Hasil untuk "Neurology. Diseases of the nervous system"

M. Ellul, L. Benjamin, Bhagteshwar Singh et al.

Background The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is of a scale not seen since the 1918 influenza pandemic. Although the predominant clinical presentation is with respiratory disease, neurological manifestations are being recognised increasingly. On the basis of knowledge of other coronaviruses, especially those that caused the severe acute respiratory syndrome and Middle East respiratory syndrome epidemics, cases of CNS and peripheral nervous system disease caused by SARS-CoV-2 might be expected to be rare. Recent developments A growing number of case reports and series describe a wide array of neurological manifestations in 901 patients, but many have insufficient detail, reflecting the challenge of studying such patients. Encephalopathy has been reported for 93 patients in total, including 16 (7%) of 214 hospitalised patients with COVID-19 in Wuhan, China, and 40 (69%) of 58 patients in intensive care with COVID-19 in France. Encephalitis has been described in eight patients to date, and Guillain-Barré syndrome in 19 patients. SARS-CoV-2 has been detected in the CSF of some patients. Anosmia and ageusia are common, and can occur in the absence of other clinical features. Unexpectedly, acute cerebrovascular disease is also emerging as an important complication, with cohort studies reporting stroke in 2–6% of patients hospitalised with COVID-19. So far, 96 patients with stroke have been described, who frequently had vascular events in the context of a pro-inflammatory hypercoagulable state with elevated C-reactive protein, D-dimer, and ferritin. Where next? Careful clinical, diagnostic, and epidemiological studies are needed to help define the manifestations and burden of neurological disease caused by SARS-CoV-2. Precise case definitions must be used to distinguish non-specific complications of severe disease (eg, hypoxic encephalopathy and critical care neuropathy) from those caused directly or indirectly by the virus, including infectious, para-infectious, and post-infectious encephalitis, hypercoagulable states leading to stroke, and acute neuropathies such as Guillain-Barré syndrome. Recognition of neurological disease associated with SARS-CoV-2 in patients whose respiratory infection is mild or asymptomatic might prove challenging, especially if the primary COVID-19 illness occurred weeks earlier. The proportion of infections leading to neurological disease will probably remain small. However, these patients might be left with severe neurological sequelae. With so many people infected, the overall number of neurological patients, and their associated health burden and social and economic costs might be large. Health-care planners and policy makers must prepare for this eventuality, while the many ongoing studies investigating neurological associations increase our knowledge base.

Yan-Chao Li, W. Bai, T. Hashikawa

Following the severe acute respiratory syndrome coronavirus (SARS‐CoV) and Middle East respiratory syndrome coronavirus (MERS‐CoV), another highly pathogenic coronavirus named SARS‐CoV‐2 (previously known as 2019‐nCoV) emerged in December 2019 in Wuhan, China, and rapidly spreads around the world. This virus shares highly homological sequence with SARS‐CoV, and causes acute, highly lethal pneumonia coronavirus disease 2019 (COVID‐19) with clinical symptoms similar to those reported for SARS‐CoV and MERS‐CoV. The most characteristic symptom of patients with COVID‐19 is respiratory distress, and most of the patients admitted to the intensive care could not breathe spontaneously. Additionally, some patients with COVID‐19 also showed neurologic signs, such as headache, nausea, and vomiting. Increasing evidence shows that coronaviruses are not always confined to the respiratory tract and that they may also invade the central nervous system inducing neurological diseases. The infection of SARS‐CoV has been reported in the brains from both patients and experimental animals, where the brainstem was heavily infected. Furthermore, some coronaviruses have been demonstrated able to spread via a synapse‐connected route to the medullary cardiorespiratory center from the mechanoreceptors and chemoreceptors in the lung and lower respiratory airways. Considering the high similarity between SARS‐CoV and SARS‐CoV2, it remains to make clear whether the potential invasion of SARS‐CoV2 is partially responsible for the acute respiratory failure of patients with COVID‐19. Awareness of this may have a guiding significance for the prevention and treatment of the SARS‐CoV‐2‐induced respiratory failure.

