Search Results (690)

Mobile health units (MHUs) can reach populations facing barriers to traditional primary care, but information about factors associated with their utilization is limited. The objective of this ecological study was to evaluate whether MHU encounter density is increased in census tracts designated as Primary Care Health Professional Shortage Areas (HPSAs) and explore whether associations varied by socioeconomic vulnerability. We analyzed Wayne State University/Wayne Health MHU encounters with adult patients from July 2021 to September 2025. Negative binomial regression models with a log link and log(population) offset tested the a priori hypothesis that encounter density was increased in designated versus undesignated HPSA census tracts. Sensitivity analyses assessed variation by social vulnerability index score quartiles established by the US Centers for Disease Control and Prevention. One quarter of the five-county metropolitan Detroit, Michigan, catchment area census tracts were designated healthcare shortage areas. Overall, 13,852 encounters with 10,924 unique patients occurred across 924 of 1305 census tracts. Encounter rate per adult population was significantly increased by severalfold comparing designated versus undesignated shortage areas, with stronger associations at lower socioeconomic vulnerability index score quartiles (interaction p = 0.0006). These findings support continued efforts to scale and evaluate MHUs to address projected healthcare shortages, particularly in socioeconomically vulnerable areas. Full article

Contemporary vehicle development, particularly for overactuated platforms, demands design methodologies that bridge the gap between high-level performance targets and hardware selection. Existing physics-based models, while essential, offer limited utility for this systems-level design task. This paper introduces a novel analytical framework for vehicle lateral dynamics, predicated on a reformulated single-track model that integrates the concept of tire-specific slip. The derived specific slip-based bicycle model enables a comprehensive frequency-domain analysis of handling characteristics, articulated through three fundamental metrics: the front and rear axle specific slips and the vehicle wheelbase. Our results quantify the influence of these parameters on key handling attributes, including stability, responsiveness, and roll susceptibility. This work provides a constitutive tool for the model-based design of next-generation vehicles, enabling the a priori selection and optimization of chassis hardware to meet predefined performance objectives and informing the synthesis of advanced motion control systems. Full article

This paper presents a physics-based hazard model for catastrophe (CAT) modelling of urban flood risk—a first step toward a complete CAT modelling framework. We introduce a linear second-order ordinary differential equation (ODE) system to simulate the underlying mechanisms of water accumulation, absorption, routing, and drainage across interconnected surfaces in densely built urban areas. The model treats an urban zone as a multivariate network of surfaces, each with unique hydrological properties, linked by directed water flows. For risk analysis, the external meteorological forcing (representing the precipitation input) is randomised. Our risk-analysis protocol relies on a Monte Carlo simulation of stochastic forcing. Its reliability is founded on rigorous mathematical properties proven for the ODE system (existence, uniqueness, positivity, monotonicity, and a priori bounds), ensuring that the probabilistic outputs are well-defined and physically plausible. A three-surface example illustrates the framework and a complete risk analysis is performed, yielding concrete risk metrics that inform mitigation strategies. Computational efficiency is shown to be optimal for linear ODE systems, outperforming generic methods. This work provides a foundational, physics-informed hazard model for next-generation CAT models, directly supporting the insurance industry’s adaptation to climate change. Full article

Human epidermal growth factor receptor 2-positive (HER2+) breast cancer is an aggressive subtype associated with a poor prognosis when not optimally treated. Over the past year, major advances—including results from DESTINY-Breast05, DESTINY-Breast09, DESTINY-Breast11, PATINA, and long-term APHINITY follow-up—have changed the treatment landscape regarding the place in therapy of antibody–drug conjugates and the optimal sequencing of systemic therapies. These developments prompted the need for updated evidence-informed consensus recommendations to support consistent, high-quality care across Canada. Research Excellence, Active Leadership Canadian Breast Cancer Alliance (REAL Alliance), comprising clinical-academic oncologists from across Canada and Breast Cancer Canada, updated its 2024 HER2+ recommendations through a modified Delphi process with up to three rounds of anonymous voting. Consensus was defined a priori as ≥75% agreement. This 2025 update incorporates new data in early-stage, metastatic, and central nervous system-involved disease, including revisions to neoadjuvant and adjuvant treatment pathways and expanded guidance on the clinical use of antibody–drug conjugates. Full article

