Search Results (5,038)

Background/Objectives: Constitutive activation of the PI3K/AKT/mTOR signaling cascade underlies the aggressive phenotype of fibroblast-like synoviocytes (FLSs) in rheumatoid arthritis (RA); however, a quantitative synthesis of in vitro data on pathway inhibition remains lacking. This systematic review and meta-analysis aimed to (i) aggregate standardized effects of pathway inhibitors on proliferation, apoptosis, migration/invasion, IL-6/IL-8 secretion, p-AKT, and LC3; (ii) assess heterogeneity and identify key moderators of variability, including stimulus type, cell source, and inhibitor class. Methods: PubMed, Europe PMC, and the Cochrane Library were searched up to 18 May 2025 (PROSPERO CRD420251058185). Twenty of 2684 screened records met eligibility. Two reviewers independently extracted data and assessed study quality with SciRAP. Standardized mean differences (Hedges g) were pooled using a Sidik–Jonkman random-effects model with Hartung–Knapp confidence intervals. Heterogeneity (τ², I²), 95% prediction intervals, and meta-regression by cell type were calculated; robustness was tested with REML-HK, leave-one-out, and Baujat diagnostics. Results: PI3K/AKT/mTOR inhibition markedly reduced proliferation (to –5.1 SD), IL-6 (–11.1 SD), and IL-8 (–6.5 SD) while increasing apoptosis (+2.7 SD). Fourteen of seventeen outcome clusters showed large effects (|g| ≥ 0.8), with low–moderate heterogeneity (I² ≤ 35% in 11 clusters). Prediction intervals crossed zero only in small k-groups; sensitivity analyses shifted pooled estimates by ≤0.05 SD. p-AKT and p-mTOR consistently reflected functional changes and emerged as reliable pharmacodynamic markers. Conclusions: Targeted blockade of PI3K/AKT/mTOR robustly suppresses the proliferative and inflammatory phenotype of RA-FLSs, reaffirming this axis as a therapeutic target. The stability of estimates across multiple analytic scenarios enhances confidence in these findings and highlights p-AKT and p-mTOR as translational response markers. The present synthesis provides a quantitative basis for personalized dual-PI3K/mTOR strategies and supports the adoption of standardized long-term preclinical protocols. Full article

We planned a systemic review and meta-analysis to evaluate the diagnostic accuracy of Monocyte Distribution Width (MDW) in aiding the diagnosis of sepsis in the Emergency Department (ED) and Intensive Care Unit (ICU). A systematic literature search was performed in PubMed, Scopus, and OVID to retrieve studies published up to 29 January 2024. We examined results using mean difference and conducted a diagnostic test accuracy (DTA) meta-analysis using a bivariate random effects model. Pooled results showed that MDW was significantly higher in sepsis patients admitted to the ED (MD = 5.59, 95%CI: 4.14–7.05) or to the ICU (MD = 8.30, 95%CI: 2.98–13.62). Nine studies conducted in the ED were included in the DTA review. The overall sensitivity was 0.80 (95%CI: 0.75–0.85), the specificity was 0.76 (95%CI: 0.66–0.83), and the false-positive rate (FPR) was 0.24 (95%CI: 0.17–0.34). Three studies were conducted in the ICU, but only two were included in the DTA meta-analysis. Of the 662 patients admitted to the ICU, 175 developed sepsis, showing higher MDW values than non-septic patients. However, significant heterogeneity was noted among the studies. MDW is a helpful biomarker for sepsis in adult patients admitted to the ED and ICU. In the ED, MDW could aid clinicians in ruling out sepsis. Full article

The cross-sectional dimensions of structural elements in a structure are design elements that need to be carefully designed and are related to the stiffness of the structure. Various optimization processes are applied to determine the optimum cross-sectional dimensions of beams or columns in structures. By repeating the optimization processes for multiple load scenarios, it is possible to create a data set that shows the optimum design section properties. However, this step means repeating the same processes to produce the optimum cross-sectional dimensions. Artificial intelligence technology offers a short-cut solution to this by providing the opportunity to train itself with previously generated optimum cross-sectional dimensions and infer new cross-sectional dimensions. By processing the data, the artificial neural network can generate models that predict the cross-section for a new structural element. In this study, an optimization process is applied to a simple tubular column and an I-section beam, and the results are compiled to create a data set that presents the optimum section dimensions as a class. The harmony search (HS) algorithm, which is a metaheuristic method, was used in optimization. An artificial neural network (ANN) was created to predict the cross-sectional dimensions of the sample structural elements. The neural architecture search (NAS) method, which incorporates many metaheuristic algorithms designed to search for the best artificial neural network architecture, was applied. In this method, the best values of various parameters of the neural network, such as activation function, number of layers, and neurons, are searched for in the model with a tool called HyperNetExplorer. Model metrics were calculated to evaluate the prediction success of the developed model. An effective neural network architecture for column and beam elements is obtained. Full article