M. Sanders

Zaldy Pagaduan, Jason Occidental, Nathaniel Duro et al.

Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise. In the Philippines, cacao farmers have limited access to data, information, and good agricultural practices. This study addresses these issues by developing a mobile application for cacao disease identification and management that functions offline, enabling use in remote areas where farms are mostly located. The core of the system is a deep learning model trained to identify cacao diseases accurately. The trained model is integrated into the mobile app to support farmers in field diagnosis. The disease identification model achieved a validation accuracy of 96.93% while the model for detecting cacao black pod infection levels achieved 79.49% validation accuracy. Field testing of the application showed an agreement rate of 84.2% compared with expert cacao technician assessments. This approach empowers smallholder farmers by providing accessible, technology-enabled tools to improve cacao crop health and productivity.

Sazzad Hossain, Saiful Islam, Muhammad Ibrahim et al.

Skin diseases are a major public health concern worldwide, and their detection is often challenging without access to dermatological expertise. In countries like Bangladesh, which is highly populated, the number of qualified skin specialists and diagnostic instruments is insufficient to meet the demand. Due to the lack of proper detection and treatment of skin diseases, that may lead to severe health consequences including death. Common properties of skin diseases are, changing the color, texture, and pattern of skin and in this era of artificial intelligence and machine learning, we are able to detect skin diseases by using image processing and computer vision techniques. In response to this challenge, we develop a publicly available dataset focused on common skin disease detection using machine learning techniques. We focus on five prevalent skin diseases in Bangladesh: Contact Dermatitis, Vitiligo, Eczema, Scabies, and Tinea Ringworm. The dataset consists of 1612 images (of which, 250 are distinct while others are augmented), collected directly from patients at the outpatient department of Faridpur Medical College, Faridpur, Bangladesh. The data comprises of 302, 381, 301, 316, and 312 images of Dermatitis, Eczema, Scabies, Tinea Ringworm, and Vitiligo, respectively. Although the data are collected regionally, the selected diseases are common across many countries especially in South Asia, making the dataset potentially valuable for global applications in machine learning-based dermatology. We also apply several machine learning and deep learning models on the dataset and report classification performance. We expect that this research would garner attention from machine learning and deep learning researchers and practitioners working in the field of automated disease diagnosis.

Yingli Gu, Kijung Sung, Chengbiao Wu

Hangeul Park, Woojin Kim, Jungbo Sim et al.

Objective Motor-evoked potential (MEP) loss during intramedullary (IM) spinal cord tumor surgery impairs the ability to monitor further neural injury. Direct wave (D-wave) monitoring may allow continued assessment of corticospinal tract integrity after MEP loss. This study evaluates the role of D-wave-guided surgery in preserving function and enabling safe resection after MEP loss. Methods A retrospective study was conducted in adult patients with ependymoma (EPN), cavernous angioma (CA) or subependymoma who experienced MEP loss during IM tumor resection between January 2012 and May 2025. Patients who underwent continued resection under D-wave guidance after MEP loss were compared with those who did not. Results Among 37 eligible patients, 9 underwent D-wave-guided surgery and 28 did not. Functional improvement at the last follow-up was more frequent in the D-wave-guided surgery group (66.7% vs. 17.9%, p=0.011). This trend remained significant in EPN patients (74.4% vs. 9.1%, p=0.003), but not in CA patients. Immediate postoperative motor grade ≤3 was more common in the D-wave-guided surgery group (66.7% vs. 39.3%), although this difference was not statistically significant (p=0.251). By last follow-up, the proportions of patients self-ambulatory without external aids (88.9% vs. 89.3%, p=1.000) were similar between groups. Extent of resection, complications, and recurrence rates showed no significant differences. Conclusion D-wave-guided surgery may enable safe continuation of tumor resection after MEP loss without increasing morbidity. It offers a viable intraoperative strategy to preserve long-term motor function by extending monitoring beyond MEP limitations.

