Search Results (1,069)

Background/Objectives: Early childhood caries (ECC) and saliva have been studied across disparate domains, including microbiome, fluoride, immune, oxidative-stress, and neuroendocrine research. However, the ECC–saliva literature has not previously been mapped as a connected system using modern natural language processing (NLP). This study treats PubMed titles and abstracts as data to identify major themes, emerging topics, and candidate neuroimmune axes in ECC–saliva research. Methods: Using the NCBI E-utilities API, we retrieved 298 PubMed records (2000–2025) matching (“early childhood caries” [Title/Abstract]) AND saliva [Title/Abstract]. Text was cleaned with spaCy and embedded using a transformer encoder; BERTopic combined UMAP dimensionality reduction and HDBSCAN clustering to derive thematic topics. We summarised topics with class-based TF–IDF, constructed keyword co-occurrence networks, defined an internal topic-level Novelty Index (semantic distance plus temporal dispersion), and mapped high-novelty topics to gene ontology and Reactome pathways using g:Profiler. Prophet was used to model temporal trends and forecast topic-level publication trajectories. Finally, we generated a fully synthetic neuroimmune salivary dataset, based on realistic ranges from the literature, to illustrate how the identified axes could be operationalised in future ECC cohorts. Results: Seven coherent ECC–saliva topics were identified, including classical microbiome and fluoride domains as well as antioxidant/redox, proteomic, peptide immunity, and Candida–biofilm themes. High-novelty topics clustered around total antioxidant capacity, glutathione peroxidase, superoxide dismutase, and peptide-based host defence. Keyword networks and ontology enrichment highlighted “Detoxification of Reactive Oxygen Species”, “cellular oxidant detoxification”, and cytokine-mediated signalling as central processes. Temporal forecasting suggested plateauing growth for classical epidemiology and fluoride topics, with steeper projected increases for antioxidant and peptide-immunity themes. A co-mention heatmap revealed a literature-level Candida–cytokine–neuroendocrine triad (e.g., Candida albicans, IL-6/TNF, cortisol), which we propose as a testable neuro-immunometabolic hypothesis rather than a confirmed mechanism. Conclusions: AI-assisted topic modelling and network analysis provide a reproducible, bibliometric map of ECC–saliva research that highlights underexplored antioxidant/redox and neuroimmune salivary axes. The synthetic neuroimmune dataset and modelling pipeline are illustrative only, but together with the literature map, they offer a structured agenda for future ECC cohorts and mechanistic studies. Full article

Background/Objectives: This study aimed to analyze the relationship between various anthropometric measurements (Body Mass Index (BMI), Clínica Universidad de Navarra-Body Adiposity Estimator (CUNBAE), hip and waist circumference (WC), weight, and height) and Triple-Negative Breast Cancer (TNBC) according to menopausal status. Methods: A total of 113 TNBC cases and 226 matched controls from the MCC-Spain study were included. Controls were matched by age, educational level, family history, and province. Conditional logistic regression models, stratified by menopausal status, were used to estimate adjusted Odds Ratios (aORs) and their 95% Confidence Intervals (95% CIs) for the association between anthropometric measures and TNBC risk. Results: A divergent non-significant trend was observed: compared to their respective controls, premenopausal cases tended to have lower mean anthropometric measurements (except height), while postmenopausal cases showed higher means. No statistically significant associations were observed for individual measures derived from logistic regressions. However, when comparing women with normal BMI and normal WC (the reference group), a non-significant association of risk was found in those premenopausal women who were centrally obese (normal weight/high WC) (aOR = 1.79; 95% CI = 0.17–18.29), but the combination of overweight and a large WC showed an aOR of 0.22 (95% CI = 0.03–1.68) before menopause. In contrast, the combination of overweight and a high WC showed a statistically significant adjusted OR of 3.28 in postmenopausal women (95% CI = 1.10–9.81). Conclusions: Our findings suggest that the relationship between adiposity and TNBC is inverse in premenopausal women and direct in postmenopausal women, highlighting the importance of considering both body fat distribution and menopausal status when evaluating TNBC. However, our findings are limited by low statistical power, which may have led to a lack of statistical significance, and there is a need for larger, collaborative studies. Full article