Background: Amid growing concerns about climate crisis and its psychological toll, understanding how people find meaning through their connection to nature is increasingly important. The first aim of this study is to examine the association between Nature Relatedness (NR) and Meaning in Life (MIL). The second aim is to investigate if gender moderates this association and to explore how Jewish traditions influence gender differences in this relationship. Methods: A multi-methods design was employed. Participants were recruited through purposive sampling of prominent Jewish community figures, followed by snowball sampling via informant referrals. Thirty-five participants completed the Meaning in Life Questionnaire (MLQ) and the NR Scale. Two questions provided qualitative insights into participants’ personal interpretations and culturally grounded meanings of NR and MIL in the context of climate change and Jewish traditions. Results: Hierarchical multiple regression analyses assessed the main effects of NR and gender, as well as their interaction, on MLQ subscales. NR positively correlated with the MLQ Search dimension, indicating that individuals with stronger NR actively seek meaning in life. Gender moderated this relationship: NR did not correlate with MLQ Presence overall, but higher NR was linked to greater MIL presence among female participants. Thematic analysis of qualitative responses revealed gender-based variations and emphasized the role of Jewish teachings in connecting NR to cultural and religious practices. Conclusions: The findings point to the importance of cultural, religious, and gender factors in shaping the relationship between NR and MIL in a time of climate change crisis, offering implications for positive mental health research and culturally sensitive interventions. Full article

Introduction: Asthma is a chronic respiratory disease affecting approximately 5 million children in the US, but little is known about whether asthma alters children’s attention and memory functions. Most studies on this topic focus on psychiatric and QoL outcomes rather than cognitive functions, leaving a gap in the literature. We aimed to conduct a systematic review and meta-analysis to evaluate the attention deficit and memory function outcomes in children with bronchial asthma. Methods: A systematic search was conducted in PubMed, Web of Science, and Cochrane Library from inception to 28 February 2025 for studies evaluating attention deficit and memory function in children with bronchial asthma. Outcomes of interest included attention deficit and memory function. Statistical analysis was performed with R 4.3.1. Heterogeneity was accessed using the I² statistics and Cochrane Q test. The standardized mean difference (SMD) with restricted maximum-likelihood estimator random-effects method was computed for all outcomes. Results: A total of seven studies were included in the final meta-analysis, comprising 104,975 patients, of whom 10,200 (9.7%) had bronchial asthma (mean age ± 8.98 years, mean 45% females). In the pooled analysis, children with asthma had a worsened attention deficit compared to the healthy group (SMD 0.29; 95% CI [0.07; 0.51]; p = 0.01; I² = 92%). However, no statistically significant difference was found in memory function between groups (SMD −0.24; 95% CI [−1.81; 1.33]; p = 0.77; I² = 96%). Conclusions: Children with asthma showed significantly higher attention deficit scores compared to healthy children. No statistically significant differences were observed in memory function between the groups. These findings may have implications for early cognitive screening in pediatric asthma management. Full article

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

This study arises from the search for non-invasive diagnostic alternatives for equine gastric ulceration (EGUS), which is prevalent, clinically variable and only confirmed by gastroscopy. The aim is to quantify five salivary biomarkers (IL1-F5, PIP, CA VI, serotransferrin, albumin) under clinical conditions by validated assays and analyse their diagnostic value. Horses were grouped in No EGUS (neither clinical signs of EGUS nor gastric lesions), EGUS non-clinical (apparently no clinical signs of EGUS but with gastric lesions), and EGUS clinical (obvious clinical signs of EGUS and with gastric lesions). The concentration of 5 analytes could be quantified using sandwich ELISA assays, with high precision (CV: 6.79–12.38%) and accuracy (>95%). Mean salivary levels of IL1-F5, CA-VI, serotransferrin and albumin were significantly higher in EGUS clinical horses compared to No EGUS horses, whereas PIP showed no statistical significance. EGUS non-clinical horses showed statistical differences with No EGUS horses for PIP and albumin. In addition, IL1-F5, CA-VI, serotransferrin and albumin showed moderate accuracy to distinguish between No EGUS and EGUS clinical horses (AUC ≥ 0.8), with sensitivity and specificity greater than 77% and 65%, respectively. Therefore, these biomarkers could be a promising starting point for screening horse that might have EGUS in practice. Full article

