Hasil untuk "Neurology. Diseases of the nervous system"

Eun Bit Bae, Jang Wook Sohn, Jang Wook Sohn et al.

The negative impact of the coronavirus disease 2019 (COVID-19) pandemic on mental health, including that of movement restrictions that unintentionally contributed to its deterioration, has been widely reported. However, the effects of isolation and related factors remain unclear. To explore the physiological, psychological, and lifestyle factors that affected stress levels in individuals with confirmed COVID-19 undergoing isolation, we used a modified version of a commercially available wearable device for the purpose of real-time monitoring. The study included 60 infection patients affected by infectious diseases (30 with confirmed COVID-19 undergoing isolation at home, and 30 inpatients at our institution with other infectious diseases). Based on the data distribution, we conducted correlation analyses within each group and evaluated the relationship between variables using conservative methods, general linear regression, and linear mixed models. The groups comparison was evaluated using an independent-samples t-test. Stress scores in the study population showed significant associations with psychological and lifestyle factors, but not with psychiatric scale scores. According to the linear model, caffeine consumption affected the root mean square of successive differences (RMSSD) (p = 0.031). In participants with confirmed COVID-19 undergoing isolation at home, alcohol consumption and anxiety levels showed strong correlations with RMSSD (p< 0.005), although this was not evident in linear models. Stress scores were significantly higher in participants with COVID-19, whereas RMSSD deviation from the mean of an age-matched Korean cohort was significantly lower than that in patients with other infectious diseases. This study suggests that while perceived stress may influence parasympathetic function in all patients with infectious diseases, this effect may be particularly pronounced in those with COVID-19 undergoing isolation. These individuals are more likely to experience stress and anxiety, and their parasympathetic function may be compromised (reflected in a reduction of heart rate variability). Our results suggests that lifestyle factors and perceived stress influences parasympathetic function in under stressful conditions associated with confinement, and that these factors should be considered in the management of individuals with COVID-19 in isolation.

Silje Mørup Ormhaug, Tine K. Jensen, Kate Louise Porcheret et al.

Background: Childhood trauma is a public health challenge, and there is currently a service/needs gap. Low-intensive treatments where the task of leading the treatment is partially shifted to a caregiver can help bridge this gap by freeing therapist resources. Stepping Together for Children after Trauma (ST-CT), is a novel parent-led, therapist-assisted treatment with promising results in the U.S. Also, it has shown to be feasible as a first-line intervention in Norwegian municipal services and ST-CT is well accepted by children, caregivers, and therapists.Objective: This study is the first randomized controlled trial of ST-CT by independent researchers. The study consists of three work-packages: WP1: Investigates (a) the effectiveness of ST-CT compared to therapy as usual (TAU) in reducing post-traumatic stress symptoms (PTSS), related mental health symptoms, sleep problems and increasing daily functioning; (b) the cost-effectiveness of ST-CT compared to TAU in terms of quality adjusted life years; (c) future health care utilization. WP2: Looks at change processes in ST-CT, with changes in sleep, post-traumatic cognitions and child–parent relationship as potential mediators of reductions in child PTSS. WP3: Investigates keys to implementation into regular first-line mental health services.Methods: The study has a type 1 hybrid effectiveness-implementation design. Children between 7-12 years of age with symptoms of PTSS and their caregivers are randomized to either ST-CT or TAU and participants are assessed at five time points during the first year. Sleep quality is measured with a non-contact radar 1 week pre- and post-treatment and registry data of use of prescribed medications and mental health service utilization will be collected 3 years post-inclusion. The study aims to include 160 child–parent dyads from 30 municipalities.Conclusion: Results can help gain a better understanding of the effectiveness and change processes of ST-CT and may provide important knowledge for a future dissemination and implementation.Trial registration: ClinicalTrials.gov identifier: NCT05734547.

Emma Fargher, Marcus Keatinge, Oluwaseyi Pearce et al.