Jessica Schlünzen, Patricia Kulla, Joachim Kruse

Some studies suggest Imagery Rescripting (ImRs) may be more effective than Imaginal Exposure (IE) for processing trauma when non-fear emotions are predominant. ImRs has been proposed to work through positive memory revaluation and increasing mastery, while IE focuses on fear toleration through inhibitory learning. We present standardized ImRs and IE analogues to explore their impact on non-fear emotions and underlying mechanisms. Forty-one participants selected an autobiographic event and were randomly assigned to ImRs, IE, or a control condition. Core elements of ImRs and IE were delivered via audio. We repeatedly assessed event-related emotions, distress, mastery, and tolerance for negative emotions. Participants in ImRs showed greater reductions in distress, fear, and anger, but not in self-conscious emotions, compared to IE. Unexpectedly, IE participants experienced increased negative emotions, including fear, with no specific advantage for increasing tolerance. Reductions in negative emotions were also observed in the control group. We found tentative indications for positive revaluation following ImRs. In conclusion, the ImRs analogue largely facilitated expected changes, while IE led to adverse effects. We discuss potential reasons for these findings and suggest improvements for the analogues and overall procedure.

Katelyn E. Mooney, Derek B. Archer, Aditi Sathe et al.

Abstract INTRODUCTION We assessed associations between apolipoprotein E Translocase of Outer Mitochondrial Membrane 40 (APOE‐TOMM40)‐‘523 haplotypes and white matter microstructure (WMM) across limbic tracts important for memory and cognition in non‐Hispanic Black and White individuals. METHODS Linear regression models, stratified by APOE and racialized groups, assessed associations between TOMM40‐‘523‐S and limbic tract WMM free‐water (FW) and free‐water‐corrected fractional anisotropy (FAFWcorr). RESULTS Black‐ε4+‐one‐'523‐S carriers had lower FW in the cingulum and inferior longitudinal fasciculus compared to Black‐ε4+‐no‐'523‐S carriers. Additionally, Black‐ε4+‐one‐'523‐S carriers had lower FW in the cingulum, uncinate, and fornix, and higher FAFWcorr in the uncinate compared to Black‐ε4+‐'523‐S/S carriers. White‐ε3/ε3‐‘523‐S/S carriers had lower FAFWcorr in the cingulum and inferior temporal gyrus compared to White‐ε3/ε3‐no‐'523‐S carriers, and lower FAFWcorr in the cingulum compared to White‐ε3/ε3‐one‐‘523‐S carriers. DISCUSSION This supports prior work that ‘523‐S is associated with abnormal aging in White‐ε3/ε3 carriers, but is potentially risk‐mitigating in Black‐ε4+ carriers, while suggesting a differential effect by racialized background of APOE on WMM. Highlights White matter microstructure (WMM) across limbic tracts important for cognition was measured by diffusion MRI. Black apolipoprotein E (APOE) ε4+ carriers with one copy of TOMM40‐‘523‐S had normal aging WMM metrics across several tracts, including the cingulum bundle, uncinate fasciculus, fornix, and inferior longitudinal fasciculus. White APOE ε3/ε3 carriers with two copies of TOMM40‐‘523‐S had abnormal aging WMM metrics in the cingulum bundle and inferior temporal gyrus. APOE associations with aging may differ in racialized groups due to TOMM40‐‘523‐S copy number.

