Hasil untuk "Neurology. Diseases of the nervous system"

J. Furness

G. Ming, Hongjun Song

E. Reynolds

G. Román, P. Spencer, J. Reis et al.

A comprehensive review of the neurological disorders reported during the current COVID-19 pandemic demonstrates that infection with SARS-CoV-2 affects the central nervous system (CNS), the peripheral nervous system (PNS) and the muscle. CNS manifestations include: headache and decreased responsiveness considered initial indicators of potential neurological involvement; anosmia, hyposmia, hypogeusia, and dysgeusia are frequent early symptoms of coronavirus infection. Respiratory failure, the lethal manifestation of COVID-19, responsible for 264,679 deaths worldwide, is probably neurogenic in origin and may result from the viral invasion of cranial nerve I, progressing into rhinencephalon and brainstem respiratory centers. Cerebrovascular disease, in particular large-vessel ischemic strokes, and less frequently cerebral venous thrombosis, intracerebral hemorrhage and subarachnoid hemorrhage, usually occur as part of a thrombotic state induced by viral attachment to ACE2 receptors in endothelium causing widespread endotheliitis, coagulopathy, arterial and venous thromboses. Acute hemorrhagic necrotizing encephalopathy is associated to the cytokine storm. A frontal hypoperfusion syndrome has been identified. There are isolated reports of seizures, encephalopathy, meningitis, encephalitis, and myelitis. The neurological diseases affecting the PNS and muscle in COVID-19 are less frequent and include Guillain-Barré syndrome; Miller Fisher syndrome; polyneuritis cranialis; and rare instances of viral myopathy with rhabdomyolysis. The main conclusion of this review is the pressing need to define the neurology of COVID-19, its frequency, manifestations, neuropathology and pathogenesis. On behalf of the World Federation of Neurology we invite national and regional neurological associations to create local databases to report cases with neurological manifestations observed during the on-going pandemic. International neuroepidemiological collaboration may help define the natural history of this worldwide problem.

Stephanie Mehl, Christopher Hautmann, Björn Schlier et al.

Abstract Psychological interventions for delusions may be enhanced by targeting their presumed causal factors. An emotion-oriented variant of cognitive behavioral therapy for delusions (CBTd-E), designed to target affect regulation and maladaptive schemata, was evaluated for its effect on delusions. A single-blind, multicenter, randomized, waitlist-controlled trial was conducted in three German outpatient clinics. Ninety-four patients with psychotic disorders and persistent delusions were randomized to 25 individual sessions of CBTd-E over 6 months (n = 47) or waitlist (n = 47). CBTd-E included two modules designed to improve affect regulation and maladaptive schemata. Assessments were performed at baseline (T1), three months (T2), and six months (T3). Regression-based analysis of covariance at T3 in the intent-to-treat sample indicated no significant benefit for the CBT-E group in the primary outcome (Psychotic Symptom Rating Scale delusions subscale, d = -0.45 [CI: 0.36; -1.26]). Regarding secondary outcomes, a significant effect favoring CBTd-E was observed in general psychopathology (d = -0.56), but no effects on positive and negative symptoms, depression, general and social functioning, or antipsychotic dosage. Regarding the proposed target mechanisms, we found improved cognitive reappraisal (d = 0.59), worrying (d = -0.52), quality of sleep (d = -0.49), and self-esteem (d = 0.36). Despite its effect on the suggested target mechanisms, affect regulation and maladaptive schemata, and on general psychopathology, this emotion-focused variant of CBT did not show an effect on delusions. A possible avenue to achieve stronger effects on delusions is to personalize the modularized interventions. Trial registration: Clinicaltrials.gov Identifier: NCT02787135

Jasmin Herz, A. Filiano, Ashtyn T Smith et al.

ehsan golestani, Akbar Atadokht, Niloofar Mikaeili et al.