Cow efficiency is vitally important to beef sustainability, and computer simulation models may be useful tools to identify characteristics of the most efficient cow genotypes for a given production environment. The objective of this analysis was to determine whether the Beef Cattle Systems Model could replicate empirical research demonstrating a genotype–nutritional environment interaction for efficiency of feed conversion to calves weaned. Combinations of cow genotypes for lactation potential (8, 10, and 12 kg/d at peak milk) and growth potential (450, 505, and 650 kg mature weight) were simulated across four dry matter intake levels (58, 76, 93, and 111 g/kg BW^0.75). At lower dry matter intakes, cows had lesser body condition scores and weight and longer postpartum intervals, but dry matter intake had minimal influence on pregnancy percentage or calf-weaning weight. These trends match empirical research except for pregnancy percentage, where decreasing dry matter intake had a dramatic effect on pregnancy percentage in high-milking, high-growth-potential genotypes. Efficiency of feed conversion was greatest at low dry matter intake for the model simulation with no evidence of a genotype–dry matter intake interaction, which is in contrast to empirical research demonstrating a genotype–dry matter intake interaction. In conclusion, standard nutrition equations do not replicate the genotype–nutritional environment interaction observed in empirical research studies. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

Five prevention is an important technical means to prevent maloperations in substations, and knowledge extraction from wiring diagrams is the key to intelligent “five prevention logic verification”. To address the error accumulation caused by multimodal object matching in traditional methods, this paper proposes a hierarchical recognition-based approach for topological knowledge extraction. This method establishes a multi-level recognition framework utilizing image tiling, decomposing the wiring diagram recognition task into three hierarchical levels from top to bottom: connection modes, bay types, and switching devices. A depth-first strategy is employed to establish parent–child node relationships, forming an initial topological structure. Based on the recognition results, the proposed approach performs regularized parsing and leverages a bay topology knowledge base to achieve automated matching of inter-device topological relationships. To enhance recognition accuracy, the model incorporates a Swin Transformer block to strengthen global feature perception and adds an ultra-small target detection layer to improve small-object recognition. The experimental results demonstrate that all recognition layers achieve mAP@0.5 exceeding 90%, with an overall precision of 93.9% and a recall rate of 91.7%, outperforming traditional matching algorithms and meeting the requirements for wiring diagram topology knowledge extraction. Full article

High-quality code representations are fundamental to code intelligence. Achieving such representations with parameter-efficient fine-tuning (PEFT) remains a key challenge. While code pre-trained models (CodePTMs) offer a robust foundation for general-purpose embeddings, current PEFT approaches face two main obstacles when adapting them: (i) they fail to adequately capture the deep structural characteristics of programs, and (ii) they are limited by the model’s finite internal parameters, restricting their ability to overcome inherent knowledge bottlenecks. To address these challenges, we introduce a parameter-efficient code representation learning framework that combines retrieval augmentation with structure-aware priors. Our framework features three complementary, lightweight modules: first, a structure–semantic dual-channel retrieval mechanism that infuses high-quality external code knowledge as non-parametric memory to alleviate the knowledge bottleneck; second, a graph relative bias module that strengthens the attention mechanism’s capacity to model structural relationships within programs; and third, a span-discriminative contrastive objective that sharpens the distinctiveness and boundary clarity of span-level representations. Extensive experiments on three benchmarks spanning six programming languages show that our method consistently outperforms state-of-the-art parameter-efficient baselines. Notably, on structure-sensitive tasks using the PLBART backbone, RS-Rep surpasses full fine-tuning, delivering a 22.1% improvement in Exact Match for code generation and a 4.4% increase in BLEU scores for code refinement, all while utilizing only about 5% of the trainable parameters. Full article

Background/Objectives: Geographic Information Systems (GIS) offer essential capabilities for identifying spatial concentrations of vulnerability and strengthening context-aware prevention strategies. This manuscript describes a geospatial architecture designed to generate anticipatory, place-based risk identification applicable across diverse community and institutional environments. Interpersonal Violence (IPV), one of several preventable harms that benefit from this spatially informed analysis, remains a critical public health challenge shaped by structural, ecological, and situational factors. Methods: The conceptual framework presented integrates de-identified surveillance data, ecological indicators, environmental and temporal dynamics into a unified spatial epidemiological model. Multilevel data layers are geocoded, spatially matched, and analyzed using clustering (e.g., Getis-Ord Gi*), spatial dependence metrics (e.g., Moran’s I), and contextual modeling to support anticipatory identification of elevated vulnerability. Framework Outputs: The model is designed to identify spatial clustering, mobility-linked risk patterns, and emerging escalation zones using neighborhood disadvantage, built-environment factors, and situational markers. Outputs are intended to support both clinical decision-making (e.g., geocoded trauma screening, and context-aware discharge planning), and community-level prevention (e.g., targeted environmental interventions and cross-sector resource coordination). Conclusions: This framework synthesizes behavioral theory, spatial epidemiology, and prevention science into an integrative architecture for coordinated public health response. As a conceptual foundation for future empirical research, it advances the development of more dynamic, spatially informed, and equity-focused prevention systems. Full article