The use of Large Language Models (LLMs) in organizational contexts has grown rapidly, particularly in project management activities. Despite this expansion, a relevant methodological gap can be observed in the literature: the absence of psychometrically validated instruments capable of measuring the acceptability of these technologies prior to their effective adoption, especially in project-oriented governance contexts. Traditional technology adoption models predominantly focus on a posteriori assessment of individual use, providing limited support for prospective analyses that inform strategic decision-making and organizational coordination mechanisms. In response to this gap, this study aims to develop and validate a psychometric scale to indirectly measure the acceptability, through outcome beliefs and with behavioral predispositions serving as structural proxies of the latent construct of LLM use by project management teams, with a focus on a priori judgments that precede the effective adoption of the technology. The initial scale, composed of 17 items, underwent content validation and was administered to a sample of 154 project management professionals. The latent structure was examined through Exploratory and Confirmatory Factor Analyses, resulting in the refinement of the instrument to 13 items distributed across two correlated factors. The results indicate that LLM acceptability is adequately represented by a bidimensional structure comprising the dimensions Intention/Predisposition and Trust/Perceived Benefit, both demonstrating high internal consistency and good statistical fit, and nomological validity evidenced by significant associations with respondents’ self-reported LLM usage frequency. These findings reinforce the conceptualization of acceptability as a prospective and multidimensional construct, relevant for supporting governance decisions and the adoption of artificial intelligence-based technologies in project-oriented organizational systems. The indirect measurement approach adopted here is theoretically grounded in the premise that a priori acceptability is not directly observable but is constituted by cognitive and dispositional beliefs formed prior to use. Full article

Existing general image inpainting works require the user to customize a mask to indicate the region to be inpainted. However, the mask is often hard to calibrate accurately in real-world applications, e.g., graffiti removal. Blind image inpainting aims to automatically restore the degraded image into the visually reasonable one without a priori mask to indicate the area to be repaired. So far, most proposed blind inpainting methods convert the task into general inpainting by predicting the mask before inpainting. However, these methods are highly dependent on mask prediction results, which may produce inferior inpainting results if the prediction is inaccurate. To address this issue, we propose a two-stage blind inpainting framework with two novel designs: (1) cross-space contrastive learning, to remove the noise in the degraded images and realize the automatic inpainting in the latent space by reducing the distance of the degraded images and the corresponding complete images in the latent space; and (2) mask-aware adversarial training, to minimize the mutual information between the inpainted feature and the noise. Extensive experiments prove that our blind inpainting framework performs better on multiple datasets than the state-of-the-art methods. Full article

Here we perform a detailed information-theoretic (IT) analysis of atomic electron densities in the periodic table, from hydrogen (Z = 1) to lawrencium (Z = 103). By use of the Shannon entropy, the Fisher information and the disequilibrium functionals in both position and momentum spaces as fundamental descriptors of the atomic densities, the periodic table can be represented in a three-dimensional information space as a continuous, highly ordered manifold. The analysis shows that chemical periodicity naturally emerges as a helicoidal manifold (reminiscent of a helix) at the coordinates of a 3D theoretic-information space (Shannon, Fisher, Disequilibrium), with each period forming one segment within the continuous global trajectory. We find information-theoretic signatures of shell structure, sub-shell filling, and electron-configuration anomalies, such as the familiar irregularities seen in chromium and copper. Therefore, the helicoidal character emerges naturally and is not imposed a priori. Further, through the uncertainty principle of the complementary analysis in momentum space, more insights are gained by exposing maximal information-theoretic differentiation for lighter atoms and compression among heavy elements. Notably, momentum-space analysis reveals that hydrogen occupies a natural intermediate position between helium and lithium based on kinetic energy distribution—contrasting with IT position-space results that emphasize hydrogen’s unique delocalized electron density. Indeed, the 3D IT representation of the elements in position space aligns with the view that H does not belong to either the alkali metals or the halogens, but rather stands as a unique, standalone element. This complementary perspective provides new quantitative support for understanding hydrogen’s dual chemical nature, providing new quantitative insight into ongoing debates about hydrogen’s optimal periodic table position. Furthermore, by considering triadic relationships and complexity properties in relation to the López–Mancini–Ruiz (LMC) and Fisher–Shannon (FS) functionals, we show that atomic complexity increases monotonically along with nuclear charge, and we provide a quantitative measure of how organized atomic electron densities are distributed throughout the periodic system. Based on our IT analyses, the fundamental character of periodicity could be addressed by employing helicoidal representations that highlight the characteristics of hydrogen, while simultaneously preserving the autonomy of the blocks of elements. Full article

