Search Results (6,114)

Even with the rise in concern over suicide and drug overdose mortality in the United States, gaps in research on at-risk populations still exist. The purpose of this study was to investigate disparities in suicide and drug overdose mortality risk among Washington State workers. Using 2014–2023 Washington State death records and American Community Survey (ACS) data, we calculated rates of suicide and drug overdose mortality by usual industry and occupation while adjusting for age and stratifying by sex and race/ethnicity. We compared the mortality risk among workers in specific industries and occupations to all workers within the same sex and race/ethnicity strata to understand how work differentially affects risk. Working in Construction & Extraction occupations was associated with an increased risk for suicide and drug overdose death for males across all race/ethnicity categories and for drug overdose death among White females. The suicide risk for Asian/Pacific Islander males had the largest increase—Asian/Pacific Islander males working in Construction & Extraction occupations had a rate 4.59 times higher than all Asian/Pacific Islander male workers. The Education, Training, & Library occupation group had significantly lower crude rates and rate ratios. Although the causal pathways that may lead someone to die by suicide or drug overdose are complex, understanding risk profiles among different industries and occupations may lead to more appropriate prevention strategies. Full article

Objective: This study examined the relationships between the developmental stages of children aged 6–72 months and their parents’ psychological resilience and psychological well-being. Method: The study was designed as a descriptive and correlational survey. The sample included a total of 184 children aged 6–72 months, as well as their parents, who visited Sinop Atatürk State Hospital. Data were collected face-to-face using the Personal Information Form, the Early Developmental Stages Inventory (EGE), the Psychological Well-being Scale (PWBS), and the Psychological Resilience Scale for Adults (PRS). The data were analyzed using the independent samples t-test, Pearson correlation coefficient, and simple and multiple linear regression analyses. Results: According to the study’s results, most children (92.4%) were found to be at a normal level in terms of overall development; importantly, personal-social skills had the highest rate of developmental delay (19.0%). Comparative analyses showed that the psychological resilience and psychological well-being scores of parents of children with typical development were higher than those of parents of children with developmental delays. Correlation analysis revealed a moderate positive relationship (r = 0.330, p < 0.001) between the Early Developmental Stages Inventory score and the Psychological Well-being Scale, and a weak positive relationship (r = 0.154, p < 0.05) between the Psychological Resilience Scale and the Early Developmental Stages Inventory score. Regression analyses suggest that psychological well-being and resilience are associated with child development outcomes. Discussion and Conclusions: The findings of this study indicate that there are meaningful associations between parents’ psychological well-being and resilience levels and children’s early developmental outcomes. Children whose parents reported higher psychological well-being and resilience tended to demonstrate more favorable developmental profiles in several developmental domains. These results suggest that parental psychological characteristics may be relevant factors associated with early childhood developmental outcomes and should be interpreted within broader ecological and contextual frameworks. Full article

Background: Pancreatic ductal adenocarcinoma (PDAC) has a profoundly immunosuppressive tumour microenvironment (TME) that limits anti-tumour immunity and contributes to resistance to immunotherapy. Although γδ T-cells can integrate innate and adaptive immune signals, their abundance, transcriptional states and regulatory pathways in PDAC remain incompletely defined. Methods: We performed integrated single-cell RNA sequencing analysis of 39 pancreatic tissue samples, comprising 33 PDAC tumours and 6 adjacent normal tissues. After dataset integration, immune cell annotation, and stringent per-cell gating, γδ T-cells were quantified and profiled for checkpoint-, ligand-, and chemokine-related programmes. Results: γδ T-cells were detectable across PDAC samples but showed substantial inter-sample heterogeneity in abundance. Among candidate inhibitory pathways, PDCD1, CD274, and HAVCR2 expression in γδ T-cells did not differ significantly between tumour and adjacent tissues, whereas KLRC1 (encoding NKG2A) showed a tumour-associated difference at the single-cell level, with a consistent directional pattern in sample-level summaries. NKG2A expression was comparable between γδ T-cells and NK cells, suggesting a shared inhibitory programme. HLA-E, the ligand for NKG2A, showed higher epithelial-cell expression in tumour than adjacent tissue in sample-level summaries (median 1.06 vs. 0.57; BH-q = 0.035). Chemokine analysis identified enrichment of CCL2, CCL4, CCL5, CXCL8 and CXCL12, with limited CXCL9/10/11 signalling within the PDAC TME. Within γδ T-cells, CXCR4 was the trafficking receptor, followed by CCR6, CCR7 and CXCR6. Conclusions: PDAC-infiltrating γδ T-cells show marked inter-sample heterogeneity and variable inhibitory and trafficking-related programmes. Integrated transcriptomic analysis nominates HLA-E–NKG2A as a candidate regulatory axis, with NK cells included as a biologically relevant comparator. Chemokine receptor patterns, particularly CXCR4, CCR6, CCR7 and CXCR6, suggest candidate trafficking features. These findings are hypothesis-generating and require spatial, protein-level and functional validation. Full article

