's effect size and to explore the effects of CBT on enhancing resilience. Results Thirteen randomized controlled trials (RCTs) were included in this meta-analysis. The effect of CBT on increasing resilience among cancer patients was small but significant immediately after the intervention (g = 1.211; p < 0.001). The results showed that CBT delivered via mobile devices was more effective than face-to-face CBT (β = 0.284; P = 0.012). Additionally, group CBT also outperformed individual CBT (β = 0.181; P = 0.042). Furthermore, CBT was more effective among patients with existing tumors (β = 0.285; P = 0.037). The evidence regarding the effects of CBT on resilience was found to be of moderate strength. Conclusions The results of this study indicate CBT can improve resilience among cancer patients. These findings underscore the importance of considering delivery methods and formats when implementing CBT interventions, with mobile device delivery and group formats resulting in better outcomes. The positive effects of CBT on patients with existing tumors highlight the importance of delivering this therapy in specific clinical contexts. Overall, this study provided moderately strong evidence that CBT is a valuable tool for enhancing resilience among cancer patients. Trial registration CRD42021256841.

Bo Liu, Bo Liu, Hui Sun et al.

Bipolar disorder (BD) is a prevalent mood disorder characterized by alternating episodes of depression and mania, often accompanied by varying degrees of cognitive impairment. Cognitive impairments often serve as indicators of a bleak prognosis or the likelihood of progressing to dementia. Additionally, some studies suggest that individuals diagnosed with BD may undergo a decline in hippocampal volume. However, the potential for reversibility of these changes, particularly in adolescents, remains unclear. We present an intriguing case involving an 18-year-old male student who experiences concurrent occurrences of both BD and mild cognitive impairment (MCI), accompanied by a subtle reduction in hippocampal volume. Initially, the individual exhibited impaired general cognitive function, as indicated by an IQ score of 80 on the Standard Raven’s Progressive Matrices test, and demonstrated slightly reduced bilateral hippocampal volume compared to the normative reference, as determined through quantitative structural magnetic resonance imaging (qsMRI). The deposition profiles of amyloid beta (Aβ) peptide in the brain were not identified with 18F-AV45 PET/MRI. Following six months of combined psychopharmacological treatment and cognitive behavioral therapy, the individual’s psychopathological symptoms improved significantly, leading to a restoration of his IQ score to 116 and normalization of hippocampal volume. This case suggests that the hippocampal volume reduction and cognitive impairment seen in some adolescents with BD may demonstrate greater plasticity compared to neurodegenerative conditions such as Alzheimer’s disease (AD). These findings highlight the potential importance of early intervention in young BD patients with cognitive impairments.

Manuel Alejandro Rojo-Romero, Manuel Alejandro Rojo-Romero, Manuel Alejandro Rojo-Romero et al.

IntroductionSuicide is a significant public health problem, with increased rates in low- and middle-income countries such as Mexico; therefore, suicide prevention is important. Suicide is a complex and multifactorial phenomenon in which biological and social factors are involved. Several studies on the biological mechanisms of suicide have analyzed the proteome of the dorsolateral prefrontal cortex (DLPFC) in people who have died by suicide. The aim of this work was to analyze the protein expression profile in the DLPFC of individuals who died by suicide in comparison to age-matched controls in order to gain information on the molecular basis in the brain of these individuals and the selection of potential biomarkers for the identification of individuals at risk of suicide. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide.MethodsBrain tissue (Brodmann area 9) was sampled from male cases (n=9) and age-matched controls (n=7). We analyzed the proteomic differences between the groups using two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Bioinformatics tools were used to clarify the biological relevance of the differentially expressed proteins. In addition, this information was analyzed using machine learning (ML) algorithms to propose a model for predicting suicide.ResultsTwelve differentially expressed proteins were also identified (t14 ≤ 0.5). Using Western blotting, we validated the decrease in expression of peroxiredoxin 2 and alpha-internexin in the suicide cases. ML models were trained using densitometry data from the 2D gel images of each selected protein and the models could differentiate between both groups (control and suicide cases).DiscussionOur exploratory pathway analysis highlighted oxidative stress responses and neurodevelopmental pathways as key processes perturbed in the DLPFC of suicides. Regarding ML models, KNeighborsClassifier was the best predicting conditions. Here we show that these proteins of the DLPFC may help to identify brain processes associated with suicide and they could be validated as potential biomarkers of this outcome.