Abstract Single nucleotide polymorphisms adjacent to the α-amino-β-carboxymuconate-ε-semialdehyde decarboxylase (ACMSD) gene have been associated with Parkinson’s disease (PD) in genome-wide association studies (GWAS). However, its biological validation as a PD risk gene has been hampered by the lack of available models. Using CRISPR/Cas9, we generated a zebrafish model of acmsd deficiency with marked increase in quinolinic acid. Despite this, acmsd -/- zebrafish were viable, fertile, morphologically normal and demonstrated no abnormalities in spontaneous movement. In contrast to the postulated pro-immune pathomechanism linking ACMSD to PD, microglial cells and expression of the proinflammatory cytokines cxcl8, il-1β, and mmp9 were similar between acmsd -/- and controls. The number of ascending dopaminergic neurons, and their susceptibility to MPP+, was also indistinguishable. An upregulation of kynurenine aminotransferase activity was identified in acmsd -/- zebrafish which may explain the absence of neurodegenerative phenotypes. Our study highlights the importance of biological validation for putative GWAS hits in suitable model systems.

Plück Julia, Nawab Laurence, Kamenetzka Elena et al.

Based on Achenbach's school-age questionnaires, research groups have investigated supplementary scales for stress problems, obsessive-compulsive problems, sluggish cognitive tempo, positive qualities, dysregulation, autism spectrum disorders, and mania in 6–18-year-olds partly only in some of the three perspectives the Achenbach System of Empirically Based Assessment (ASEBA) provides.

Yidong Chai, Haoxin Liu, Jiaheng Xie et al.

Wearable sensor technologies and deep learning are transforming healthcare management. Yet, most health sensing studies focus narrowly on physical chronic diseases. This overlooks the critical need for joint assessment of comorbid physical chronic diseases and depression, which is essential for collaborative chronic care. We conceptualize multi-disease assessment, including both physical diseases and depression, as a multi-task learning (MTL) problem, where each disease assessment is modeled as a task. This joint formulation leverages inter-disease relationships to improve accuracy, but it also introduces the challenge of double heterogeneity: chronic diseases differ in their manifestation (disease heterogeneity), and patients with the same disease show varied patterns (patient heterogeneity). To address these issues, we first adopt existing techniques and propose a base method. Given the limitations of the base method, we further propose an Advanced Double Heterogeneity-based Multi-Task Learning (ADH-MTL) method that improves the base method through three innovations: (1) group-level modeling to support new patient predictions, (2) a decomposition strategy to reduce model complexity, and (3) a Bayesian network that explicitly captures dependencies while balancing similarities and differences across model components. Empirical evaluations on real-world wearable sensor data demonstrate that ADH-MTL significantly outperforms existing baselines, and each of its innovations is shown to be effective. This study contributes to health information systems by offering a computational solution for integrated physical and mental healthcare and provides design principles for advancing collaborative chronic disease management across the pre-treatment, treatment, and post-treatment phases.

Changgang Wang, Wei Liu, Yu Cao et al.

In the context of the rising share of new energy generation, accurately generating new energy output scenarios is crucial for day-ahead power system scheduling. Deep learning-based scenario generation methods can address this need, but their black-box nature raises concerns about interpretability. To tackle this issue, this paper introduces a method for day-ahead new energy scenario generation based on an improved conditional generative diffusion model. This method is built on the theoretical framework of Markov chains and variational inference. It first transforms historical data into pure noise through a diffusion process, then uses conditional information to guide the denoising process, ultimately generating scenarios that satisfy the conditional distribution. Additionally, the noise table is improved to a cosine form, enhancing the quality of the generated scenarios. When applied to actual wind and solar output data, the results demonstrate that this method effectively generates new energy output scenarios with good adaptability.

Congqi Liu, Congqi Liu, Congqi Liu et al.

