Search Results (5,131)

Background: Prior research confirms that social support promotes resilience among medical patients with chronic illness. Beyond emotional benefits, research has increasingly shown the importance of social support on physical health outcomes. Therefore, identifying and evaluating interventions that increase social support among medical patients with chronic conditions is a priority for healthcare. Methods: This meta-analysis summarized data from 39,493 medical patients across 14 non-randomized trials that had been identified by a prior review of the survival benefits of social support interventions. Results: Across four studies reporting hazard ratio data, the results failed to reach statistical significance (HR = 2.10, 95% CI = 0.99 to 4.48, p = 0.0546), and the results of ten studies reporting odds ratio data were of smaller magnitude (OR = 1.27, 95% CI [0.72, 2.23], p > 0.05). Heterogeneity characterized both the odds ratio data (I² = 53%; Q = 18.1, p = 0.03) and hazard ratio data (I² = 89%, Q = 23, p < 0.001). A notable finding was that studies with longer periods of data collection showed longer survival among medical patients receiving social support. Conclusions: Long-term observations may be necessary for the survival benefits of social support interventions to become apparent. Further research with a larger pool of data from long-term follow-up studies will be needed to establish firm conclusions. Full article

Powder bed fusion (PBF) is a widely adopted additive manufacturing (AM) process category that enables high-resolution fabrication across metals, polymers, ceramics, and composites. However, its inherent process complexity demands robust modeling to ensure quality, reliability, and scalability. This review provides a critical synthesis of advances in physics-based simulations, machine learning, and digital twin frameworks for PBF. We analyze progress across scales—from micro-scale melt pool dynamics and mesoscale track stability to part-scale residual stress predictions—while highlighting the growing role of hybrid physics–data-driven approaches in capturing process–structure–property (PSP) relationships. Special emphasis is given to the integration of real-time sensing, multi-scale modeling, and AI-enhanced optimization, which together form the foundation of emerging PBF digital twins. Key challenges—including computational cost, data scarcity, and model interoperability—are critically examined, alongside opportunities for scalable, interpretable, and industry-ready digital twin platforms. By outlining both the current state-of-the-art and future research priorities, this review positions digital twins as a transformative paradigm for advancing PBF toward reliable, high-quality, and industrially scalable manufacturing. Full article

Background/Objectives: Artificial intelligence (AI) has gained substantial prominence in pediatric dentistry, offering new opportunities to enhance diagnostic precision and clinical decision-making. AI-based systems are increasingly applied in caries detection, early childhood caries (ECC) risk prediction, tooth development assessment, mesiodens identification, and other key diagnostic tasks. This systematic review and meta-analysis aimed to synthesize evidence on the diagnostic performance of AI models developed specifically for pediatric dental applications. Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase following PRISMA-DTA guidelines. Studies evaluating AI-based diagnostic or predictive models in pediatric populations (≤18 years) were included. Reference screening, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using random-effects models. Sources of heterogeneity related to imaging modality, annotation strategy, and dataset characteristics were examined. Results: Thirty-two studies met the inclusion criteria for qualitative synthesis, and fifteen were eligible for quantitative analysis. For radiographic caries detection, pooled sensitivity, specificity, and AUC were 0.91, 0.97, and 0.98, respectively. Prediction models demonstrated good diagnostic performance, with pooled sensitivity of 0.86, specificity of 0.82, and AUC of 0.89. Deep learning architectures, particularly convolutional neural networks, consistently outperformed traditional machine learning approaches. Considerable heterogeneity was identified across studies, primarily driven by differences in imaging protocols, dataset balance, and annotation procedures. Beyond quantitative accuracy estimates, this review critically evaluates whether current evidence supports meaningful clinical translation and identifies pediatric domains that remain underrepresented in AI-driven diagnostic innovation. Conclusions: AI technologies exhibit strong potential to improve diagnostic accuracy in pediatric dentistry. However, limited external validation, methodological variability, and the scarcity of prospective real-world studies restrict immediate clinical implementation. Future research should prioritize the development of multicenter pediatric datasets, harmonized annotation workflows, and transparent, explainable AI (XAI) models to support safe and effective clinical translation. Full article

