Browse Articles
Discover research articles across all indexed journals
Evaluation of normalized T1 signal intensity obtained using an automated segmentation model in lower leg MRI as a potential imaging biomarker in Charcot–Marie–Tooth disease type 1 A
Association between the central sensitization inventory score and health-related quality of life in community-dwelling middle-aged and older adults
Background Central sensitization is an important factor associated with impaired health-related quality of life in patients with musculoskeletal disorders and community-dwelling older adults. However, health-related quality-of-life domains strongly associated with central sensitization in the general population remain unclear. This study aimed to examine the association between the Central Sensitization Inventory Part A scores and health-related quality of life using community health checkup data. Methods A total of 419 middle-aged and older adults (mean age, 64.4 ± 11.2 years; 59.4% female) were included. Participants completed a questionnaire survey on pain, including visual analogue scales (VASs) for lower-back and knee pain, and the Central Sensitization Inventory Part A. Additionally, participants completed the Short-Form 36-Item Health Survey, and three component-summary scores and eight subscales were calculated. Additionally, participants completed the 5-level EuroQol 5 dimensions, and health-state utility values were calculated. The correlation between the Central Sensitization Inventory Part A scores and these health-related quality-of-life measures was investigated. Results Central Sensitization Inventory Part A score ≥40 was observed in 2.6% participants. Significant moderate negative correlations were observed between the Central Sensitization Inventory Part A scores and EuroQol 5 dimensions health-state utility values ( r = −0.631, P < 0.001), Short-Form 36 mental-component summary ( r = −0.550, P < 0.001), body pain ( r = −0.556, P < 0.001), general health ( r = −0.556, P < 0.001), vitality ( r = −0.610, P < 0.001), and mental health ( r = −0.556, P < 0.001). Similar results were obtained for participants with Central Sensitization Inventory Part A scores <30. Conclusions In community-dwelling middle-aged and older adults, Central Sensitization Inventory Part A scores were negatively correlated with health-related quality-of-life scores, even in participants with Central Sensitization Inventory Part A scores <30.
Synergistic effects of colistin-based combinations against colistin and carbapenem-resistant Klebsiella pneumoniae isolated from nosocomial bloodstream infections
Evaluating effectiveness of self-help groups in reduction of stigma in patients with neglected tropical diseases in Southern Nigeria: A cluster randomised study
Introduction Neglected tropical diseases (NTDs), notably leprosy and Buruli ulcer (BU), often lead to visible impairments and disabilities. Consequently, individuals affected by these conditions face stigmatization, discrimination, and mental health disorders. Stigma is particularly prevalent in leprosy, affecting self-esteem, social participation, self-efficacy, and overall quality of life. Various interventions have been developed to mitigate leprosy-related stigma, including individual and group counseling, peer support, economic empowerment initiatives, and establishment of self-help groups. However, rigorous evaluation of the various interventions through randomized controlled trials is lacking, especially within the Nigerian context, and none has included persons affected by BU. To address this gap, effectiveness of combined leprosy and BU self-help groups (SHGs) on stigma reduction was assessed as part of a larger randomized controlled study. Methodology/principal findings A cluster randomized study was conducted in Southern Nigeria from 2021 to 2023. Five local government areas (LGAs) were selected for each of the intervention and control arms. People affected by leprosy or BU were organized into SHGs in the intervention LGAs. Control LGAs were maintained on the existing standard of care, which did not include SHGs. Monthly peer-support meetings were held for 24 months following standardized guidelines after appropriate training of SHG members. The intervention targeted perceived and experienced stigma. Pre- and post-intervention stigma assessments were conducted using the stigma assessment and reduction of impact (SARI) scale in both intervention and control LGAs. A total of 635 persons affected by leprosy or BU were recruited—362 in the intervention group and 273 in the control group. At baseline, respondents in the intervention group had a significantly higher adjusted mean total score on the SARI Stigma Scale compared to the control group, with an adjusted mean difference of 10.75 (95% CI: 8.05–13.46, p < 0.000). However, post-intervention, the adjusted mean SARI Stigma Score significantly decreased in the intervention group compared to the control group, with an adjusted mean difference of 37.72 (95% CI: 36.01–39.43, p < 0.000). Conclusion/significance Peer-support and psychosocial support through SHGs significantly contributed to stigma reduction among persons affected by Leprosy/BU. SHGs can play a key role in stigma reduction interventions within NTD programs, contributing to the global leprosy target of achieving zero stigma and discrimination. Trial Registration: ISRCTN Registry: ISRCTN 83649248. https://trialsearch.who.int/Trial2.aspx? TrialID = ISRCTN83649248.