Yueheng Jiang, Peng Zhang

Multi-disease diagnosis using multi-modal data like electronic health records and medical imaging is a critical clinical task. Although existing deep learning methods have achieved initial success in this area, a significant gap persists for their real-world application. This gap arises because they often overlook unavoidable practical challenges, such as modality missingness, noise, temporal asynchrony, and evidentiary inconsistency across modalities for different diseases. To overcome these limitations, we propose HGDC-Fuse, a novel framework that constructs a patient-centric multi-modal heterogeneous graph to robustly integrate asynchronous and incomplete multi-modal data. Moreover, we design a heterogeneous graph learning module to aggregate multi-source information, featuring a disease correlation-guided attention layer that resolves the modal inconsistency issue by learning disease-specific modality weights based on disease correlations. On the large-scale MIMIC-IV and MIMIC-CXR datasets, HGDC-Fuse significantly outperforms state-of-the-art methods.

Vanessa Kellermann, Ece Sengun Filiz, Olena Said et al.

Abstract Background The OPEN feasibility trial testing olanzapine in anorexia nervosa (AN) in young people (YP) was not successful due to poor recruitment. This study aims to understand clinicians’ views and experiences of using olanzapine in AN and the challenges in implementing the trial in National Health Service (NHS) clinical settings. Methods We conducted qualitative interviews with eating disorders (ED) clinicians involved with the study (n = 11). Framework analysis was applied to qualitative data to identify barriers and facilitators to recruitment and study implementation. A web-based semi-structured Qualtrics survey was administered to ED clinicians (n = 24). Findings from the survey were used to corroborate and expand on the information derived from qualitative interviews. Results Qualitative analysis identified four main themes: (1) Acknowledging Service User (SU) / Family Concerns, (2) Prioritising person-centred care, (3) Limited Service Capacity and (4) Study eligibility criteria. Subthemes are outlined accordingly. Clinicians appeared confident addressing SU concerns around olanzapine in clinical discussions, but timing was critical, and olanzapine was considered one aspect of treatment that needed to align with their holistic approach. Service pressures restricted opportunities for recruitment and the ability to offer regular review. At the same time, some YP were ineligible for the trial, as they were already taking olanzapine, or needed to be prescribed it more promptly than the study procedures allowed. Survey findings underlined confidence in prescribing and informing on olanzapine, the various possible benefits of olanzapine besides weight gain, and the importance of therapeutic alliances and informed consent. Both data sets highlight the need for further evidence on long-term safety, side effects and efficacy of olanzapine use for AN. Where clinical service capacity is at a premium, research implementation is not prioritised, particularly in intensive clinical settings. Conclusions Findings provide first-hand insight into individual and systemic challenges with research implementation in the NHS, which need to be considered when designing future clinical research studies. We emphasise a person-centred approach when discussing olanzapine to consider a holistic recovery from AN beyond weight-gain as an isolated outcome for improvement.

Abhay Matkar

Andrew Maris, Cristina Rea, Alessandro Pau et al.

The "density limit" is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot plant concepts must operate near or above the Greenwald limit to achieve their objectives. Evidence has also grown that the Greenwald scaling - in its remarkable simplicity - may not capture the full complexity of the density limit. In this study, we assemble a multi-machine database to quantify the effectiveness of the Greenwald limit as a predictor of the L-mode density limit and compare it with data-driven approaches. We find that a boundary in the plasma edge involving dimensionless collisionality and pressure, $ν_{*\rm, edge}^{\rm limit} = 3.5 β_{T,{\rm edge}}^{-0.40}$, achieves significantly higher accuracy (false positive rate of 2.3% at a true positive rate of 95%) of predicting density limit disruptions than the Greenwald limit (false positive rate of 13.4% at a true positive rate of 95%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. This boundary can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.