Herbage intake by grazers and browsers is of fundamental importance to agricultural ecosystems worldwide but is also notoriously difficult to quantify. The intake process is mediated by herbage comminution in the mouth. The attendant chew actions generate sound bursts that can be detected acoustically and analyzed to help elucidate the entire process. Goats consuming a single plant species were acoustically monitored in order to (i) determine the sensitivity of the chewing effort to the large variation in bite mass and satiety level and (ii) estimate how well the amount of herbage consumed can be predicted by counting chews. Experiments used hand-constructed patches containing bite-sized carob (Ceratonia siliqua L.) leaflets of a pre-determined mass that were presented to six goats, individually, with acoustic sensors attached to their horns. Experiment 1 determined the chewing effort and the sequence of bites and chews for three bite masses across five levels of total intake. Experiment 2 determined the chewing effort and the chew sequence at three levels of satiety, achieved by control of the feeding regime, using a single bite mass across three levels of total intake. In Experiment 1, the global chewing coefficient was ≈4 chews g⁻¹ fresh mass ingested (≈10 chews g⁻¹ dry matter). For an individual animal, the chewing coefficient was fairly stable, being influenced mildly by bite mass, but the variation between animals was large. In Experiment 2, the chewing coefficient was again fairly stable in an individual animal, although the chewing effort was slightly elevated at low satiety. At the population level, and for the most relevant range of intake levels, inverse regression of the pooled data from both experiments estimated the two-sided 95% confidence interval of the predicted intake of carob leaves to be <10% of the predicted value. If chewing coefficients can be estimated locally, usefully precise intake predictions should be attainable for the tested vegetation. These results are promising for the future potential of acoustic monitoring, although significant challenges remain. Full article

This paper proposes a robust convolutional neural network (CNN) architecture for human activity recognition (HAR) using smartphone accelerometer data, evaluated on the WISDM dataset. We introduce two novel pooling mechanisms—Pooling A (Extrema Contrast Pooling (ECP)) and Pooling B (Center Minus Variation (CMV))—that enhance feature discrimination and noise robustness. ECP emphasizes sharp signal transitions through a nonlinear penalty based on the squared range between extrema, while CMV Pooling penalizes local variability by subtracting the standard deviation, improving resilience to noise. Input data are normalized to the [0, 1] range to ensure bounded and interpretable pooled outputs. The proposed framework is evaluated in two separate configurations: (1) a 1D CNN applied to raw tri-axial sensor streams with the proposed pooling layers, and (2) a histogram-based image encoding pipeline that transforms segment-level sensor redundancy into RGB representations for a 2D CNN with fully connected layers. Ablation studies show that histogram encoding provides the largest improvement, while the combination of ECP and CMV further enhances classification performance. Across six activity classes, the 2D CNN system achieves up to 96.84% weighted classification accuracy, outperforming baseline models and traditional average pooling. Under Gaussian, salt-and-pepper, and mixed noise conditions, the proposed pooling layers consistently reduce performance degradation, demonstrating improved stability in real-world sensing environments. These results highlight the benefits of redundancy-aware pooling and histogram-based representations for accurate and robust mobile HAR systems. Full article

Background/Objectives: Type 2 diabetes mellitus (T2DM) significantly elevates the risk of coronary artery disease (CAD), particularly in Asian populations where both conditions are epidemic. While shared genetic factors contribute to this comorbidity, evidence from Asian cohorts remains fragmented, with limited focus on population-specific variants. This meta-analysis synthesizes evidence on genetic variants associated with CAD risk in Asian patients with T2DM. Methods: We systematically searched several databases according to the PRISMA statement and checklist. Pooled odds ratios (ORs) with corresponding 95% confidence intervals (CIs) were calculated using random-effects models, with heterogeneity assessed via I² and Cochran’s Q, and publication bias via funnel plots and Egger’s test. Results: In total, data on 11,268 subjects were reviewed, including 4668 cases and 6600 controls. Among 950 identified studies, 18 met eligibility criteria, and 14 studies provided sufficient data for the meta-analysis. The random-effects pooled estimate across all studied variants was not statistically significant (OR = 1.16 [95% CI: 0.68–2.00]; z = 0.56, p = 0.58). However, analysis of individual loci revealed gene-specific associations with CAD among this population: PCSK1 gene (OR = 2.12 [95% CI: 1.26–3.52]; p < 0.05; weight = 8.77%), GLP1R gene (OR = 2.25 [95% CI: 1.27–3.97]; p < 0.01; weight = 8.62%). ADIPOQ gene (OR = 8.00 [95% CI: 2.34–27.14]; p < 0.01; weight = 6.35%). Several genes were associated with an elevated risk of CAD: PCSK1 gene (OR = 2.12 [95% CI: 1.26–3.52]; p < 0.05; weight = 8.77%), GLP1R gene (OR = 2.25 [95% CI: 1.27–3.97]; p < 0.01; weight = 8.62%) and ADIPOQ gene (OR = 8.00 [95% CI: 2.34–27.14]; p < 0.01; weight = 6.35%). Several genes were associated with possible protective effects: ACE gene (OR = 0.41 [95% CI: 0.23–0.73]; p < 0.01; weight = 8.57%), Q192R gene (OR = 0.20 [95% CI: 0.08–0.52]; p < 0.001; weight = 7.41%). Heterogeneity was substantial (τ² = 0.78; I² = 81.95%; Q (13) = 64.67, p < 0.001). Conclusions: This first meta-analysis of genetic variants associated with CAD in Asian populations with T2DM identified specific locus-level associations implicating lipid metabolism, incretin signaling, and oxidative stress pathways. The lack of a significant pooled effect, alongside high heterogeneity, underscores the complexity and population-specific nature of this genetic architecture. These findings suggest that effective precision risk stratification may depend more on specific variants than on a broad polygenic signal, highlighting the need for further research in a larger, distinct sample size. Full article