Multi-object tracking (MOT) in videos captured by Unmanned Aerial Vehicles (UAVs) is critically challenged by significant camera ego-motion, frequent occlusions, and complex object interactions. To address the limitations of conventional trackers that depend on static, rule-based association strategies, this paper introduces STC-SORT, a novel tracking framework whose core is a two-level reasoning architecture for data association. First, a Spatio-Temporal Consistency Graph Network (STC-GN) models inter-object relationships via graph attention to learn adaptive weights for fusing motion, appearance, and geometric cues. Second, these dynamic weights are integrated into a 4D association cost volume, enabling globally optimal matching across a temporal window. When integrated with an enhanced AEE-YOLO detector, STC-SORT achieves significant and statistically robust improvements on major UAV tracking benchmarks. It elevates MOTA by 13.0% on UAVDT and 6.5% on VisDrone, while boosting IDF1 by 9.7% and 9.9%, respectively. The framework also maintains real-time inference speed (75.5 FPS) and demonstrates substantial reductions in identity switches. These results validate STC-SORT as having strong potential for robust multi-object tracking in challenging UAV scenarios. Full article

Background/Objectives: Postoperative opioid exposure after lumbar fusion remains a key clinical concern. Understanding which perioperative factors are associated with lower postoperative opioid use may help optimize recovery after minimally invasive (MIS) transforaminal lumbar interbody fusion (TLIF). This study aimed to determine if patients undergoing MIS-TLIF under spinal anesthesia (SA) showed lower postoperative opioid use compared to those undergoing MIS-TLIF under general anesthesia (GA). Methods: We retrospectively studied all adult patients (>18 years) undergoing 1- and contiguous 2-level MIS-TLIFs performed by a single surgeon. Patients undergoing the procedure under GA were compared to those undergoing the procedure under SA. Postoperative oral opioid use, up to 3 months post discharge, was collected. A 1:1 propensity score matching (PSM) protocol was implemented. Each outcome variable was initially assessed using univariate regression. Predictor variables with a p-value < 0.2 were included in the multivariate regression model. This was a retrospective, non-randomized study, and residual confounding cannot be excluded despite PSM. Results: The matched groups (n = 50 in each group) did not differ significantly depending on demographics or levels fused. Before regression, mean number of postoperative opioid prescriptions (p = 0.03), mean total operating room (OR) time in minutes (p < 0.01), and median length of stay (LOS) in days (p = 0.03) were significantly different. Multivariate regression showed that the GA group received 216.5 more total morphine milligram equivalents than the SA group (95% CI = 0.7–432.2, p = 0.049). The days of opioid use were higher in the GA group by 3.8 days (95% CI = 0.5 to 7.1, p = 0.025). On multivariate regression, LOS in hours was greater in the GA group by 14.1 h (p = 0.042). Conclusions: SA is an effective anesthetic modality for spinal surgery with the advantages of reduced postoperative opioid use, reduced OR time, and shorter LOS compared to GA. Full article

Objectives: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder with diverse pathogenetic mechanisms and clinical manifestations. Phenotype D PCOS is characterized by oligomenorrhoea and polycystic ovaries without hyperandrogenism. Altered adipokine profiles may contribute to reproductive and metabolic disturbances. Chemerin is an adipokine involved in inflammatory and metabolic processes. It remains unclear whether altered chemerin levels in PCOS reflect metabolic dysfunction alone or are directly associated with hyperandrogenism. The aim of this study was to compare serum chemerin levels in women with normoandrogenic PCOS and a control group. Methods: This cross-sectional preliminary study included 49 women with phenotype D PCOS and 40 healthy, age- and body mass index (BMI)-matched controls. Anthropometric, biochemical, hormonal parameters, and serum chemerin concentrations were assessed. Results: Serum chemerin concentrations did not differ significantly between the groups. In the PCOS group, the 95% confidence interval ranged from 198.61 to 234.37, while in the controls, it ranged from 187.13 to 216.21. In women with PCOS, chemerin showed significant positive correlations with weight, BMI, waist and hip circumference, total adipose tissue, and both gynoid and android fat content. Positive correlations were also observed with highly sensitive C-reactive protein (hs-CRP), insulin, glucose, triglycerides, and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), and a negative correlation was found with high-density lipoprotein (HDL) cholesterol. Chemerin was weakly negatively correlated with sex hormone binding globulin (SHBG) and positively correlated with the free androgen index (FAI). In the control group, chemerin correlated positively with CRP, insulin, triglycerides, total and gynoid adipose tissue, and negatively correlated with HDL cholesterol and SHBG. Conclusions Although chemerin levels did not differ from controls, chemerin was associated with metabolic and inflammatory markers in both groups. These findings should be considered preliminary due to the limited sample size. Chemerin may reflect metabolic and inflammatory status rather than hyperandrogenism in normoandrogenic PCOS. Full article