Background: Drug-induced sleep endoscopy (DISE) with a jaw-thrust maneuver is used to simulate mandibular advancement in obstructive sleep apnea (OSA), yet determinants of functional airway improvement remain incompletely defined. Objective: To identify clinical, polysomnographic, and baseline DISE anatomic factors associated with jaw-thrust responsiveness. Methods: We conducted a single-center retrospective observational study of adults with polysomnography-confirmed OSA who underwent DISE with paired baseline and jaw-thrust VOTE assessments between 1 January 2015 and 31 December 2025 (n = 355). Jaw-thrust responsiveness was defined a priori as a within-subject reduction in the number of obstructed VOTE sites (grade ≥ 1). Multivariable logistic regression was used to identify independent correlates within a prespecified explanatory modeling framework. The study was approved by the Institutional Review Board of Taipei Tzu Chi Hospital (protocol 14-IRB079), with the need for informed consent being waived. Results: Jaw thrust reduced overall obstruction burden from two (two to three) to one (one to two) sites (Wilcoxon p < 0.001). Hypopharyngeal levels demonstrated the greatest improvement, particularly at the tongue base (39.2% to 7.6%) and epiglottis (23.9% to 5.4%) (both p < 0.001). Overall, 62.8% met responder criteria and 18.9% achieved complete normalization. In multivariable analysis (n = 272), baseline tongue-base collapse (adjusted odds ratio [aOR] 2.46, 95% CI 1.20–5.04) and greater baseline multilevel obstruction burden (aOR 1.85 per SD, 95% CI 1.19–2.85) were independently associated with responsiveness, whereas conventional PSG severity metrics were not. Conclusions: In adults with OSA, jaw-thrust responsiveness during DISE is more strongly associated with baseline anatomic phenotype than with global PSG severity. Standardized DISE functional assessment may provide complementary information to support phenotype-informed selection of non-CPAP therapies, pending prospective validation. Full article

Multi-view clustering (MVC) is crucial for exploiting complementary information from multi-view data. Anchor-based MVC methods are efficient for large-scale tasks but lack the ability to balance view-specific local complementarity and cross-view global consistency. To address this issue, we propose GL4-MVC, a dual-level anchor graph learning framework. It constructs anchor graphs with integrated adaptive learning of view-specific local anchors and concatenated a priori cross-view global anchor guidance, with an orthogonal mapping matrix enabling cross-level alignment to ensure effective guidance of global information for local learning. GL4-MVC is scalable and suitable for large-scale data. Extensive experimental results confirm the effectiveness and efficiency of GL4-MVC. Full article

Electroencephalogram (EEG) methods for the diagnosis of Benign Epilepsy with Centrotemporal Spikes (BECTS) are reviewed. The focus is on procedures reported for EEG analysis and diagnosis in BECTS, since some recent and potential applications of artificial intelligence (AI) aim to enhance the diagnostic accuracy and time reduction process, thereby moving a step closer to advancing our knowledge of the electrical nuclei sources and dynamics of energy distribution through the scalp in patients with epilepsy. The advantages of AI classification techniques have an increasing publication rate in the specialist literature, with no clear agreement on methodology. Hence, a better understanding of the procedures, arguments, and achievements is needed. To achieve this goal, (1) we review the background knowledge of the clinical characteristics of BECTS, (2) we analyze the results and advantages of computational processing methods for source and connectivity analyses of EEG in BECTS, and finally, (3) we explore the AI methods published in specialized journals for BECTS analysis. In conclusion, we argue in favor of the combined use of a priori information, which is the basis of the clinical visual analysis of EEG, as a potential feature to be included in AI methods for the classification of epileptiform graphoelements in EEG in BECTS diagnosis. Full article