Cancer remains a significant challenge in the field of medicine, primarily due to its inherent plasticity and the development of resistance to conventional therapeutic interventions. Genomic mutations and the activation of oncogenes enable cancer cells to resist senescence and apoptosis, leading to uncontrolled growth with harmful consequences. Small peptides are molecules with interesting anti-tumour properties and represent a valid alternative to conventional treatments. Our group has previously identified a class of small peptides bound to the DNA that can be extracted from the chromatin of various tissues, including wheat germ and trout. These peptide pools have been shown to possess interesting antiproliferative and apoptotic properties, and they are associated with cell cycle regulation. However, given the complexity of the extraction process, it is necessary to identify a substrate that will enable a more efficient extraction of these peptides, while also ensuring a composition that is simple to investigate. The present study developed a method for the extraction of this group of peptides from yeast, and the extract was then tested on cancer cells in order to confirm its anti-tumoral properties. The peptides were obtained from chromatin extracted from Saccharomyces cerevisiae cells through alkalisation and purification by gel filtration chromatography. The extract was tested on HeLa cells to verify its effects on vitality and the cell cycle. The data demonstrate that the chromatographic profile of this peptide extract indicates a more basic composition than the pool extracted from other tissues and exhibits comparable antiproliferative properties. The ability to rapidly obtain a biologically active, analytically accessible, and adequately purified fraction from the widely available substrate Saccharomyces cerevisiae represents a significant advance in the study of these DNA-binding peptides. Full article

In the Internet of Things (IoT), ultra-wideband (UWB) plays an essential role in localization and navigation. However, in indoor environments, UWB signals are often blocked by obstacles, leading to non-line-of-sight (NLOS) propagation. Thus, reliable line-of-sight (LOS)/NLOS identification is essential for reducing errors and enhancing the robustness of localization. This paper focuses on a single-anchor UWB configuration and proposes a temporal deep learning framework that jointly exploits two-way ranging (TWR) and angle-of-arrival (AOA) measurements for LOS/NLOS identification. At the core of the model is a temporal convolutional network (TCN) augmented with a self-attentive pooling mechanism, which enables the extraction of dynamic propagation patterns and temporal contextual information. Experimental evaluations on real-world measurement data show that the proposed method achieves an accuracy of 96.65% on the collected dataset and yields accuracies ranging from 88.72% to 93.56% across the three scenes, outperforming representative deep learning baselines. These results indicate that jointly exploiting geometric and temporal information in a single-anchor configuration is an effective approach for robust UWB indoor positioning. Full article

The proliferation of smart devices and the Internet of Things (IoT) has led to massive data generation, particularly in complex domains such as aerospace. Cloud computing provides essential scalability and advanced analytics for processing these vast datasets. However, relying solely on the cloud introduces significant challenges, including high latency, network congestion, and substantial bandwidth costs, which are critical for real-time on-orbit spacecraft services. Cloud-edge Internet of Things (cloud-edge IoT) computing emerges as a promising architecture to mitigate these issues by pushing computation closer to the data source. This paper proposes an improved YOLOV8-based model specifically designed for edge computing scenarios within a cloud-edge IoT framework. By integrating the Cross Stage Partial Spatial Pyramid Pooling Fast (CSPPF) module and the WDIOU loss function, the model achieves enhanced feature extraction and localization accuracy without significantly increasing computational cost, making it suitable for deployment on resource-constrained edge devices. Meanwhile, by processing image data locally at the edge and transmitting only the compact segmentation results to the cloud, the system effectively reduces bandwidth usage and supports efficient cloud-edge collaboration in IoT-based spacecraft monitoring systems. Experimental results show that, compared to the original YOLOV8 and other mainstream models, the proposed model demonstrates superior accuracy and instance segmentation performance at the edge, validating its practicality in cloud-edge IoT environments. Full article