Electric vehicles (EVs), electric buses (EBs), and battery energy storage system (BESS), as both controllable power sources and load, play a great role in providing flexibility for the power grid, especially with the increased renewable energy penetration. However, there is still a lack of studies on EVs’ pricing strategy as well as the EBs-charging piles matching problem. To address these issues, a multi-objective optimal operation model is presented to achieve the lowest load fluctuation level, minimum electricity cost, and maximum discharging benefit. An improved load boundary prediction method (ICDLBPM) and a novel pricing strategy are proposed. In addition, reduction in the number of EBs charging piles would not only impact normal operation of EBs, but also even lead to load flexibility decline. Thus a handling method of the EBs-charging piles matching problem is presented. Several case studies were conducted on a regional distribution network comprising 100 EVs, 30 EBs, and 20 BESS units. The developed model and methodology demonstrate superior performance, improving load smoothness by 45.78% and reducing electricity costs by 19.73%. Furthermore, its effectiveness is also validated in a large-scale system, where it achieves additional reductions of 39.31% in load fluctuation and 62.45% in total electricity cost. Full article

Objectives: This study aimed to perform matched comparisons of the surgical outcomes of combined anterior cruciate ligament (ACL) and anterolateral ligament (ALL) reconstruction with those of isolated ACL reconstruction, in which allografts were used for the ACL. Methods: Patients who underwent anatomical ACL reconstruction with or without additional ALL reconstruction between 2017 and 2023 and had a minimum follow-up of 2 years were included and grouped according to whether an additional ALL reconstruction was performed. The cohorts were statistically adjusted using an inverse probability of treatment weighting (IPTW) to control for potential confounders related to surgical indication, including age, activity level, sex, rotational knee laxity, and preoperative osteoarthritic grade. Between-group comparisons were conducted for baseline characteristics, clinical outcomes, knee laxity, and radiologic parameters. Results: Fifty-nine patients were included (Group 1: 39 isolated ACL reconstructions; Group 2: 20 combined ACL and ALL reconstructions). Before IPTW adjustment, a significant difference was observed in the preoperative pivot-shift test (p = 0.008), which was no longer significant after weighting. Postoperative functional outcomes and knee stability were comparable between groups; however, the incidence of surgical failure was significantly lower in Group 2 both before and after IPTW adjustment (p = 0.044 and p = 0.049, respectively). Regarding radiologic parameters, the signal-to-noise quotient of the ACL graft was also significantly lower in Group 2, both before and after IPTW adjustment (p = 0.046 and p = 0.038, respectively). Conclusions: In ACL reconstruction using allografts, the addition of ALL reconstruction resulted in more favorable clinical and radiologic outcomes—particularly a lower incidence of surgical failure and greater postoperative graft maturity—compared with isolated ACL reconstruction. Full article

Background/Objectives: Missing data in clinical observational studies, such as out-of-hospital cardiac arrest (OHCA) registries, can compromise statistical validity. Single imputation methods are simple alternatives to complete-case analysis (CCA) but do not account for imputation uncertainty. Multiple imputation (MI) is the standard for handling missing-at-random (MAR) data, yet its implementation remains challenging. This study evaluated the performance of MI in association analysis compared with CCA and single imputation methods. Methods: Using a simulation framework with real-world Singapore OHCA registry data (N = 13,274 complete cases), we artificially introduced 20%, 30%, and 40% missingness under MAR. MI was implemented using predictive mean matching (PMM), random forest (RF), and classification and regression trees (CART) algorithms, with 5–20 imputations. Performance was assessed based on bias and precision in a logistic regression model evaluating the association between alert issuance and bystander CPR. Results: CART outperformed PMM, providing more accurate β coefficients and stable CIs across missingness levels. Although K-Nearest Neighbours (KNN) produced similar point estimates, it underestimated imputation uncertainty. PMM showed larger bias, wider and less stable CIs, and in some settings performed similarly to CCA. MI methods produced wider CIs than single imputation, appropriately capturing imputation uncertainty. Increasing the number of imputations had minimal impact on point estimates but modestly narrowed CIs. Conclusions: MI performance depends strongly on the chosen algorithm. CART and RF methods offered the most robust and consistent results for OHCA data, whereas PMM may not be optimal and should be selected with caution. MI using tree-based methods (CART/RF) remains the preferred strategy for generating reliable conclusions in OHCA research. Full article