The growing emphasis on environmental sustainability within the European Union raises important questions about the internal structure of corporate environmental effort. This study examines environmental expenditures (intermediate consumption of environmental protection services) and environmental investments (gross fixed capital formation for environmental protection) in ten EU member states over 2015–2022, using Eurostat Environmental Protection Expenditure Accounts data at both the national and sectoral levels (NACE Rev.2 sectors A, B, C, D). Two hypotheses are tested empirically. First, sectoral differences in the investment-to-expenditure ratio are statistically significant (Kruskal–Wallis H = 27.72, p < 0.0001): electricity, mining, and manufacturing each display a higher ratio than agriculture, with the most pronounced contrast for electricity. Second, Eastern European member states exhibit a systematically higher investment-to-expenditure ratio than the remaining countries in the sample (level difference: β = +1.01, p < 0.001), although the two groups follow parallel trajectories without convergence or divergence over the period examined. Building on the relative intensity of the two indicators, the study proposes a four-quadrant typology—active transformation, investment focus, maintenance model, and low-intensity profile—whose stability is confirmed by bootstrap resampling, sub-sample analysis, and an alternative deflator specification. The findings suggest that the internal composition of environmental effort is as informative as its overall level and that sectoral disaggregation is essential for characterising patterns of environmental effort in the EU. Full article

This article presents a multi-criteria comparative analysis of modern wall partitions in light-frame technology, with a focus on highly energy-efficient modular construction. The motivation for this research stems from the critical need to optimize building thermal insulation materials to minimize heat loss, while simultaneously ensuring low structural weight, rapid assembly, and hygrothermal safety in prefabricated systems. The aim of this study is to identify the most advantageous insulating materials and structural configurations by evaluating their thermal transmittance, moisture behavior, thermal dynamics, and fire resistance. The analysis encompassed four structural variants paired with seven types of advanced and conventional insulation materials. This comprehensive matrix allowed for the development of 28 computational models. Simulations were carried out for severe winter climatic conditions in Poland, utilizing the Ubakus software and conforming to the PN-EN ISO 13788, PN-EN ISO 6946, PN-EN 12524, and DIN 4108-3 standards. The simulations assumed strict steady-state boundary conditions for a 90-day condensation period, with an external profile of −14 °C/80% RH and an internal climate of 20 °C/50% RH. The evaluation focused on key physical and energy parameters, including the heat transfer coefficient (U-value), condensation risk, diffusion resistance, thermal phase shift, and partition weight. Quantitative findings reveal that the ventilated system with resol foam insulation (variant 4d) yielded the best overall performance, achieving a U-value of 0.089 W/(m²·K) W/(m²·K). The results confirm that the strategic selection of high-performance thermal insulation materials, coupled with structural thermal bridge mitigation, significantly enhances the energy efficiency, thermal stability, and moisture resistance of lightweight enclosures, establishing a comprehensive comparative framework for optimizing modular building envelopes. Full article

Low-temperature conditioning is a key procedure in the flexibility evaluation of waterproofing membranes and directly affects the reliability of subsequent performance assessments. However, the internal unsteady-state heat transfer kinetics and the thermal gradient evolution mechanisms in multi-layer composite membranes under transient cold shocks require further investigation. Focusing on commonly utilized 3 mm and 4 mm thick SBS (Styrene–Butadiene–Styrene)-modified bitumen waterproofing membranes as subjects, this study investigated the internal dynamic temperature fields and microstructural response of bituminous waterproofing membranes under standard low-temperature flexibility testing conditions. By accurately pre-embedding micro-temperature sensors in situ at the interface between the surface layer and the reinforcement matrix, the transient thermal response profiles of specimens with varying specifications in a −25 °C liquid environment were quantified. Simultaneously, a three-dimensional transient heat conduction finite element model was established to elucidate the dynamic evolution of internal spatial temperature gradients. The congruence between experimental and numerical results demonstrates that upon exposure to extreme cold, composite membranes of different thicknesses exhibit a pronounced “surface-to-core” heat transfer lag effect. The cooling rate maximized within the initial 10 min of exposure. Conversely, the internal interface layer—acting as a high-thermal-resistance zone and the most unfavorable point for heat conduction—necessitated 10~20 min of nonlinear thermal dissipation to stabilize at the target ambient temperature. This study clarifies the transient thermal response and temperature-field evolution laws of bituminous waterproofing membranes, providing a robust theoretical framework for elucidating low-temperature embrittlement mechanisms and informing the material design and application of waterproofing projects in cold regions. Full article