Background: Differentiating bipolar disorder (BD) from unipolar major depressive disorder (MDD) at first episode is clinically consequential but challenging. Artificial intelligence/machine learning (AI/ML) may improve early diagnostic accuracy across imaging and non-imaging data sources. Methods: Following PRISMA 2020 and a pre-registered protocol on protocols.io, we searched PubMed, Scopus, Europe PMC, Semantic Scholar, OpenAlex, The Lens, medRxiv, ClinicalTrials.gov, and Web of Science (2014–8 October 2025). Eligible studies developed/evaluated supervised ML classifiers for BD vs. MDD at first episode and reported test-set discrimination. AUCs were meta-analyzed on the logit (GEN) scale using random effects (REML) with Hartung–Knapp adjustment and then back-transformed. Subgroup (imaging vs. non-imaging), leave-one-out (LOO), and quality sensitivity (excluding high risk of leakage) analyses were prespecified. Risk of bias used QUADAS-2 with PROBAST/AI considerations. Results: Of 158 records, 39 duplicates were removed and 119 records screened; 17 met qualitative criteria; and 6 had sufficient data for meta-analysis. The pooled random-effects AUC was 0.84 (95% CI 0.75–0.90), indicating above-chance discrimination, with substantial heterogeneity (I² = 86.5%). Results were robust to LOO, exclusion of two high-risk-of-leakage studies (pooled AUC 0.83, 95% CI 0.72–0.90), and restriction to higher-rigor validation (AUC 0.83, 95% CI 0.69–0.92). Non-imaging models showed higher point estimates than imaging models; however, subgroup comparisons were exploratory due to the small number of studies: pooled AUC ≈ 0.90–0.92 with I² = 0% vs. 0.79 with I² = 64%; test for subgroup difference Q = 7.27, df = 1, p = 0.007. Funnel plot inspection and Egger/Begg tests found that we could not reliably assess small-study effects/publication bias due to the small number of studies. Conclusions: AI/ML models provide good and robust discrimination of BD vs. MDD at first episode. Non-imaging approaches are promising due to higher point estimates in the available studies and practical scalability, but prospective evaluation is needed and conclusions about modality superiority remain tentative given the small number of non-imaging studies (k = 2). Full article

Background: Oral potentially malignant disorders (OPMDs) often exhibit heterogeneous clinical features, making the early detection of dysplasia very difficult. Several chemiluminescence-based devices, like ViziLite^®, have been suggested as non-invasive adjuncts that can enhance the visualization of suspicious mucosal changes. However, their true diagnostic value remains unclear. Methods: A systematic review and meta-analysis were conducted in line with PRISMA 2020 guidelines. Thirteen clinical studies met the inclusion criteria, necessitating chemiluminescence as index test and histopathology as reference standard, with extractable 2 × 2 diagnostic data. For all OPMDs and leukoplakia-only subgroups, pooled sensitivity and specificity, DOR, SROC curves, and device-specific diagnostic accuracy were determined. Results: Of all the OPMDs, chemiluminescence demonstrated a high pooled sensitivity of 0.82 and a low specificity of 0.48 with considerable heterogeneity among studies. The results in the leukoplakia subgroup improved sensitivity of 0.87 and a specificity of 0.51 were recorded with a more concave SROC curve, which illustrated a better discriminative ability in keratinized lesions. Comparison of devices illustrates accuracy was best for ViziLite + Lugol iodine (~0.82) followed by standard ViziLite (~0.62) and ViziLite Plus (~0.53). Conclusions: Chemiluminescence, while it may demonstrate good sensitivity, has repeatedly shown to have limited specificity in a consistent manner, particularly in populations with mixed OPMD where inflammatory and benign lesions inflate the false-positive rates. Notably, diagnostic performance was higher in leukoplakia, suggesting that keratinized lesions benefit most from this adjunctive tool. Overall, chemiluminescence may facilitate lesion visualization and biopsy site selection but cannot supplant histopathological examination as a definitive diagnostic modality. Full article