Evidence on modifiable post-diagnosis factors influencing outcomes after intravesical Bacillus Calmette–Guérin (BCG) therapy for high-risk non-muscle-invasive bladder cancer (NMIBC) is limited. In this exploratory, feasibility-focused prospective multicenter cohort (March 2023–November 2024), BCG-naïve patients completed repeated interviewer-administered 24 h dietary recalls; prespecified food groups, selected foods, and nutrients were screened for associations with 1-year intravesical recurrence using Firth’s penalized logistic regression adjusted a priori for age, sex, and total energy intake, with false discovery rate control within each exposure family. Forty-six patients were enrolled; 41 had evaluable recurrence status, including 8 recurrences (19.5%). Participants were predominantly overweight (mean body mass index (BMI) 28.4 kg/m²) and had low adherence to a Mediterranean dietary pattern (median Mediterranean Adequacy Index 2.25). No dietary exposure met the within-family false discovery rate threshold; the smallest q-value was 0.361. Nominal inverse associations were observed for leafy green vegetables (OR per 1 SD 0.385; 95% CI 0.101–0.972) and for energy-adjusted zinc (OR 0.280; 95% CI 0.069–0.802) and magnesium intakes (OR 0.260; 95% CI 0.045–0.872), but these did not remain significant after FDR adjustment. These exploratory signals warrant replication in larger, biomarker-informed cohorts incorporating dietary biomarkers and immune profiling during BCG. Given the limited sample size and low number of recurrence events, these findings are strictly hypothesis-generating and should not be interpreted as evidence of definitive protective or risk dietary factors. Full article

Based on its rich information of chemical specificity, Raman spectroscopy has been widely applied for in vivo biomedical investigations. For extracting quantitative information of target constitution, it is imperative to establish a robust model for unveiling the relationship between spectral features with/without priori references. By integrating a variety of traditional machine learning and artificial neural network algorithms, an integrated Raman spectra analysis toolbox (AI-Assisted Raman Spectra Analysis Toolbox [AI-Raman] V 1.0) was developed for spectral processing, model training, and regression analysis by using MATLAB R2024a. Besides the utilization of back propagation artificial neural network and convolutional neural network algorithms, classical machine learning algorithms, such as partial least squares regression and support vector regression, were also compacted as the supporting functions of presented toolbox. A spectral dataset obtained from nailfold from different subjects was utilized to evaluated the feasibility and performance of the developed software, which demonstrated that the analysis software can predict glucose concentrations by in vivo Raman spectral measurement. With a friendly graphics interface, the analytical model can be customized and optimized for accomplishing the desired objectives, which will benefit many Raman-based inventions, especially for biomedical transformations. Full article

Sustainable land use requires precise monitoring of soil pollution, yet accurately predicting the spatial distribution of heavy metals often relies on post hoc accuracy comparisons with limited a priori diagnosis. To address the challenge of cost effective environmental monitoring, we conducted a PRISMA guided systematic review (2000–2024) and synthesized 135 studies to develop a mechanism-informed, context aware method selection framework. Evidence revealed three regularities: (i) element–driver coupling is structured (Pb/Cd/Zn predominantly anthropogenic; Cr/Ni geogenic; As/Hg mixed), with dominant influence scales from local to regional; (ii) model performance hinges on alignment between algorithmic assumptions, and context hybrid machine learning models integrating multi-source covariates tend to excel under strong, non-stationary anthropogenic heterogeneity, whereas kriging variants are more robust when geogenic continuity holds; and (iii) applicability is jointly constrained by environmental context, data foundations, and management objectives. Building on these insights, we propose a three-step decision workflow—goal definition, contextual diagnosis, and method matching. This framework serves as a decision support tool that shifts selection from trial and error to a priori alignment, optimizing resource allocation and enhancing the reliability of pollution assessments for sustainable soil remediation and policymaking. Full article