This study designed an educational board game and investigated its effectiveness on the symptoms of attention deficit and hyperactivity disorder (ADHD) in children. The population included 40 children aged seven to nine years in Baharestan County, Tehran Province in 2023 and selected by purposeful sampling and divided into two experimental and control groups randomly. The educational game was employed for 10 sessions in the experimental group. The instruments included the SNAP-IV questionnaire and diagnostic interview. Data was analyzed using repeated measures variance test in SPSS 23 software. The results showed that the educational board game had a significant effect on reducing children's hyperactivity symptoms and there was a significant difference between the experimental and control groups. Also, these effects remained stable in the follow-up phase.​​​​​​​​​ According to the findings, it can be generally concluded that game therapy, including physical games and cognitive games such as educational board games, can be used alongside first-line treatments as a complement or as an independent intervention for these children.

J. Oltra Cucarella, C. Iñesta Carrizosa, B. Bonete López et al.

Haokun Zhao, Yingzhe Bai, Qingyang Xu et al.

Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it impractical to perform all possible medical tests on a patient to diagnose or predict hundreds or thousands of diseases. In this work, we propose Collaborative Learning for Disease Detection (CLDD), a novel graph-based deep learning model that formulates disease detection as a collaborative learning task by exploiting associations among diseases and similarities among patients adaptively. CLDD integrates patient-disease interactions and demographic features from electronic health records to detect hundreds or thousands of diseases for every patient, with little to no reliance on the corresponding medical tests. Extensive experiments on a processed version of the MIMIC-IV dataset comprising 61,191 patients and 2,000 diseases demonstrate that CLDD consistently outperforms representative baselines across multiple metrics, achieving a 6.33\% improvement in recall and 7.63\% improvement in precision. Furthermore, case studies on individual patients illustrate that CLDD can successfully recover masked diseases within its top-ranked predictions, demonstrating both interpretability and reliability in disease prediction. By reducing diagnostic costs and improving accessibility, CLDD holds promise for large-scale disease screening and social health security.

Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid et al.

Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.

L Cif, D Demailly, JP Lin et al.

Heterozygous mutations in KMT2B are associated with an early-onset, progressive, and often complex dystonia (DYT28). Key characteristics of typical disease include focal motor features at disease presentation, evolving through a caudocranial pattern into generalized dystonia, with prominent oromandibular, laryngeal, and cervical involvement. Although KMT2B-related disease is emerging as one of the most common causes of early-onset genetic dystonia, much remains to be understood about the full spectrum of the disease. We describe a cohort of 53 patients with KMT2B mutations, with detailed delineation of their clinical phenotype and molecular genetic features. We report new disease presentations, including atypical patterns of dystonia evolution and a subgroup of patients with a non-dystonic neurodevelopmental phenotype. In addition to the previously reported systemic features, our study has identified co-morbidities, including the risk of status dystonicus, intrauterine growth retardation, and endocrinopathies. Analysis of this study cohort (n = 53) in tandem with published cases (n = 80) revealed that patients with chromosomal deletions and protein-truncating variants had a significantly higher burden of systemic disease (with earlier onset of dystonia) than those with missense variants. Eighteen individuals had detailed longitudinal data available after insertion of deep brain stimulation for medically refractory dystonia. Median age at deep brain stimulation was 11.5 years (range: 4.5 to 37.0 years). Follow-up after deep brain stimulation ranged from 0.25 to 22 years. Significant improvement of motor function and disability (as assessed by the Burke-Fahn-Marsden Dystonia Rating Scales, BFMDRS-M and BFMDRS-D) was evident at 6 months, 1 year, and last follow-up (motor, P = 0.001, P = 0.004, and P = 0.012; disability, P = 0.009, P = 0.002, and P = 0.012).

Nathalia A. Loureiro, Camilo R. Neto, Jack Sutton et al.