Preterm birth (PTB) remains a leading cause of neonatal morbidity and mortality and disproportionately affects Black women in the United States. While racial disparities in PTB are well documented, the molecular pathways underlying these differences remain incompletely understood. Extracellular vesicles (EVs) are circulating lipid-bound particles that carry coding and non-coding RNAs reflecting cellular stress states and may serve as integrative molecular indicators of pregnancy biology. In this hypothesis-generating pilot study, EVs were isolated from maternal plasma collected at delivery from non-Hispanic Black and non-Hispanic White women with preterm and full-term births. EV concentration and size were assessed, and EV-associated mRNA and miRNA cargo were profiled by next-generation sequencing (n = 5 per group), enabling differential expression and pathway enrichment analyses stratified by gestational outcome. EV concentrations were significantly elevated in PTB compared with full-term deliveries (p < 0.0001), with a greater increase among Black participants. Analysis of EV-associated mRNA transcripts identified a shared signature enriched for platelet activation and coagulation pathways across racial groups. Race-stratified analyses revealed distinct EV miRNA profiles in PTB, with enrichment of cytokine-mediated signaling pathways among Black participants and apoptosis-related pathways among White participants, while a subset of miRNAs differed by race independent of gestational outcome. These findings support EV profiling as a framework to investigate biological pathways contributing to PTB disparities. Full article

Background/Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous inflammatory disease in which eosinophilic subclassification is widely used for clinical stratification. However, it remains unclear how closely nominal histologic eosinophilic labels reflect the broader molecular organization of diseased tissue. Methods: We performed an inference-based integrative analysis of public datasets spanning discovery single-cell RNA sequencing (scRNA-seq), independent scRNA-seq validation, GeoMx digital spatial profiling, and bulk transcriptomic replication cohorts. A sample-level molecular burden framework was constructed using four dimensions: type 2 inflammation, epithelial injury/remodeling, extracellular-matrix remodeling, and barrier/defense impairment. Composite burden and component-level features were then examined across nominal eosinophilic categories, epithelial states, spatial compartments, and independent bulk cohorts. Results: Nominal eosinophilic labels were directionally informative but incompletely concordant with molecular burden. In the discovery cohort, eosinophilic CRSwNP samples were enriched toward the higher-burden end, whereas nominally non-eosinophilic CRSwNP samples extended across the intermediate-to-high burden range. Across discovery and validation scRNA-seq datasets, GeoMx spatial profiling, and independent bulk cohorts, the most reproducible burden-associated signals centered on epithelial injury/remodeling-like programs and related remodeling features. In the epithelial compartment, higher burden was associated with epithelial state reorganization, stronger wounding-associated activity, and trajectory-linked glandular/secretory remodeling. Independent validation and spatial analyses further supported epithelial wounding-, barrier-, and myeloid remodeling-related features, whereas type 2 context signals were directionally consistent but less uniform across platforms. In bulk replication, composite burden, epithelial wounding, and myeloid remodeling were more consistent across cohorts than type 2 context alone. Conclusions: Nominal eosinophilic labels in CRSwNP capture clinically relevant but incomplete information about underlying tissue biology. Epithelial injury/remodeling-like programs and remodeling-linked myeloid features emerged as the most stable organizational axes of molecular burden across public multimodal datasets. These findings support a graded, multidimensional view of CRSwNP and may complement, rather than replace, conventional pathology-based eosinophilic subclassification. Full article

Electron emission from hydrogen atoms induced by antiproton impact at intermediate energies is investigated using the one-centre Basis Generator Method within a semi-classical impact-parameter framework. The formulation employs a single-centre expansion of the time-dependent Schrödinger equation with a pseudostate basis consisting of hydrogenic orbitals acted upon by powers of a Yukawa-regularized potential, providing a compact and effective representation of the electronic continuum. Ionization probabilities are obtained by projecting the time-evolved wavefunction onto Coulomb continuum states, from which energy-differential cross sections (EDCS) are extracted. Exponential piecewise functions are constructed to interpolate between the pseudostate eigenenergies, yielding smooth EDCS profiles for each partial wave. The total EDCS, reconstructed by summing over all partial-wave contributions, exhibits good agreement with results from other pseudostate-based approaches. Full article