Periodic structures of solitons and shock wave solutions in the fractional nonlinear Shynaray-IIA equation via a generalized analytical method
Network reconfiguration and DG based compensation of Wolaita Sodo distribution system by using particle swarm optimisation
Improving voltage profiles and reducing power losses are critical challenges in modern distribution systems, especially in real, unbalanced and weakly interconnected grids. This study presents a novel application of simultaneous network reconfiguration and distributed generation (DG) placement using Particle Swarm Optimization (PSO) to the Wolaita Sodo distribution network in Ethiopia. Unlike most prior works conducted on IEEE standard feeders, this research uses actual network topology, load data, conductor parameters and operational constraints from a real Ethiopian distribution system. The optimization problem considers radiality constraints, DG size limits and voltage and current limits, with objectives of minimizing active/reactive power losses, improving voltage profiles and maximizing economic benefits. A backward-forward sweep load flow method evaluates network performance under four scenarios: base case, reconfiguration only, DG placement only and simultaneous implementation. Results show that the proposed method reduces active power losses by 72.064%, decreases voltage deviation from 24.63% to 4.5% and improves the minimum bus voltage from 0.7537 pu to 0.9550 pu. The annual financial savings of 16.3956 million ETB yield a payback period of six years. This grouping of real-network data, simultaneous optimization, and integrated techno-economic analysis offers a practical and replicable decision-support tool for utilities in emerging power systems.
Author Correction: Prophylactic TNF blockade uncouples efficacy and toxicity in dual CTLA-4 and PD-1 immunotherapy
A federated incremental blockchain framework with privacy preserving XAI optimization for securing healthcare data
Abstract Federated learning (FL) has become more popular in the area of machine learning for protecting data privacy, its unique distributed data processing characteristics have garnered widespread attention. However, the implementation of FL faces many challenges, it can be difficult has to decide on a compromise between model security, data privacy, and system efficiency, often requiring the give up of efficiency for privacy, traceability, interpretability, and security. In this paper, privacy-preserving federated incremental learning blockchain-optimized explainable artificial intelligence (PPFILB-OXAI) leveraging the benefits of Blockchain, Federated Incremental Learning (FIL), and explainable artificial intelligence (XAI) with optimization. Chaotic Bobcat Optimization Algorithm (CBOA) is introduced to XAI for selecting most important features from the dataset. The CBOA mimics the instinctive behaviors of wild bobcats, incorporating a chaotic operator to randomly generate the population during the selection phase. It is inspired by the bobcat’s hunting tactics, particularly the approach and pursuit of prey. Throughout the algorithm iterations, the most optimal feature solution is gradually identified. The FIL algorithm is capable of adapting to increasing resources in real-time without the need for retraining, all while extracting meaningful patterns from the collective client side data. Meanwhile, Blockchain technology makes it possible to handle medical data securely and transparently, and XAI improves the clarity and understanding of model decisions. To coordinate client privacy protection, PPFILB-OXAI integrates the blockchain process, FIL, and privacy approach. It then uses an aggregate to filter out aberrant models. Lastly, Entropy Deep Belief Network (EDBN) has shown the ability to classify and identify attacks. PPFBXAIO provides the best performance on a breast cancer wisconsin and heart disease in terms of precision, recall, f-measure, accuracy, loss, latency, and throughput. Heart disease, the precision, recall, f-measure, and accuracy of the suggested system are 94.87%, 96.73%, 95.79%, and 95.71%, respectively. The precision, recall, f-measure, and accuracy of the suggested method for breast cancer wisconsin are 97.13%, 97.70%, 97.41%, and 96.84%, respectively.