BackgroundMental health challenges are encountered by frail older adults as the population ages. The extant literature is scant regarding the correlation between depressive symptoms and social participation among frail older adults.MethodsThis study is based on an analysis of data from China Health and Retirement Longitudinal Study (CHARLS) participants aged 60 and older who are frail. A frailty index (FI) was developed for the purpose of assessing the frailty level of the participants. Additionally, latent class analysis (LCA) was employed to classify the participants’ social engagement patterns in 2015 and 2018. The study used ordered logistic regression to examine the relationship between social participation type and depressive symptoms. We also used Latent Transition Analysis (LTA) methods to explore the impact of changes in social activity types on depressive symptoms after three years of follow-up in 2018. In addition, the response surface analysis (RSM) investigation explored the relationship among FI, depression, and social participation.ResultsA total of 4,384 participants completed the baseline survey; three years later, 3,483 were included in the follow-up cohort. The baseline survey indicates that female older adults in rural areas who are single, have lower incomes, shorter sleep durations, and lighter weights exhibited more severe depressive symptoms. Social participation patterns were categorized into five subgroups by LCA. The findings indicate that individuals classified as “board game enthusiasts” (OR, 0.62; 95% CI, 0.47-0.82) and those as “extensive social interaction” (OR,0.67; 95% CI, 0.49-0.90) have a significantly lower likelihood of developing depressive symptoms compared to the “socially isolated” group. We also discovered that “socially isolated” baseline participants who transitioned to the “helpful individual” group after three years had significantly greater depressed symptoms (OR, 1.56; 95% CI, 1.00-2.44). More social activity types and less FI are linked to lower depression in our study.ConclusionThe results of the study emphasize the importance of social participation patterns and the number of social participation types in relation to the severity of depression among frail older adults individuals. This study’s findings may provide important insights for addressing depressive symptoms in frail older adults person.

Rachael J. Murphy, Subin Paul, Ralph Primelo

Primary hyperparathyroidism (PHPT) and subsequent hypercalcemia have been reported to be associated with psychosis. Here we report the case of a 28-year-old cannabis using male with his first contact with psychiatric care because of mood instability, bizarre behavior, and poor ability to carry out activities of daily living. Hypercalcemia was identified, and a subsequent endocrine workup confirmed PHPT. After parathyroidectomy, there was no longer any need for antipsychotic or other psychotropic medications; the report emphasizes the importance of considering organic causes, such as hyperparathyroidism, in patients presenting with psychotic-like symptoms, including in the setting of substance use disorder. Prompt recognition and appropriate management of the underlying condition are crucial for optimizing patient outcomes.

Kang-Min Choi, Kang-Min Choi, Taegyeong Lee et al.

IntroductionRecent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions.MethodsThirty-one drug-naïve patients with MDD (aged 46.61 ± 10.05, female 28) and twenty-one healthy controls (aged 43.86 ± 14.14, female 19) participated in the study. After 8 weeks of pharmacological treatment, the patients were divided into non-remitted MDD (nrMDD, n = 14) and remitted-MDD (rMDD, n = 17) groups. EEG data under three conditions (resting-state, standard, and deviant) were analyzed. The SN was constructed with three cortical regions as nodes and weighted phase-lag index as edges, across alpha, low-beta, high-beta, and gamma bands. A repeated measures analysis of the variance model was used to examine the group-by-condition interaction. Machine learning-based classification analyses were also conducted between the nrMDD and rMDD groups.ResultsA notable group-by-condition interaction was observed in the high-beta band between nrMDD and rMDD. Specifically, patients with nrMDD exhibited hypoconnectivity between the dorsal anterior cingulate cortex and right insula (p = 0.030). The classification analysis yielded a maximum classification accuracy of 80.65%.ConclusionOur study suggests that abnormal condition-dependent changes in the SN could serve as potential predictors of pharmacological treatment efficacy in patients with MDD.

Shasha Li, Shasha Li, Yuting Gao et al.