Guilan Sun, Zhongxia Shen, Minmin Wang et al.

Treatment-resistant depression (TRD) poses a significant challenge in psychiatric practice. While repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising non-invasive neuromodulation technique for TRD, a subset of patients fails to respond adequately to these traditional rTMS protocols. This case report describes the treatment course of a 53-year-old female patient with a complex psychiatric history. Despite initial successful treatment and remission, the patient experienced a relapse of severe depression characterized by sleep disturbances, anxiety, anhedonia, and suicidal ideation. The patient underwent multiple pharmacological treatments, intermittent theta burst stimulation (iTBS) and electroconvulsive therapy (ECT) with limited success over the course of two years. Subsequently, the patient received accelerated continuous theta burst stimulation (a-cTBS) targeting the right dorsolateral prefrontal cortex (DLPFC). Following a-cTBS treatment (18000 pulses each day for 5 consecutive days), the patient showed significant improvement in depressive and anxiety symptoms, as well as in cognitive functions. Remarkable clinical improvement was observed: the Montgomery Depression Rating Scale score decreased from 32 to 9, the Hamilton Anxiety Rating Scale score dropped from 20 to 6, and suicidal ideation decreased from 13 to 5, ultimately disappearing. The outcomes of this intervention suggest that a-cTBS may represent a viable alternative for patients with TRD who do not benefit from existing treatment modalities.

Idris Ochijenu, Monday Abutu Idakwo, Sani Felix

Like many countries, Nigeria is naturally endowed with fertile agricultural soil that supports large-scale tomato production. However, the prevalence of disease causing pathogens poses a significant threat to tomato health, often leading to reduced yields and, in severe cases, the extinction of certain species. These diseases jeopardise both the quality and quantity of tomato harvests, contributing to food insecurity. Fortunately, tomato diseases can often be visually identified through distinct forms, appearances, or textures, typically first visible on leaves and fruits. This study presents an enhanced Capsule-YOLO network architecture designed to automatically segment overlapping and occluded tomato leaf images from complex backgrounds using the YOLO framework. It identifies disease symptoms with impressive performance metrics: 99.31% accuracy, 98.78% recall, and 99.09% precision, and a 98.93% F1-score representing improvements of 2.91%, 1.84%, 5.64%, and 4.12% over existing state-of-the-art methods. Additionally, a user-friendly interface was developed to allow farmers and users to upload images of affected tomato plants and detect early disease symptoms. The system also provides recommendations for appropriate diagnosis and treatment. The effectiveness of this approach promises significant benefits for the agricultural sector by enhancing crop yields and strengthening food security.

Ruihao Zhang, Mao chen, Fei Ye et al.

T cell receptor (TCR) repertoires encode critical immunological signatures for autoimmune diseases, yet their clinical application remains limited by sequence sparsity and low witness rates. We developed EAMil, a multi-instance deep learning framework that leverages TCR sequencing data to diagnose systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) with exceptional accuracy. By integrating PrimeSeq feature extraction with ESMonehot encoding and enhanced gate attention mechanisms, our model achieved state-of-the-art performance with AUCs of 98.95% for SLE and 97.76% for RA. EAMil successfully identified disease-associated genes with over 90% concordance with established differential analyses and effectively distinguished disease-specific TCR genes. The model demonstrated robustness in classifying multiple disease categories, utilizing the SLEDAI score to stratify SLE patients by disease severity as well as to diagnose the site of damage in SLE patients, and effectively controlling for confounding factors such as age and gender. This interpretable framework for immune receptor analysis provides new insights for autoimmune disease detection and classification with broad potential clinical applications across immune-mediated conditions.