Radiomics promises quantitative biomarkers extracted from routine hip imaging to support diagnosis, prognosis, and surgical planning, but current evidence is fragmented across pathologies, modalities, and computational pipelines. We conducted a scoping review following PRISMA-ScR and the Population–Concept–Context framework, including peer-reviewed original studies on adults (≥18 years) that applied radiomics or deep-radiomics to hip imaging (X-ray, CT, MRI, DEXA) with clinically relevant outcomes. PubMed (MEDLINE), Embase and Scopus (Elsevier) were searched from 1 January 2021 to 30 August 2025 and complemented by snowballing; screening and data charting were performed in duplicate. Given heterogeneity, findings were synthesized narratively by a priori clusters. In fragility/osteoporosis, opportunistic CT and radiograph-based models frequently achieved AUCs around 0.90–0.96, while DXA-radiomics added information beyond bone mineral density/FRAX and trabecular MRI provided complementary microarchitectural signals. For osteonecrosis of the femoral head, multisequence MRI enabled early diagnosis with AUCs > 0.94; radiomics differentiated transient bone marrow edema with AUCs~0.92–0.94 and predicted collapse using radiographs or MRI with AUCs~0.85–0.90, including automated pipelines with external validation around 0.85. In femoroacetabular impingement, 3D Dixon-MRI studies reported very high performance (~0.97–1.00) with preliminary multicenter generalizability and added value from periarticular soft-tissue features. In total hip arthroplasty, radiomics anticipated press-fit cup stability from preoperative radiographs (AUC~0.82) and predicted 6-month functional recovery using clinico-radiomic CT models (AUC~0.95). Across clusters, methodological robustness was variable (sample sizes, harmonization, leakage control, external/temporal validation, calibration, clinical utility). Radiomics for adult hip disorders shows tangible translational promise in opportunistic screening, complex differential diagnosis, and perioperative decision support, but broader clinical adoption will require multicenter datasets, IBSI-aligned standardization, transparent reporting of calibration and decision-curve analyses, and prospective validation. Full article

Background & Objectives: Quantitative ultrasound (QUS) radiomics extracts microstructure-sensitive spectral features from radiofrequency data and may provide contrast-free, early indicators of neoadjuvant chemotherapy (NAC) response in breast cancer. This review synthesized open access human studies evaluating QUS radiomics for a priori prediction and early on-treatment monitoring. Methods: Following PRISMA-2020, we included English, free full-text clinical studies of biopsy-proven breast cancer receiving NAC that reported QUS spectral parameters (mid-band fit, spectral slope/intercept) ± textures/derivatives and machine learning models against clinical/pathologic response. Data on design, RF acquisition/normalization, features, validation, and performance (area under the curve (AUC), accuracy, sensitivity/specificity, balanced accuracy) were extracted. Results: Twelve cohorts were included. A priori baseline models achieved accuracies of 76–88% with AUCs 0.68–0.90; examples include 87% accuracy in a multi-institutional study, 82% accuracy/AUC 0.86 using texture-derivatives, 86% balanced accuracy with transfer learning, 88% accuracy/AUC 0.86 with deep learning, and AUC 0.90 in a hybrid QUS and molecular-subtype model. Early monitoring improved discrimination: week-1 results ranged from AUC 0.81 to 1.00 and accuracy 70 to 100%, noting that the upper bound was reported in a small cohort using combined QUS and diffuse optical spectroscopy features, while week 4 typically peaked (AUC 0.87–0.91; accuracy 80–86% in observational cohorts), and one series reported week-8 accuracy of 93%. Across reporting cohorts, mean AUC increased with a 0.05 absolute gain. A randomized feasibility study reported prospective week-4 model accuracy of 98% and demonstrated decision impact. Conclusions: QUS radiomics provides informative a priori prediction and strengthens by weeks 1–4 of NAC, supporting adaptive treatment windows without contrast or radiation. Standardized radiofrequency (RF) access, normalization, region of interest (ROI)/margin definitions, and external validation are priorities for clinical translation. Full article