Inter-city interactions are critical for the transmission of infectious diseases, yet their effects on the scaling of disease cases remain largely underexplored. Here, we use the commuting network as a proxy for inter-city interactions, integrating it with a general scaling framework to describe the incidence of seven infectious diseases across Brazilian cities as a function of population size and the number of commuters. Our models significantly outperform traditional urban scaling approaches, revealing that the relationship between disease cases and a combination of population and commuters varies across diseases and is influenced by both factors. Although most cities exhibit a less-than-proportional increase in disease cases with changes in population and commuters, more-than-proportional responses are also observed across all diseases. Notably, in some small and isolated cities, proportional rises in population and commuters correlate with a reduction in disease cases. These findings suggest that such towns may experience improved health outcomes and socioeconomic conditions as they grow and become more connected. However, as growth and connectivity continue, these gains diminish, eventually giving way to challenges typical of larger urban areas - such as socioeconomic inequality and overcrowding - that facilitate the spread of infectious diseases. Our study underscores the interconnected roles of population size and commuter dynamics in disease incidence while highlighting that changes in population size exert a greater influence on disease cases than variations in the number of commuters.

Netta Palez, Léonie Straß, Sebastian Meller et al.

Canine gait analysis using wearable inertial sensors is gaining attention in veterinary clinical settings, as it provides valuable insights into a range of mobility impairments. Neurological and orthopedic conditions cannot always be easily distinguished even by experienced clinicians. The current study explored and developed a deep learning approach using inertial sensor readings to assess whether neurological and orthopedic gait could facilitate gait analysis. Our investigation focused on optimizing both performance and generalizability in distinguishing between these gait abnormalities. Variations in sensor configurations, assessment protocols, and enhancements to deep learning model architectures were further suggested. Using a dataset of 29 dogs, our proposed approach achieved 96% accuracy in the multiclass classification task (healthy/orthopedic/neurological) and 82% accuracy in the binary classification task (healthy/non-healthy) when generalizing to unseen dogs. Our results demonstrate the potential of inertial-based deep learning models to serve as a practical and objective diagnostic and clinical aid to differentiate gait assessment in orthopedic and neurological conditions.

Mark Gaertner, Mark Gaertner, Christopher M. DeGiorgio et al.

Bandita Abhijita, Jilisha Gnanadhas, Sujita Kumar Kar et al.

Rabah Dabouz, Pénélope Abram, Jose Carlos Rivera et al.

Abstract ‘Wet’ age-related macular degeneration (AMD) is characterized by pathologic choroidal neovascularization (CNV) that destroys central vision. Abundant evidence points to inflammation and immune cell dysfunction in the progression of CNV in AMD. Mast cells are resident immune cells that control the inflammatory response. Mast cells accumulate and degranulate in the choroid of patients with AMD, suggesting they play a role in CNV. Activated mast cells secrete various biologically active mediators, including inflammatory cytokines and proteolytic enzymes such as tryptase. We investigated the role of mast cells in AMD using a model of CNV. Conditioned media from activated mast cells exerts proangiogenic effects on choroidal endothelial cells and choroidal explants. Laser-induced CNV in vivo was markedly attenuated in mice genetically depleted of mast cells (KitW−sh/W−sh) and in wild-type mice treated with mast cell stabilizer, ketotifen fumarate. Tryptase was found to elicit pronounced choroidal endothelial cell sprouting, migration and tubulogenesis; while tryptase inhibition diminished CNV. Transcriptomic analysis of laser-treated RPE/choroid complex revealed collagen catabolism and extracellular matrix (ECM) reorganization as significant events correlated in clusters of mast cell activation. Consistent with these analyses, compared to wildtype mice choroids of laser-treated mast cell-deficient mice displayed less ECM remodelling evaluated using collagen hybridizing peptide tissue binding. Findings herein provide strong support for mast cells as key players in the progression of pathologic choroidal angiogenesis and as potential therapeutic targets to prevent pathological neovascularization in ‘wet’ AMD.

Xuanzhong Chen, Xiaohao Mao, Qihan Guo et al.