Vahid Salehi, Josef Vilsmeier, Shirui Wang

Today, products are no longer isolated artifacts, but nodes in networked systems. This means that traditional, linearly conceived life cycle models are reaching their limits: Interoperability across disciplines, variant and configuration management, traceability, and governance across organizational boundaries are becoming key factors. This collective contribution classifies the state of the art and proposes a practical frame of reference for SoS lifecycle management, model-based systems engineering (MBSE) as the semantic backbone, product lifecycle management (PLM) as the governance and configuration level, CAD-CAE as model-derived domains, and digital thread and digital twin as continuous feedback. Based on current literature and industry experience, mobility, healthcare, and the public sector, we identify four principles: (1) referenced architecture and data models, (2) end-to-end configuration sovereignty instead of tool silos, (3) curated models with clear review gates, and (4) measurable value contributions along time, quality, cost, and sustainability. A three-step roadmap shows the transition from product- to network- centric development: piloting with reference architecture, scaling across variant and supply chain spaces, organizational anchoring (roles, training, compliance). The results are increased change robustness, shorter throughput times, improved reuse, and informed sustainability decisions. This article is aimed at decision-makers and practitioners who want to make complexity manageable and design SoS value streams to be scalable.

Houda Belhad, Asmae Bourbia, Salma Boughanja

Chronic diseases, such as cardiovascular disease, diabetes, chronic kidney disease, and thyroid disorders, are the leading causes of premature mortality worldwide. Early detection and intervention are crucial for improving patient outcomes, yet traditional diagnostic methods often fail due to the complex nature of these conditions. This study explores the application of machine learning (ML) and deep learning (DL) techniques to predict chronic disease and thyroid disorders. We used a variety of models, including Logistic Regression (LR), Random Forest (RF), Gradient Boosted Trees (GBT), Neural Networks (NN), Decision Trees (DT) and Native Bayes (NB), to analyze and predict disease outcomes. Our methodology involved comprehensive data pre-processing, including handling missing values, categorical encoding, and feature aggregation, followed by model training and evaluation. Performance metrics such ad precision, recall, accuracy, F1-score, and Area Under the Curve (AUC) were used to assess the effectiveness of each model. The results demonstrated that ensemble methods like Random Forest and Gradient Boosted Trees consistently outperformed. Neutral Networks also showed superior performance, particularly in capturing complex data patterns. The findings highlight the potential of ML and DL in revolutionizing chronic disease prediction, enabling early diagnosis and personalized treatment strategies. However, challenges such as data quality, model interpretability, and the need for advanced computational techniques in healthcare to improve patient outcomes and reduce the burden of chronic diseases. This study was conducted as part of Big Data class project under the supervision of our professors Mr. Abderrahmane EZ-ZAHOUT and Mr. Abdessamad ESSAIDI.

Christos Theodoropoulos, Andrei Catalin Coman, James Henderson et al.

The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw text. To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease, enabling the identification of semantic relations between biomedical entities. Extensive benchmarking explores various ways to represent relations and entity representations, offering insights into optimal modeling strategies for semantic relation detection and highlighting language models' competence in knowledge discovery. We also conduct probing experiments using different layer representations and attention scores to explore transformers' ability to capture semantic relations.

Jessica L. Ross, Arman Sabbaghi, Run Zhuang et al.

Clinical trials are critical in advancing medical treatments but often suffer from immense time and financial burden. Advances in statistical methodologies and artificial intelligence (AI) present opportunities to address these inefficiencies. Here we introduce Prognostic Covariate-Adjusted Mixed Models for Repeated Measures (PROCOVA-MMRM) as an advantageous combination of prognostic covariate adjustment (PROCOVA) and Mixed Models for Repeated Measures (MMRM). PROCOVA-MMRM utilizes time-matched prognostic scores generated from AI models to enhance the precision of treatment effect estimators for longitudinal continuous outcomes, enabling reductions in sample size and enrollment times. We first provide a description of the background and implementation of PROCOVA-MMRM, followed by two case study reanalyses where we compare the performance of PROCOVA-MMRM versus the unadjusted MMRM. These reanalyses demonstrate significant improvements in statistical power and precision in clinical indications with unmet medical need, specifically Alzheimer's Disease (AD) and Amyotrophic Lateral Sclerosis (ALS). We also explore the potential for sample size reduction with the prospective implementation of PROCOVA-MMRM, finding that the same or better results could have been achieved with fewer participants in these historical trials if the enhanced precision provided by PROCOVA-MMRM had been prospectively leveraged. We also confirm the robustness of the statistical properties of PROCOVA-MMRM in a variety of realistic simulation scenarios. Altogether, PROCOVA-MMRM represents a rigorous method of incorporating advances in the prediction of time-matched prognostic scores generated by AI into longitudinal analysis, potentially reducing both the cost and time required to bring new treatments to patients while adhering to regulatory standards.