Background: The persistently low 5-year survival rate for pancreatic cancer (PC) underscores the critical need for early detection. However, population-wide screening remains impractical. Artificial Intelligence (AI) models using electronic health record (EHR) data offer a promising avenue for pre-symptomatic risk stratification. Objective: To systematically review and meta-analyze the performance of AI models for PC prediction based exclusively on structured EHR data. Methods: We systematically searched PubMed, MedRxiv, BioRxiv, and Google Scholar (2010–2025). Inclusion criteria encompassed studies using EHR-derived data (excluding imaging/genomics), applying AI for PC prediction, reporting AUC, and including a non-cancer cohort. Two reviewers independently extracted data. Random-effects meta-analysis was performed for AUC, sensitivity (Se), and specificity (Sp) using R software version 4.5.1. Heterogeneity was assessed using I² statistics and publication bias was evaluated. Results: Of 946 screened records, 19 studies met the inclusion criteria. The pooled AUC across all models was 0.785 (95% CI: 0.759–0.810), indicating good overall discriminatory ability. Neural Network (NN) models demonstrated a statistically significantly higher pooled AUC (0.826) compared to Logistic Regression (LogReg, 0.799), Random Forests (RF, 0.762), and XGBoost (XGB, 0.779) (all p < 0.001). In analyses with sufficient data, models like Light Gradient Boosting (LGB) showed superior Se and Sp (99% and 98.7%, respectively) compared to NNs and LogReg, though based on limited studies. Meta-analysis of Se and Sp revealed extreme heterogeneity (I² ≥ 99.9%), and the positive predictive values (PPVs) reported across studies were consistently low (often < 1%), reflecting the challenge of screening a low-prevalence disease. Conclusions: AI models using EHR data show significant promise for early PC detection, with NNs achieving the highest pooled AUC. However, high heterogeneity and typically low PPV highlight the need for standardized methodologies and a targeted risk-stratification approach rather than general population screening. Future prospective validation and integration into clinical decision-support systems are essential. Full article

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R²) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios. Full article

Background: Microvascular osseous free flaps play a central role in head and neck reconstruction; surgeons often rely on fragmented and inconsistently reported data when counselling patients and planning reconstructive strategies. This systematic review and meta-analysis aimed to quantify postoperative complication rates and to evaluate complication patterns according to flap type. Methods: The study protocol was registered in PROSPERO (CRD420251237516). Studies published between January 2000 and November 2025 reporting postoperative complications following mandibular or maxillary reconstruction with osseous free flaps were identified. Eligible studies included adult cohorts with a minimum sample size of twenty patients. Random-effects meta-analyses of proportions were conducted. Risk of bias was assessed using the ROBINS-I tool. Results: Fourteen retrospective studies encompassing 1198 flaps were included. The pooled incidence of total flap loss was 6% (95% CI 3–9%), and partial flap loss was 6% (95% CI 3–10%). The pooled rates for postoperative infection, fistula formation, and wound dehiscence were 7% (95% CI 2–22%), 12% (95% CI 7–20%), and 16% (95% CI 8–31%), respectively, with substantial heterogeneity. Fibular free flaps demonstrated pooled rates of 6.1% for total flap loss, 6.6% for partial flap loss, 9.0% for infection, 10.4% for fistula formation, and 17.1% for wound dehiscence. For scapular free flaps, pooled total flap loss was 5% (95% CI 1–29%). DCIA flaps demonstrated hardware-related complications (8.1%), fistulas (16.7%), bone exposure (4.2%), and wound dehiscence (29.7%). Donor site morbidity was inconsistently reported and could not be quantitatively synthesized. Conclusions: Osseous free flap reconstruction shows relevant complication rates, highlighting the need for standardized reporting to support evidence-based decision-making. Full article

Background/Objectives: Functional dyspepsia (FD) is a gastrointestinal disorder typically treated by changes in diet and lifestyle. However, in the Colombian population, few studies have addressed its etiology and diagnosis. This exploratory study aimed to identify predictive variables associated with the presence of dyspeptic symptoms (DS). Methods: To address this, a self-survey was conducted evaluating sociodemographic characteristics, clinical history, and dietary habits. A DS index was calculated using participant’s clinical history to explore the characteristics of the groups with more and less DS (MDS and LDS groups). Additionally, a regression model was applied to identify the predictors of higher DS scores. Pooled data from the rolling, cross-sectional eating habits and DS survey between May and July of 2024. We enrolled 102 Colombian participants between 18 and 65 years old. Results: Significant differences were identified between MDS and LDS groups in occupation and dietary habits, with students exhibiting a higher DS index. Moreover, MDS exhibited greater consumption of fatty and fried foods than LDS groups. Regression analysis revealed that high intake of fatty foods and sesame were the best predictors of higher DS index. In contrast, the consumption of Saccharomyces boulardii probiotic and white onion was associated with better gastrointestinal health. Conclusions: Changes in dietary habits are associated with lower DS; the effect and its etiology might also depend on the participants’ occupation and nutritional habits. Full article