Autonomous underwater vehicles (AUVs) have obtained extensive application in the exploitation of marine resources. Terrain-aided navigation (TAN), as an accurate and reliable autonomous navigation method, is commonly used for AUV navigation. However, its accuracy degrades significantly in self-similar terrain features or measurement uncertainties. To overcome these challenges, we propose a novel terrain-aided navigation framework integrating an Improved Marine Predators Algorithm with Depth-First Search optimization (DFS-IMPA-TAN). This framework maintains positioning precision in partially self-similar terrains through two synergistic mechanisms: (1) IMPA-driven optimization based on the hunger-inspired adaptive exploitation to determine optimal trajectory transformations, cascaded with Kalman filtering for navigation state correction; (2) a Robust Tree (RT) hypothesis manager that maintains potential trajectory candidates in graph-structured memory, employing Depth-First Search for ambiguity resolution in feature matching. Experimental validation through simulations and in-vehicle testing demonstrates the framework’s distinctive advantages: (1) consistent terrain association in partially self-similar topographies; (2) inherent error resilience against ambiguous feature measurements; and (3) long-term navigation stability. In all experimental groups, the root mean squared error of the framework remained around 60 m. Under adverse conditions, its navigation accuracy improved by over 30% compared to other traditional batch processing TAN methods. Comparative analysis confirms superior performance over conventional methods under challenging conditions, establishing DFS-IMPA-TAN as a robust navigation solution for AUVs in complex underwater environments. Full article

Background/Objectives: Autism spectrum disorder (ASD) has been extensively studied through neuroimaging, primarily focusing on grey matter and more in children than in adults. Studies in children and adolescents fail to capture changes that may dampen with age, thus leaving only changes specific to ASD. While grey matter has been the primary focus, white matter (WM) may be more specific in identifying the particular biological signature of the neurodiversity of ASD. Diffusion tensor imaging (DTI) is the more appropriate tool to investigate WM in ASD. Despite being introduced in 1994, its application to ASD research began in 2001. Studies employing DTI identify altered fractional anisotropy (FA), mean diffusivity, and radial diffusivity (RD) in individuals with ASD compared to typically developing (TD) individuals. Methods: We systematically reviewed literature on 21 May 2025 on PubMed using the following strategy: (“autism spectrum”[ti] OR autistic[ti] OR ASD[ti] OR “high-functioning autism” OR Asperger*[ti] OR Rett*[ti]) AND (DTI[ti] OR “diffusion tensor”[ti] OR multimodal[ti] OR “white matter”[ti] OR tractograph*[ti]). Our search yielded 239 results, of which 26 were adult human studies and eligible. Results: Analysing the evidence, we obtained regionally diverse WM alterations in adult ASD, specifically in FA, MD, RD, axial diffusivity and kurtosis, neurite density, and orientation dispersion index, compared to TD individuals, mostly in frontal and interhemispheric tracts, association fibres, and subcortical projection pathways. These alterations were less prominent than those of children and adolescents, indicating that individuals with ASD may improve during brain maturation. Conclusions: Our findings suggest that white matter alterations in adults with ASD are regionally diverse but generally less pronounced than in younger populations. This may indicate a potential improvement or adaptation of brain structure during maturation. Further research is needed to clarify the neurobiological mechanisms underlying these changes and their implications for clinical outcomes. Full article