Background: Tirbanibulin 1% ointment has demonstrated short-term efficacy and excellent tolerability in the treatment of actinic keratosis (AK) on the face and scalp. However, data on long-term efficacy are still lacking. Materials and Methods: This prospective, single-center, 12-month extension study included patients with facial and scalp AKs previously treated with tirbanibulin 1% ointment once daily for 5 consecutive days. Long-term analysis was restricted to lesions that had achieved complete clinical and dermoscopic clearance at the 2-month follow-up. At 12 months, the treated areas were reassessed clinically and dermoscopically. High-resolution images obtained at baseline, 2 months, and 12 months were compared lesion by lesion to distinguish sustained clearance, recurrence at the same anatomical site, and the development of new AKs within the treated field. Results: Thirty-seven patients were reassessed at 12 months. Of the 228 AKs treated at baseline, 116 lesions had achieved complete clearance at 2 months and were therefore eligible for long-term evaluation. At 1 year, 70/116 lesions (60.3%) remained free of recurrence, whereas 46/116 (39.7%) relapsed. Sustained clearance was observed in 35/51 grade 1 lesions (68.6%), 32/57 grade 2 lesions (56.1%), and 3/8 grade 3 lesions (37.5%). In addition, 35 new AKs developed within the previously treated field. No delayed local or systemic adverse events and no progression to invasive cSCC were observed during follow-up. Patient-reported satisfaction was high, and 94% of patients stated they would be willing to repeat the treatment. Conclusions: Tirbanibulin was associated with sustained lesion clearance at one year, particularly in lower-grade AKs. While recurrence remains relatively common—especially in thicker lesions—the treatment was well tolerated and associated with no delayed adverse effects. Its short application regimen and excellent safety profile support tirbanibulin’s role in the long-term management of field cancerization. Full article

Triple-negative breast cancer is defined by the absence of druggable receptor targets and by a biologically dynamic phenotype that renders static, single-timepoint biomarker strategies fundamentally inadequate. Current predictive markers, including PD-L1 expression, tumor mutational burden, and genomic profiling, fail to capture the therapy-induced transcriptional reprogramming, spatial heterogeneity, and drug-tolerant persister states that drive resistance and relapse. In this review, we argue that RNA, particularly non-coding RNA (ncRNA), represents a complementary and state-aware platform for biomarker development in TNBC, capable of capturing transcriptional adaptation, regulatory threshold dynamics, and cell state transitions that static genomic markers cannot fully detect. Unlike messenger RNAs, which reflect active transcriptional programs, long non-coding RNAs and circular RNAs modulate the stability of state transitions and are specifically induced under conditions of therapeutic stress, immune exclusion, and drug tolerance, which are properties that make them suitable as potential early and sensitive indicators of adaptive reprogramming. We review the biological rationale for RNA as a state-aware readout across five dimensions: tumor plasticity, immune context, stress response, therapy adaptation, and microenvironment composition. An examination is conducted regarding how spatial transcriptomics can map RNA-defined resistant niches within TNBC, how serial liquid biopsy RNA measurements, including extracellular vesicle RNA and circulating tumor RNA, enable temporal monitoring of transcriptional state shifts before radiologic progression, and what analytical and clinical standards deployable RNA assays must meet. Finally, a state-guided adaptive management framework is proposed in which RNA signatures function as iteratively updated measurement layers informing therapy selection, on-treatment monitoring, and early resistance detection. This review outlines trial design models and defines the validation standards required before RNA-guided adaptation can enter clinical practice. Full article

To address the urgent need for electronics operable in extremely high-temperature environments, this paper presents a novel three-terminal, dual-gate, lateral 4H-SiC n-channel depletion-mode junction field effect transistor (JFET) without a backside electrode. Featuring a fully planar electrode layout, the device eliminates the back-gate effect and significantly improves integration compatibility. Experimental results demonstrate stable DC operation up to 500 °C, with an intrinsic gain of 9.79 at room temperature and 6.01 at 500 °C. Comparison with TCAD simulations confirms excellent agreement in the key physical trends of threshold voltage drift and mobility degradation, though quantitative discrepancies are observed and attributed to process-induced parasitic effects such as non-ideal ohmic contacts and interface states. Analysis shows that the new structure broadens the channel depletion layer by optimizing the depletion profile, thereby suppressing channel-length modulation and improving both output resistance and gate control. This work not only provides an effective device platform for high-temperature 4H-SiC analog integrated circuits (ICs) but also deepens the understanding of process-performance correlations, offering clear guidance for process-oriented device optimization. The proposed structure serves as a foundation for developing fully planar, high-temperature 4H-SiC analog ICs with promising potential in aerospace, automotive, and energy exploration systems. Full article