Correction: Consensus structure prediction of A. thaliana’s MCTP4 structure using prediction tools and coars grained simulations of transmembrane domain dynamics
DC-Mamba for surface micro defect classification on large aperture optics with multi axis attention
A multicenter, single-arm study using a modified faricimab treat-and-extend regimen in patients with macular edema due to central retinal vein occlusion: RVOSTAR study design protocol
Introduction Anti–vascular endothelial growth factor (anti-VEGF) agents are generally considered to be the first line of therapy for macular edema due to retinal vein occlusion (RVO-ME). However, current anti-VEGF treatment regimens in Japan are unable to maintain long-term vision improvement, particularly in patients with central RVO-ME (CRVO-ME). Faricimab is a dual angiopoietin-2/VEGF inhibitor approved for the treatment of RVO-ME in Japan. The RVOSTAR study is being conducted to evaluate the long-term maintenance of vision outcomes with faricimab using a modified treat-and-extend (T&E) regimen in treatment-naïve Japanese patients with CRVO-ME or hemi–RVO-ME (HRVO-ME) and to assess the factors affecting visual acuity and treatment intervals. Methods and design RVOSTAR (Japan Registry of Clinical Trials; jRCTs041250001) is an unmasked, single-arm, multicenter, prospective interventional study. Patients with CRVO-ME or HRVO-ME will 1) receive faricimab 6 mg every 4 weeks (≤6 injections) until ME is resolved; 2) be observed with no treatment until ME reoccurs, based on pre-specified central subfield thickness (CST) criteria; and then 3) be treated according to a T&E regimen up to 72 weeks, with dosing intervals based on time to relapse and adjusted by 4-week increments (minimum interval: every 4 weeks; maximum interval: no limit). Vision outcomes include best-corrected visual acuity (BCVA) and CST. The primary endpoint is the change from baseline in BCVA at week 72. Safety outcomes include ocular and non-ocular adverse events. Conclusion RVOSTAR will evaluate the long-term maintenance of vision outcomes with a modified faricimab T&E regimen in patients with CRVO-ME or HRVO-ME while reducing the burden associated with frequent injections. The findings from this study may help to optimize dosing frequency in clinical practice.
Investigation of temperature rise characteristics in the pre-stage of a deflector jet servo valve
Abstract In aerospace equipment, the deflector jet servo valve is a high-end core control component of electro-hydraulic servo systems. The pre-stage is crucial because of the compact structure and complex jet morphology. Temperature variations can significantly impair the valve’s control accuracy. To address the mechanism of heat generation and temperature prediction inside the pre-stage, a thermodynamic model of the pre-stage is developed using the control volume method to predict heat generation and temperature rise under varying operating conditions. This model enables prediction of temperature distribution across various chambers and the casing based on specified deflector displacements. A dedicated temperature-controlled test rig is established to measure the temperature distribution on the servo valve casing. Comparison of simulation results with experimental data validated the accuracy of the proposed model, and the temperature distribution predicted by the model has less than 0.76 °C error compared to the experiment. This study provides a theoretical basis for performance enhancement and structural optimization of deflector jet servo valves.