Patients with neuromyelitis optica spectrum disorder (NMOSD) coexisting with both Sjögren’s syndrome (SS) and pancytopenia are exceptionally rare. There is no study on the treatment of such patients. We presented a case of AQP4-IgG seropositive refractory NMOSD patient combined with SS and pancytopenia with significant response to inebilizumab. In 2017 the 49-year-old female patient was diagnosed with SS and pancytopenia without any treatment. In August 2022, she had a sudden onset of lower limbs weakness, manifested as inability to walk, accompanied by urinary incontinence. After receiving methylprednisolone and cyclophosphamide, she regained the ability to walk. In February 2023, she suffered from weakness of both lower limbs again and paralyzed in bed, accompanied by retention of urine and stool, and loss of vision in both eyes. After receiving methylprednisolone and three plasmapheresis, the condition did not further worsen, but there was no remission. In March 2023, the patient was admitted to our hospital and was formally diagnosed with AQP4-IgG seropositive NMOSD combined with SS and pancytopenia. After receiving two 300 mg injections of inebilizumab, not only the symptoms of NMOSD improved significantly, but also the symptoms of concurrent SS and pancytopenia. In the cases of AQP4-IgG seropositive NMOSD who have recurrent episodes and are comorbid with other autoimmune disorders, inebilizumab may be a good choice.

Alex K Miller, MD, Matthew R Cederman, BS, Daniel K Park, MD

ABSTRACT: Background: There is growing interest in transitioning various surgical procedures to the outpatient care setting. However, for Medicare patients, the site of service for surgical procedures is influenced by regulations within the Inpatient and Outpatient Prospective Payment Systems. The purpose of this study is to quantify changes in utilization of outpatient spine surgery within the Medicare population, as well as to determine changes in outpatient volume after removal of a procedure from the “inpatient-only” list. Methods: This is a cross-sectional study of Medicare billing database information for selected spine procedures included in the Medicare Physician/Supplier Procedure Summary (PSPS) public use files from 2010–2021. These files include aggregated data from Medicare Part B fee-for-service claims, published yearly. Procedures from Healthcare Common Procedural Coding System (HCPCS) code ranges 22010–22899 and 62380–63103 were selected for analysis, limited to surgical services delivered in the inpatient, hospital outpatient department (HOPD), and ambulatory surgical center (ASC) settings. For each HCPCS code included, estimates of the total number of services and corresponding changes in volume were calculated. Results: Within the range of codes included in the study, the total number of outpatient spine procedures rose approximately 193% from 2010 to 2021, with compound annual growth rate (CAGR) for outpatient procedures per year of 9.9% for HOPDs and 15.7% for ASCs (-2.2% for inpatient procedures). Within this period, the ASC list grew from 12 procedures to 58 procedures. In 2021, the highest volume ASC procedure was HCPCS 63047, at approximately 4970 procedures. Conclusions: This study demonstrates a trend of increasing utilization of HOPDs and ASCs for spine procedures among Medicare beneficiaries from 2010 to 2021. Though HOPDs are currently more widely utilized, the ongoing additions of spine procedures to the ASC covered procedures list may shift this balance.

Pierluigi Colli, Gabriela Marinoschi, Elisabetta Rocca et al.

Partial differential equation (PDE) models for infectious disease have received renewed interest in recent years. Most models of this type extend classical compartmental formulations with additional terms accounting for spatial dynamics, with Fickian diffusion being the most common such term. However, while diffusion may be appropriate for modeling vector-borne diseases, or diseases among plants or wildlife, the spatial propagation of airborne diseases in human populations is heavily dependent on human contact and mobility patterns, which are not necessarily well-described by diffusion. By including an additional chemotaxis-inspired term, in which the infection is propagated along the positive gradient of the susceptible population (from regions of low- to high-density of susceptibles), one may provide a more suitable description of these dynamics. This article introduces and analyzes a mathematical model of infectious disease incorporating a modified chemotaxis-type term. The model is analyzed mathematically and the well-posedness of the resulting PDE system is demonstrated. A series of numerical simulations are provided, demonstrating the ability of the model to naturally capture important phenomena not easily observed in standard diffusion models, including propagation over long spatial distances over short time scales and the emergence of localized infection hotspots

Fabienne E. Andres, Tracey Thornborrow, Wienis N. Bowie et al.