Background and Objectives: Metabolic syndrome (MetS) is a complex condition marked by insulin resistance, central obesity, dyslipidemia, and chronic inflammation. Emerging evidence highlights the roles of hypoxia and mitochondrial stress in its pathophysiology. Hypoxia-inducible factor-1 alpha (HIF1α) and the mitophagy-associated proteins BNIP3 and BNIP3L are key components of hypoxia-responsive mitochondrial stress signaling. This study aimed to evaluate the circulating levels of HIF1α, BNIP3, and BNIP3L in MetS and to explore their associations with metabolic and inflammatory parameters. Materials and Methods: Serum concentrations of HIF1α, BNIP3, and BNIP3L were measured by ELISA in 40 patients with MetS and 40 age and sex-matched controls. Biochemical, hematological, and anthropometric parameters were assessed, and receiver operating characteristic (ROC) analyses were performed to evaluate diagnostic performance. Results: Serum levels of HIF1α, BNIP3, and BNIP3L levels were significantly higher in MetS patients compared with controls (p = 0.001). ROC analysis demonstrated strong diagnostic potential, particularly for BNIP3 (AUC = 0.928), followed by HIF1α (AUC = 0.885) and BNIP3L (AUC = 0.770). These markers showed significant associations with metabolic indicators such as BMI, fasting glucose, triglycerides, and inflammatory markers. Conclusions: The coordinated upregulation of circulating HIF1α, BNIP3, and BNIP3L in MetS is associated with metabolic dysregulation and systemic inflammation, reflecting alterations in hypoxia-responsive mitophagy-associated signaling rather than direct functional impairment of mitophagy. These findings support the potential relevance of these markers as indicators of metabolic stress in MetS. Further tissue-based and mechanistic studies are warranted to clarify their role in disease pathophysiology. Full article

Understanding how primary structural features and secondary material properties adapt to functional loads is essential to determining their effect on changes in joint biomechanics over time. The objective of this study was to map and correlate spatiotemporal changes in primary structural features, secondary material properties, and dentoalveolar joint (DAJ) stiffness with age in rats subjected to prolonged chewing of soft foods versus hard foods. To probe how loading history shapes the balance between the primary and secondary features, four-week-old rats were fed either a hard-food (HF, N = 25) or soft-food (SF, N = 25) diet for 4, 12, 16, and 20 weeks, and functional imaging of intact mandibular DAJs was performed at 8, 12, 16, 20, and 24 weeks. Across this time course, the primary structural determinants of joint function (periodontal ligament (PDL) space, contact area, and alveolar bone socket morphology) and secondary material and microstructural determinants (tissue-level stiffness encoded by bone and cementum volume fractions, pore architecture, and bone microarchitecture) were quantified. As the joints matured, bone and cementum volume fractions increased in both the HF and SF groups but along significantly different trajectories, and these changes correlated with a pronounced decrease in PDL-space from 12 to 16 weeks in both diets. With further aging, older HF rats maintained significantly wider PDL-spaces than SF rats. These evolving physical features were accompanied by an age-dependent significant increase in the contact ratio in the SF group. The DAJ stiffness was significantly greater in SF than HF animals at younger ages, indicating that food hardness-dependent remodeling alters the relative contribution of structural versus material factors to joint function across the life course. At the tissue level, volumetric strains, representing overall volume changes, and von Mises bone strains, representing shape changes, increased with age in HF and SF joints, with volumetric strain rising rapidly from 16 to 20 weeks and von Mises strain increasing sharply from 12 to 16 weeks. Bone in SF animals exhibited higher and more variable strain values than age-matched HF bone, and changes in joint space, degrees of freedom, contact area, and bone strain correlated with joint biomechanics, demonstrating that multiscale functional biomechanics, including bone strain in intact DAJs, are colocalized with anatomy-specific physical effectors. Together, these spatiotemporal shifts in primary (structure/form), and secondary features (material properties and microarchitecture) define divergent mechanobiological pathways for the DAJ and suggest that altered loading histories can bias joints toward early maladaptation and potential degeneration. Full article