Background: Rock–blast design is a canonical inverse problem that joins elastodynamic partial differential equations (PDEs), fracture mechanics, and stochastic heterogeneity. Objective: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a systematic review of mathematical methods for geomechanically informed blast modelling and optimisation is provided. Methods: A Scopus–Web of Science search (2000–2025) retrieved 2415 records; semantic filtering and expert screening reduced the corpus to 97 studies. Topic modelling with Bidirectional Encoder Representations from Transformers Topic (BERTOPIC) and bibliometrics organised them into (i) finite-element and finite–discrete element simulations, including arbitrary Lagrangian–Eulerian (ALE) formulations; (ii) geomechanics-enhanced empirical laws; and (iii) machine-learning surrogates and multi-objective optimisers. Results: High-fidelity simulations delimit blast-induced damage with ≤0.2 m mean absolute error; extensions of the Kuznetsov–Ram equation cut median-size mean absolute percentage error (MAPE) from 27% to 15%; Gaussian-process and ensemble learners reach a coefficient of determination ($R^2>0.95$) while providing closed-form uncertainty; Pareto optimisers lower peak particle velocity (PPV) by up to 48% without productivity loss. Synthesis: Four themes emerge—surrogate-assisted PDE-constrained optimisation, probabilistic domain adaptation, Bayesian model fusion for digital-twin updating, and entropy-based energy metrics. Conclusions: Persisting challenges in scalable uncertainty quantification, coupled discrete–continuous fracture solvers, and rigorous fusion of physics-informed and data-driven models position blast design as a fertile test bed for advances in applied mathematics, numerical analysis, and machine-learning theory. Full article

High-voltage diodes, as key devices in power electronic systems, have important significance for system reliability and preventive maintenance in terms of storage life prediction. In this paper, we propose a hybrid modeling framework that integrates the Long Short-Term Memory Network (LSTM) and Transformer structure, and is hyper-parameter optimized by the Improved Artificial Bee Colony Algorithm (IABC), aiming to realize the high-precision modeling and prediction of high-voltage diode storage life. The framework combines the advantages of LSTM in time-dependent modeling with the global feature extraction capability of Transformer’s self-attention mechanism, and improves the feature learning effect under small-sample conditions through a deep fusion strategy. Meanwhile, the parameter type-aware IABC search mechanism is introduced to efficiently optimize the model hyperparameters. The experimental results show that, compared with the unoptimized model, the average mean square error (MSE) of the proposed model is reduced by 33.7% (from 0.00574 to 0.00402) and the coefficient of determination (R²) is improved by 3.6% (from 0.892 to 0.924) in 10-fold cross-validation. The average predicted lifetime of the sample was 39,403.3 h, and the mean relative uncertainty of prediction was 12.57%. This study provides an efficient tool for power electronics reliability engineering and has important applications for smart grid and new energy system health management. Full article

Foraging behavior in hens is an important indicator of animal welfare. It involves both the search for food and exploration of the environment, which provides necessary enrichment. In addition, it has been inversely linked to damaging behaviors such as severe feather pecking. Conventional studies rely on manual observation to investigate foraging location, duration, timing, and frequency. However, this approach is labor-intensive, time-consuming, and subject to human bias. Our study developed computer vision-based methods to automatically detect foraging hens in a cage-free research environment and compared their performance. A cage-free room was divided into four pens, two larger pens measuring 2.9 m × 2.3 m with 30 hens each and two smaller pens measuring 2.3 m × 1.8 m with 18 hens each. Cameras were positioned vertically, 2.75 m above the floor, recording the videos at 15 frames per second. Out of 4886 images, 70% were used for model training, 20% for validation, and 10% for testing. We trained multiple You Only Look Once (YOLO) object detection models from YOLOv9, YOLOv10, and YOLO11 series for 100 epochs each. All the models achieved precision, recall, and mean average precision at 0.5 intersection over union (mAP@0.5) above 75%. YOLOv9c achieved the highest precision (83.9%), YOLO11x achieved the highest recall (86.7%), and YOLO11m achieved the highest mAP@0.5 (89.5%). These results demonstrate the use of computer vision to automatically detect complex poultry behavior, such as foraging, making it more efficient. Full article