Abstract Background Body dissatisfaction (BD) is a growing concern in Latin America; reliable and culturally appropriate scales are necessary to support body image research in Spanish speaking Latin American countries. We sought to validate a Latin-American Spanish version of the Body Esteem Scale for Adolescents and Adults (BESAA; Mendelson et al. 2001). Methods The BESAA was translated, culturally adapted, and validated in a sample of adults in Colombia (N = 525, 65% women, M age 24.4, SD = 9.28). We assessed factor structure (using confirmatory factor analysis (CFA), exploratory factor analysis (EFA) and exploratory structural equation model (ESEM)), internal reliability (using Cronbach’s alpha and omega), validity (using the Body Appreciation Scale BAS and Sociocultural Attitudes Towards Appearance Questionnaire SATAQ), test–retest stability in a small subsample (N = 84, using Intraclass correlations ICC) and measurement invariance across gender. To evaluate the generalizability of the scale, we assessed reliability, validity, and factor structure in a second sample from rural Nicaragua (N = 102, 73% women, M age 22.2, SD = 4.72), and assessed measurement invariance across Nicaraguan and Colombian participants. Results The scale showed good internal reliability and validity in both samples, and there was evidence of adequate test–retest stability in the Colombian sample. EFA showed a three-factor structure with subscales we labelled ‘appearance-positive’, ‘appearance-negative’ and ‘weight’, that was confirmed using CFA and ESEM in the Colombian sample. Measurement invariance was confirmed across the Colombian and Nicaraguan samples, and across gender within the Colombian sample. Conclusion The Latin-American Spanish version of the BESAA (BESAA-LA) appears to be a psychometrically sound measure with good reliability, validity and invariance across gender and countries. These results support the use of this scale to measure body satisfaction/dissatisfaction in Latin American adult populations.

Qing-Sheng Li, Yan-Jie Jia

Ferroptosis, a new non-necrotizing programmed cell death (PCD), is driven by iron-dependent phospholipid peroxidation. Ferroptosis plays a key role in secondary traumatic brain injury and secondary spinal cord injury and is closely related to inflammation, immunity, and chronic injuries. The inhibitors against ferroptosis effectively improve iron homeostasis, lipid metabolism, redox stabilization, neuronal remodeling, and functional recovery after trauma. In this review, we elaborate on the latest molecular mechanisms of ferroptosis, emphasize its role in secondary central nervous trauma, and update the medicines used to suppress ferroptosis following injuries.

Chenhao Li, Wei Dai, Shuai Miao et al.

It has yet to be determined whether medication overuse headache (MOH) is an independent disorder or a combination of primary headache and substance addiction. To further explore the causes of MOH, we compared MOH with substance use disorder (SUD) in terms of the brain regions involved to draw more targeted conclusions. In this review, we selected alcohol use disorder (AUD) as a representative SUD and compared MOH and AUD from two aspects of neuroimaging and basic research. We found that in neuroimaging studies, there were many overlaps between AUD and MOH in the reward circuit, but the extensive cerebral cortex damage in AUD was more serious than that in MOH. This difference was considered to reflect the sensitivity of the cortex structure to alcohol damage. In future research, we will focus on the central amygdala (CeA), prefrontal cortex (PFC), orbital-frontal cortex (OFC), hippocampus, and other brain regions for interventions, which may have unexpected benefits for addiction and headache symptoms in MOH patients.