Obesity is a major global health challenge characterized by chronic low-grade inflammation, oxidative stress, and an increased risk of cardiometabolic disorders. Although sex-related differences in inflammatory and redox biomarkers have been reported in obese populations, the molecular mechanisms underlying this heterogeneity remain incompletely understood. In this study, we applied a proteomics-based approach to investigate urinary extracellular vesicles from 45 obese individuals (BMI 30–40 kg/m²; age 50–70 years) in order to identify molecular signatures associated with metabolic dysregulation. Shotgun proteomics analysis performed by nanoLC–MS/MS enabled the identification of 3822 proteins. Hierarchical clustering of proteomic profiles revealed two distinct molecular groups, predominantly enriched in males (Group I) and females (Group II). Label-free quantitative analysis identified 466 differentially abundant proteins between the two clusters. Functional enrichment analysis highlighted pathways associated with immune response, metabolic regulation, and redox homeostasis, including glycolysis/gluconeogenesis, lysosome activity, leukocyte transendothelial migration, and glutathione, cysteine and methionine metabolism. Notably, proteins related to ferroptosis were enriched, suggesting the involvement of iron-dependent oxidative cell death mechanisms in the metabolic imbalance observed in a subset of subjects. Furthermore, the non-enzymatic glycosylation of urinary proteins was significantly higher in Group I compared with Group II (p = 0.0002), indicating increased formation of advanced glycation products in individuals with a more pronounced pro-oxidant state. Preliminary follow-up data suggested a higher incidence of pathological events, including cardiovascular complications, among individuals belonging to Group I. Overall, these findings demonstrate that urinary proteomic profiling can identify distinct molecular phenotypes among obese individuals and highlight oxidative stress, ferroptosis, and protein glycation as potential determinants of metabolic vulnerability, supporting the use of non-invasive proteomic approaches for improved risk stratification in obesity. Full article

Automated depression assessment is critical for scalable mental healthcare but faces dual challenges: the lack of clinical interpretability in “black-box” deep learning models and the excessive computational cost of large-scale fusion architectures. To bridge this gap, we propose NS-Dep-KAN, a novel neuro-symbolic framework that harmonizes DSM-5-guided reasoning with Kolmogorov–Arnold Networks (KANs). Our approach leverages a Large Language Model (LLM) to extract symbolic symptom evidence aligned with diagnostic criteria, which then guides the aggregation of multimodal features from frozen pretrained encoders (WavLM and Qwen). Unlike traditional Multi-Layer Perceptrons, the proposed KAN prediction head employs learnable B-spline activation functions to capture complex nonlinear symptom–severity mappings with extreme parameter efficiency. Evaluations on the DAIC-WOZ benchmark demonstrate that NS-Dep-KAN achieves state-of-the-art performance among audio-text models (MAE 2.69, 13.5% improvement over the three-modality baseline MSGAF at MAE 3.11), with only ∼4.9 K trainable parameters. Moreover, the framework offers inherent interpretability, revealing granular symptom contribution profiles that align with clinical intuition. This work establishes a path toward explainable trustworthy AI for mental health screening. Full article

Extracellular vesicle (EV) transcriptomic data provides a high-dimensional representation of cellular states but remains challenging to interpret due to noise, redundancy, and limited sample sizes. Most existing approaches rely on supervised differential expression analyses, which can be biased and may fail to capture latent structure in small datasets. In this study, we propose an unsupervised machine learning framework based on non-negative matrix factorization (NMF) to identify latent expression programs from EV mRNA profiles. A structured preprocessing pipeline combining expression filtering, variance selection, ANOVA-based feature selection, and correlation pruning was used to reduce dimensionality and improve signal quality prior to matrix factorization. NMF was applied to decompose the data into interpretable gene modules and sample-specific activation patterns. Model selection was performed using reconstruction error and component stability across multiple initializations. Candidate features were prioritized using a composite ranking score integrating module loadings, group-level expression differences, and model stability. The approach identified a stable low-rank representation capturing dominant patterns in the data and a compact set of informative features. These results demonstrate that unsupervised matrix factorization can effectively extract structured, interpretable signals from small-scale transcriptomic datasets and provide a robust framework for feature prioritization and representation learning in high-dimensional biological data. Full article