Background/Objectives: Patients with an ileoanal pouch change their diet to manage their symptoms and will often resort to the internet for nutrition advice. Currently, no evidence-based dietary guidelines exist to inform online resources. Hence, this study aims to assess the quality of online nutrition information directed towards patients with an ileoanal pouch. Methods: A systematic Google search was conducted to identify consumer websites including information on nutrition for those with ileoanal pouches. Quality was assessed using the DISCERN instrument, and the readability of written content was assessed using the Flesch–Kincaid score. A summative content analysis was used to identify the frequency of particular topics. Websites were also assessed against standards from the National Institute for Health and Care Excellence (NICE) framework for shared decision-making support tools. Results: A total of 12 websites met the inclusion criteria. Mean total DISCERN scores across all websites are 33 out of 75, indicating that overall, the websites were of poor quality. The mean Flesch–Kincaid score was 57 out of 100, or “fairly difficult” in terms of readability. The main themes according to the content analysis were “general dietary advice for pouch”, “dietary strategies for symptom management”, “addressing risks associated with having a pouch”, and “optimisation of nutritional intake”. Overall, websites did not meet the standards for shared decision-making. Conclusions: Online nutrition information for patients with an ileoanal pouch is of poor quality and difficult to understand. There is a need for higher quality online resources for these patients, ideally co-produced with a multidisciplinary team and patient, to provide patients with good quality, understandable, and accessible nutrition information. Full article

Fly-ash-based bubble lightweight soil is widely used due to its environmental friendliness, load reduction, ease of construction, and low costs. In this study, 41 sets of 28 d compressive strength data on lightweight soils with different water–cement ratios, blowing agent dosages, and fly ash dosages were collected through a literature search and indoor tests. Using the compressive strength index and SEM tests, the correlation between the mix ratio design and the microscopic particle components was investigated. The findings were as follows: carbonation reactions occurred in lightweight soil during the maintenance process, and the particles were spherical; increasing the dosage of blowing agent increased the soil’s porosity and pore diameter, leading to the formation of through-holes and reducing the compressive strength and mobility; increasing the fly ash dosage and water–cement ratio increased the soil’s mobility but reduced its compressive strength; and the strength decreased significantly when the fly ash dosage was more than 16% (e.g., the strength at a 20% dosage was 17.8% lower than that at a 15% dosage). Feature importance analysis showed that the water–cement ratio (57.7%), fly ash dosage (30.9%), and blowing agent dosage (11.1%) had a significant effect on strength. ExtraTrees, LightGBM, and Bayesian-optimized Random Forest models were used for 28d strength prediction with coefficients of determination (R²) of 0.695, 0.731, and 0.794, respectively. The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. The accuracy of the model is expected to be further improved with expansions in the database. A 28 d compressive strength prediction platform for fly-ash-based bubble lightweight soil was ultimately developed, providing a convenient tool for researchers and engineers to predict material properties and mix ratios. Full article

Background/Objectives: Being overweight and obesity are major public health concerns that demand effective nutritional strategies for weight and body composition management. Beyond excess weight, these conditions are closely linked to chronic inflammation, oxidative stress, and gut dysbiosis, all of which contribute to cardiometabolic risk. Polyphenols—bioactive compounds in plant-based foods—may support improvements in body composition and metabolic health by modulating gut microbiota, reducing oxidative stress, and suppressing inflammation. This systematic review and meta-analysis aimed to evaluate the effects of polyphenol-rich interventions on gut microbiota composition, in combination with either oxidative stress or inflammatory biomarkers, and their potential impact on body composition in overweight or obese adults. Methods: A systematic search of PubMed, Scopus, Cochrane, and Web of Science was conducted through May 2025. Eligible randomized controlled trials included adults (BMI ≥ 25 kg/m²) receiving polyphenol-rich interventions, with reported outcomes on gut microbiota and at least one inflammatory or oxidative stress biomarker. Standardized mean differences (SMDs) were pooled using a random-effects model. Results: Thirteen trials (n = 670) met inclusion criteria. Polyphenol supplementation significantly reduced circulating lipopolysaccharides (LPSs; SMD = −0.56; 95% CI: −1.10 to −0.02; p < 0.04), indicating improved gut barrier function. Effects on cytokines (IL-6, TNF-α) and CRP were inconsistent. Catalase activity improved significantly (SMD = 0.79; 95% CI: 0.30 to 1.28; p < 0.001), indicating enhanced antioxidant defense. Gut microbiota analysis revealed increased butyrate (SMD = 0.57; 95% CI: 0.18 to 0.96; p < 0.001) and acetate (SMD = 0.42; 95% CI: 0.09 to 0.75; p < 0.01), supporting prebiotic effects. However, no significant changes were observed in BMI or body weight. Conclusions: Polyphenol supplementation in overweight or obese adults may reduce metabolic endotoxemia, boost antioxidant activity, and promote SCFAs production. Effects on inflammation and body weight remain unclear. Further long-term trials are needed. Full article