Study protocol: Using ecological momentary assessment and wearable sensors to examine mechanisms linking sleep and smoking cessation among adults who are socioeconomically disadvantaged
Background Cigarette smoking is highly concentrated among individuals with lower socioeconomic status (SES) who often lack access to smoking cessation services. Thus, smoking cessation in lower SES adults remains a critical public health concern that warrants further study and attention. Smokers attempting to quit are at the highest risk for lapse within the first weeks of their quit attempt, and an initial lapse is highly likely to lead to full relapse. It is essential to identify and understand behavioral factors that may increase or decrease the likelihood of successful smoking cessation among lower SES adults during a quit attempt (pre-and post-quit). Recently, sleep dysregulation, such as insufficient sleep duration, has been considered as a potential intervention target to address smoking behaviors (e.g., number of cigarettes smoked per day) and improve smoking cessation outcomes (e.g., abstinence). Recent studies have found that lower SES is associated with higher rates of poor sleep. Thus, SES should be accounted for when assessing sleep dysregulation during smoking cessation attempts. Although previous studies have examined the relationship between sleep dysregulation and smoking behavior and/or cessation outcomes, they have several methodological limitations, including the use of retrospective survey methods, use of cross-sectional study designs, relying solely on laboratory-based data collection, not assessing integrated sleep health dimensions (usually only sleep duration or quality is assessed), omitting lower SES adults who smoke, and focusing on a single pathway rather than bidirectional associations. Methods This study will use a real-time data capture approach among lower SES adults who are attempting to quit smoking. This approach will involve a granular examination of the bidirectional and temporal associations between daily sleep dysregulation and smoking cession processes (pre- and post-quit) using smartphone-based ecological momentary assessment (EMA) and wearable sensors. Specifically, we aim to identify bidirectional and temporal associations between daily smoking abstinence and sleep dysregulation via EMA and wrist-worn sensors during the first four weeks of a smoking cessation attempt. Discussion Findings from this study will yield preliminary data that will be used to develop and implement a Just-in-Time-Adaptive Intervention (JITAI) that aims to improve sleep health during smoking cessation.
A multi-label visualisation approach for malware behaviour analysis
Abstract Modern malware evolves continuously, posing persistent challenges to cybersecurity. Conventional classification approaches typically group malware by its primary objective, emphasising dominant behaviours while overlooking the complex and overlapping strategies common in real-world attacks. Here we present DECODE (DEep Classification Of Dynamic Exploits), a proportional multi-label, context-aware framework that combines object detection, explainable artificial intelligence (XAI), and agent-based large language models (LLMs) to deliver interpretable and comprehensive malware analysis. DECODE introduces the first object detection dataset specifically for malware classification, generated through an automated annotation pipeline that removes the need for manual labelling and remains effective even for visually indistinguishable malware features. To improve attribution reliability, we extend Gradient-weighted Class Activation Mapping (Grad-CAM) with a Bayesian formulation, enabling uncertainty-aware visualisation of discriminative regions linked to multiple categories. The regions identified through object detection are subsequently mapped to their corresponding API call sequences and interpreted via a multi-agent reasoning module, which incorporates critique-and-verification loops to reduce hallucinations and bias. Experimental evaluation shows multi-label and binary classification accuracies of 0.8513 and 0.9380, respectively, outperforming conventional deep learning baselines. By combining visual localisation, proportional multi-label scoring, and human-readable behavioural narratives, DECODE enables malware to be classified not only by intended impact but also by fine-grained structural and behavioural traits, offering a richer understanding of complex threats.