Mahendra Kumar Gohil, Anirudha Bhattacharjee, Rwik Rana et al.

Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.

A. Madoz-Gúrpide, E. Ochoa Mangado, P. Cuadrado del Rey

Introduction Stigma in mental illness has a negative impact on the daily functioning of the patient, their personal development and their clinical prognosis. Direct contact with people who suffer from this pathology could modify the stigma towards these populations. Objectives The objective of the study is to assess whether the stigma of mental illness in university students is modified by contact with people suffering from mental illness, established through volunteering activities with that population. Methods Observational case-control study. The sample is made up of young subjects (18 to 35 years old) who have studied or are studying a university degree during the 2021-2022 academic year. The cases (n=91) are subjects who have ever volunteered with people diagnosed with mental illness. Those who have not had this experience constitute the control group (n=237). The variables were collected by completing an anonymous online questionnaire. To analyze stigma, the Attribution Questionnaire-27 questionnaire was used, which offers a total score as well as 9 domains related to stigma. Statistical analysis (including multiple linear regression) was performed with the statistical package IBM SPSS Statistics, version 20. Results Once adjusted for age and gender, the case group scores lower, with statistically significant differences, in the subscales Anger (p-value: 0.001), Dangerousness (p-value: 0.000), Fear (p-value: 0.000 ), Coercion (p-value: 0.028), Segregation (p-value: 0.000), Avoidance (p-value: 0.000), as well as in the Total Score (p-value: 0.000). Likewise, it is also observed that the group of cases score higher on the Help subscale (p-value: 0.002). Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. 95% Confidence Interval for B B Std. Error Beta Lower limit Upper Limit (Constant) 72,745 10,931 6,655 ,000 51,234 94,256 Volunteering 13,100 3,196 ,236 4,098 ,000 6,810 19,391 Age ,669 ,342 ,113 1,956 ,051 -,004 1,342 Gender -,196 2,941 -,004 -,067 ,947 -5,983 5,591 a. Dependent Variable: Total Score Conclusions Previous contact with patients with mental illness through voluntary activities seems to favor less stigma towards mental pathology. Disclosure of Interest None Declared

M. Gheis, G. Sekhon

Introduction Patients with Functional Neurological Disorder have a high return rate to Emergency Rooms. Objectives To assess possible changes in Emergency Room presentation rates in patients with Functional Neurological Disorder following their attendance of specialized long-term multidisciplinary treatment and rehabilitation program. Methods Seventy-two adult patients with Functional Neurological Disorder were included. These patients were consecutive referrals accepted for ongoing specialist FND treatment. The total number of Emergency Room presentations in the year prior to program admission was obtained from central health records. Patients were provided ongoing treatment for one year, during which the number of ER presentations was monitored. Patients received one or more of the following treatment modalities: psychoeducation, psychological therapy, psychologically informed physical and occupational rehabilitation and psychopharmacological treatments. We subsequently compared high and low emergency service users. Low ER users are those with pre-treatment Emergency Room presentations of less than 3 per year. High emergency service users are those who presented to the emergency room 3 or more times per year before the start of their treatment. Results The mean emergency room presentation per year in the year leading to patients referral was 2.6 per patient, SD 9.4; dropped to 1.2 emergency room presentations per year, with a standard deviation of 4.4 in the year following the start of treatment. The difference was statistically significant (p= 0.02). There was a strong positive correlation between the pre and post-treatment number of presentations with a Pearson Correlation Coefficient of 0.976 (95% Confidence Interval 0.962 to 0.985). The reduction in emergency room presentations in both high and low-emergency service user groups was significant, with a mean difference of 12 ER visits a year in high-frequency emergency service users (p= 0.04) and a mean difference of 0.5 visits a year in low-frequency emergency service users (p < 0.001). Conclusions Ongoing specialist treatment and rehabilitation of patients with Functional Neurological Disorder significantly reduce their need for emergency room presentation, regardless of the treatment modality. Disclosure of Interest None Declared