Jai Vardhan, Kothapalli Sai Swetha

Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these diseases can significantly mitigate crop loss. In this study, we propose a deep learning-based approach for efficient detection of plant diseases using drone-captured imagery. A comprehensive database of various plant species, exhibiting numerous diseases, was compiled from the Internet and utilized as the training and test dataset. A Convolutional Neural Network (CNN), renowned for its performance in image classification tasks, was employed as our primary predictive model. The CNN model, trained on this rich dataset, demonstrated superior proficiency in crop disease categorization and detection, even under challenging imaging conditions. For field implementation, we deployed a prototype drone model equipped with a high-resolution camera for live monitoring of extensive agricultural fields. The captured images served as the input for our trained model, enabling real-time identification of healthy and diseased plants. Our approach promises an efficient and scalable solution for improving crop health monitoring systems.

Anna Daniel Fome, Wolfgang Bock, Axel Klar

In the world of epidemics, the mathematical modeling of disease co-infection is gaining importance due to its contributions to mathematics and public health. Because the co-infection may have a double burden on families, countries, and the universe, understanding its dynamics is paramount. We study a SEIQR (susceptible-exposed-infectious-quarantined-recovered) deterministic epidemic model with a single host population and multiple strains (-$c$ and -$i$) to account for two competitive diseases with quarantine effects. To model the role of quarantine and isolation efficacy in disease dynamics, we utilize a linear function. Further, we shed light on the standard endemic threshold and determine the conditions for extinction or coexistence with and without forming co-infection. Next, we show the dependence of the criticality based on specific parameters of the different pathogens. We found that the disease-free equilibrium (DFE) of the single-strain model always exists and is globally asymptotically stable (GAS) if $\tilde{\mathcal{R}}_k^q\leq 1$, else, a stable endemic equilibrium. On top of that, the model has forward bifurcation at $\tilde{\mathcal{R}}_k^q = 1$. In the case of a two-strain model, the strain with a large reproduction number outcompetes the one with a smaller reproduction number. Further, if the co-infected quarantine reproduction number is less than one, the infections of already infected individuals will die out, and co-infection will persist in the population otherwise. We note that the quarantine and isolation of exposed and infected individuals will reduce the number of secondary cases below one, consequently reducing the disease complications if the total number of people in the quarantine is at most the critical value.

Mutong Liu, Yang Liu, Jiming Liu

Infectious diseases, either emerging or long-lasting, place numerous people at risk and bring heavy public health burdens worldwide. In the process against infectious diseases, predicting the epidemic risk by modeling the disease transmission plays an essential role in assisting with preventing and controlling disease transmission in a more effective way. In this paper, we systematically describe how machine learning can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious disease risks. First, we introduce the background and motivation of using machine learning for infectious disease risk prediction. Next, we describe the development and components of various machine learning models for infectious disease risk prediction. Specifically, existing models fall into three categories: Statistical prediction, data-driven machine learning, and epidemiology-inspired machine learning. Subsequently, we discuss challenges encountered when dealing with model inputs, designing task-oriented objectives, and conducting performance evaluation. Finally, we conclude with a discussion of open questions and future directions.

Asim Khan, Umair Nawaz, Lochan Kshetrimayum et al.

Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields.

Xiaozhao Liu, Mianxin Liu, Lang Mei et al.

To characterize atypical brain dynamics under diseases, prevalent studies investigate functional magnetic resonance imaging (fMRI). However, most of the existing analyses compress rich spatial-temporal information as the brain functional networks (BFNs) and directly investigate the whole-brain network without neurological priors about functional subnetworks. We thus propose a novel graph learning framework to mine fMRI signals with topological priors from brain parcellation for disease diagnosis. Specifically, we 1) detect diagnosis-related temporal features using a "Transformer" for a higher-level BFN construction, and process it with a following graph convolutional network, and 2) apply an attention-based multiple instance learning strategy to emphasize the disease-affected subnetworks to further enhance the diagnosis performance and interpretability. Experiments demonstrate higher effectiveness of our method than compared methods in the diagnosis of early mild cognitive impairment. More importantly, our method is capable of localizing crucial brain subnetworks during the diagnosis, providing insights into the pathogenic source of mild cognitive impairment.