Generalist Large Language Models (LLMs), such as GPT-4, have shown considerable promise in various domains, including medical diagnosis. Rare diseases, affecting approximately 300 million people worldwide, often have unsatisfactory clinical diagnosis rates primarily due to a lack of experienced physicians and the complexity of differentiating among many rare diseases. In this context, recent news such as "ChatGPT correctly diagnosed a 4-year-old's rare disease after 17 doctors failed" underscore LLMs' potential, yet underexplored, role in clinically diagnosing rare diseases. To bridge this research gap, we introduce RareBench, a pioneering benchmark designed to systematically evaluate the capabilities of LLMs on 4 critical dimensions within the realm of rare diseases. Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain. To facilitate differential diagnosis of rare diseases, we develop a dynamic few-shot prompt methodology, leveraging a comprehensive rare disease knowledge graph synthesized from multiple knowledge bases, significantly enhancing LLMs' diagnostic performance. Moreover, we present an exhaustive comparative study of GPT-4's diagnostic capabilities against those of specialist physicians. Our experimental findings underscore the promising potential of integrating LLMs into the clinical diagnostic process for rare diseases. This paves the way for exciting possibilities in future advancements in this field.

Nikita Kuzmin, Hieu-Thi Luong, Jixun Yao et al.

In this work, we describe our submissions for the Voice Privacy Challenge 2024. Rather than proposing a novel speech anonymization system, we enhance the provided baselines to meet all required conditions and improve evaluated metrics. Specifically, we implement emotion embedding and experiment with WavLM and ECAPA2 speaker embedders for the B3 baseline. Additionally, we compare different speaker and prosody anonymization techniques. Furthermore, we introduce Mean Reversion F0 for B5, which helps to enhance privacy without a loss in utility. Finally, we explore disentanglement models, namely $β$-VAE and NaturalSpeech3 FACodec.

Erman Şentürk, Behçet Coşar, Zehra Arıkan

Cryptocurrencies has been considered as both an investment tool and a great invention that will replace money and change the world order. Although crypto currency trading has been investigated in many aspects, the psychological dimension that directly affects investors has often been ignored. Control of cryptocurrency trading is in the hands of investors rather than a central authority or institution. Thus, the value of cryptocurrencies changes with the reactions of investors. This situation suggests that psychological factors may be more prominent in cryptocurrency trading. Cryptocurrency trading has many similarities with gambling and betting, such as risk taking, getting quick returns, extreme gains or losses. Some significant components of behavioral addiction are also seen in individuals who spend so much time with cryptocurrency trading. The purpose of this article is to provide a better understanding of the psychological effects of cryptocurrency trading, which has entered our lives over a relatively brief period of time and reached millions of investors.

Esther-Sevil Eigl, Laura Krystin Urban-Ferreira, Manuel Schabus et al.

BackgroundApproximately one-third of the healthy population suffer from sleep problems, but only a small proportion of those affected receive professional help. Therefore, there is an urgent need for easily accessible, affordable, and efficacious sleep interventions.ObjectiveA randomized controlled study was conducted to investigate the efficacy of a low-threshold sleep intervention consisting of either (i) sleep data feedback plus sleep education or (ii) sleep data feedback alone in comparison with (iii) no intervention.Material and methodsA total of 100 employees of the University of Salzburg (age: 39.51 ± 11.43 years, range: 22–62 years) were randomly assigned to one of the three groups. During the 2-week study period, objective sleep parameters were assessed via actigraphy. In addition, an online questionnaire and a daily digital diary were used to record subjective sleep parameters, work-related factors, as well as mood and well-being. After 1 week, a personal appointment was conducted with participants of both experimental group 1 (EG1) and experimental group 2 (EG2). While the EG2 only received feedback about their sleep data from week 1, the EG1 additionally received a 45-min sleep education intervention containing sleep hygiene rules and recommendations regarding stimulus control. A waiting-list control group (CG) did not receive any feedback until the end of the study.ResultsResults indicate positive effects on sleep and well-being following sleep monitoring over the course of 2 weeks and minimal intervention with a single in-person appointment including sleep data feedback. Improvements are seen in sleep quality, mood, vitality, and actigraphy-measured sleep efficiency (SE; EG1), as well as in well-being and sleep onset latency (SOL) in EG2. The inactive CG did not improve in any parameter.ConclusionResults suggest small and beneficial effects on sleep and well-being in people being continuously monitored and receiving (actigraphy-based) sleep feedback when paired with a single-time personal intervention.