E. Benveniste

F. Ghrissi, F. Fekih-romdhane, M. Stambouli et al.

Introduction Violence is a common behavioral and health concern among adolescents, aged 12 to 18 years old. In fact, aggressiveness may result in severe outcome in a critical age characterised by biological, psychological, and social changes. Schizophrenia is a severe and chronic condition, with elevated level of aggressiveness. Since unaffected biological relatives of schizophrenia patients share similar though less severe neurocognitive and behavioral abnormalities seen in their affected relatives, they are at increased risk of violence mainly during adolescence. However, studies including adolescents with schizophrenia first degree history are scarce. Objectives The aim of this survey was to evaluate the aggressiveness among unaffected adolescents with fist degree family history of schizophrenia and in a control group of adolescents with no family psychiatric history. Methods In this purpose wo conducted a case-control cross sectional study in Razi hospital during three months: from July to September 2022. Unaffected adolescents aged 12 to 18 whom first-degree relatives were diagnosed with schizophrenia according to DSM-5 criteria were included. Adolescents with psychiatric conditions or medical affections associated with psychiatric presentation were not included. Control group was selected amongst the population. Sociodemographic data were collected on a preestablished questionnaire and the following scales were used: The Life History of Aggression LHA, an 11 items self-reported tool, in the Arabic version, The Aggression Questionnaire AQ which is a 29 items self-reported scale in Arabic version. Written informed consent was obtained from the legal tutor of each adolescent. Results Results of this survey are ongoing. Conclusions Results of this survey are ongoing. Disclosure of Interest None Declared

William J. Bosl, William J. Bosl, William J. Bosl et al.

IntroductionAnxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety.MethodsWe used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls.ResultsWe found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders.ConclusionThis study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.

Xiaofan Zhou

The target of Electronic Health Record (EHR) coding is to find the diagnostic codes according to the EHRs. In previous research, researchers have preferred to do multi-classification on the EHR coding task; most of them encode the EHR first and then process it to get the probability of each code based on the EHR representation. However, the question of complicating diseases is neglected among all these methods. In this paper, we propose a novel EHR coding framework, which is the first attempt at detecting complicating diseases, called ComplicaCode. This method refers to the idea of adversarial learning; a Path Generator and a Path Discriminator are designed to more efficiently finish the task of EHR coding. We propose a copy module to detect complicating diseases; by the proposed copy module and the adversarial learning strategy, we identify complicating diseases efficiently. Extensive experiments show that our method achieves a 57.30\% ratio of complicating diseases in predictions, and achieves the state-of-the-art performance among cnn-based baselines, it also surpasses transformer methods in the complication detection task, demonstrating the effectiveness of our proposed model. According to the ablation study, the proposed copy mechanism plays a crucial role in detecting complicating diseases.

Maria Frasca, Davide La Torre, Gabriella Pravettoni et al.

Parkinson's disease is a neurological condition that occurs in nearly 1% of the world's population. The disease is manifested by a drop in dopamine production, symptoms are cognitive and behavioural and include a wide range of personality changes, depressive disorders, memory problems, and emotional dysregulation, which can occur as the disease progresses. Early diagnosis and accurate staging of the disease are essential to apply the appropriate therapeutic approaches to slow cognitive and motor decline. Currently, there is not a single blood test or biomarker available to diagnose Parkinson's disease. Magnetic resonance imaging has been used for the past three decades to diagnose and distinguish between PD and other neurological conditions. However, in recent years new possibilities have arisen: several AI algorithms have been developed to increase the precision and accuracy of differential diagnosis of PD at an early stage. To our knowledge, no AI tools have been designed to identify the stage of progression. This paper aims to fill this gap. Using the "Parkinson's Progression Markers Initiative" dataset, which reports the patient's MRI and an indication of the disease stage, we developed a model to identify the level of progression. The images and the associated scores were used for training and assessing different deep-learning models. Our analysis distinguished four distinct disease progression levels based on a standard scale (Hoehn and Yah scale). The final architecture consists of the cascading of a 3DCNN network, adopted to reduce and extract the spatial characteristics of the RMI for efficient training of the successive LSTM layers, aiming at modelling the temporal dependencies among the data. Our results show that the proposed 3DCNN + LSTM model achieves state-of-the-art results by classifying the elements with 91.90\% as macro averaged OVR AUC on four classes

Hyukje Lee, Sang-Won Yoo, Joong-Seok Kim