Physiology of body lateralization on regional lung ventilation and lung volumes in healthy subjects: Within-subjects design
Introduction Lateral positioning improves pulmonary mechanics and lung volumes, but its effects in healthy adults remain unclear due to individual variability. Objective To analyze the acute effects of lateral body positioning on regional lung ventilation and lung volumes in healthy adults. Methods This within-subject study included two protocols: supine and left-lateral position (unilateral) with repeated measures and supine, left, and right-lateral positions (bilateral). All positions were performed at 30° for 5 minutes on an automated rotation bed. Electrical Impedance Tomography measured regional lung ventilation (%) and end-expiratory lung volumes (EELV) across four lung regions: (anterior right [AR] and left [AL]; posterior right [PR], and left [PL]). Linear mixed models assessed the influence of body position and individual variability on regional ventilation and lung volumes, while the Restricted Maximum Likelihood method compared between right- and left-lateral positioning. Results In the unilateral protocol (n = 29; 58.6% male; 22.8 ± 4.0 years), left-lateral positioning decreased regional ventilation in nondependent regions (AR: −0.96%, PR: −1.63%) and increased it in dependent regions (PL: 1.17%, AL: 1.42%) versus supine (p < 0.001). EELV increased in PL (+ 0.7 mL/kg PBW), PR (+2.0), and AR (+2.8), but decreased in AL (−2.3) ( p < 0.001). In the bilateral protocol (n = 10, 70% male; 23.6 ± 3.2 years), regional ventilation showed no significant effects of position, ROI, or interaction ( p > 0.05). However, EELV varied significantly with body position ( p < 0.001), with no isolated ROI effect ( p = 1.000). Conclusions Lateral positioning improves regional ventilation in dependent lung regions and increases EELV in nondependent and posterior dependent lung regions, regardless of side. Trial registration ClinicalTrials.gov [ NCT06044896 ]
A novel spatial framework to validate arsenic exposure gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology
Physical Fitness Index and body mass index: A cross‐sectional study based on 1.3 million college students
Background To explore differences in physical fitness levels among different grade groups and identify the association between body mass index (BMI) and physical fitness index (PFI). Methods This study collected data from 1307857 participants (600999 females & 706858 males) who were undergraduate students aged from 18 to 24 years old. PFI was calculated using the z-scores of 5 sex-specific physical fitness items, namely sit-and-reach, broad jump, pull-up/sit-up, 50-meter dash, and 800/1000-meter run. BMI was classified into 4 categories based on the Asian standards recommended by the WHO: (1) BMI < 18.5 kg/m 2 ; (2) 18.5 kg/m 2 ≤ BMI < 23 kg/m 2 ; (3) 23 kg/m 2 ≤ BMI < 25 kg/m 2 ; (4) BMI ≥ 25 kg/m 2 . ANOVA was used to detect variations in PFI among BMI categories and differences in physical fitness levels within grade groups. Finally, quadratic models were constructed to explore the association between BMI and PFI. Results (1) An inverted “J” shape association was identified between BMI and PFI. (2) Both boys and girls in higher grades had higher PFI than those in lower grades. Conclusions The association between BMI and PFI is non-linear. Physical development and physical activity engagement may assist in improving the physical fitness level of college students. Therefore, colleges should foster a more physical activity-friendly environment to reduce overweight and obesity rates, thereby enhancing fitness levels.
Optical closed form soliton structures for the Kuralay-II equation in nonlinear optical complex media
Interpretable ensemble learning model with shapley additive explanations for predicting anxiety symptoms risk in Chinese older adults with body shape index abnormality
This study aimed to construct and validate an interpretable risk prediction model for anxiety symptoms in Chinese older adults with abnormal body shape, explore the association between A Body Shape Index (ABSI) and anxiety symptoms, and identify key predictive factors via explainable methods. Data were from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008–2014 (n = 1,844/2,663/3,058 for 2008/2011/2014). The 2008 data (80% training, 20% internal validation) and 2011/2014 data (external validation) were used. Feature selection, data balancing, ensemble learning (Boosting/Stacking/Voting), and Shapley Additive exPlanations (SHAP) were applied. ABSI was positively associated with anxiety symptoms (P = 0.038), with stronger effects in males (trend slope = 0.03) than females (0.02); female anxiety prevalence (39.37%) was higher than males (20.79%). The Boosting-ADASYN model performed best (internal AUC = 0.814, external AUC = 0.766–0.772). SHAP identified marital status, age, self-reported health, education, and happiness as top predictors. ABSI outperformed BMI in capturing abnormal body fat distribution. This study provides an interpretable tool for early anxiety identification in this population, supporting precise interventions combining ABSI and psychosocial strategies.