Timely vaccination effectively reduced COVID-19 hospitalizations and mortality, yet vaccination hesitancy undermined this benefit. Understanding the factors contributing to hesitancy is critical for improving future pandemic control by identifying barriers to timely vaccination. This paper operationalizes hesitancy in terms of vaccine delay-a key public health metric that reflects changing vaccination policies and infection status, factors that can alter individuals' eligibility, and real-world complexities like infections. Using longitudinal data from the earliest stage of the pandemic in Hong Kong, we examined how institutional trust and the 5C constructs (confidence, complacency, constraints, calculation, and collective responsibility) influenced both vaccination intention and timing. Our results show that only 34.89% and 42.97% of vaccinated participants received their first and third doses within 100 days of eligibility, respectively, despite rising uptake prior to government mandates. Confidence and vaccination intention are key predictors of delay, and higher institutional trust boosts both confidence and collective responsibility, thereby enhancing intention and reducing delays. These findings underscore the importance of building institutional trust and public confidence to minimize vaccine delay, particularly among vulnerable populations. Ultimately, incorporating vaccine delay as a key metric into public health strategies can guide more effective interventions and strengthen pandemic preparedness. Vaccination against COVID-19 has prevented many hospitalizations and deaths, yet delays in getting vaccinated have limited these benefits. We examined why people in Hong Kong postponed vaccination and proposed vaccination delay as an important metric for understanding vaccine hesitancy and rollout success. Using repeated survey data collected during the pandemic, we analyzed how trust in health authorities and psychological factors—such as confidence, perceived barriers, and social responsibility—affected both vaccination intention and timing. We found that higher trust and confidence were linked to earlier vaccination. Recognizing vaccination delay as a key public health measure can help governments design strategies that build trust, encourage timely uptake, and improve preparedness for future pandemics.
Dementia is a multifactorial and debilitating condition marked by cognitive decline and behavioral changes that compromise independence and daily activities. This condition is a growing challenge in Brazil, and early identification of associated factors can guide preventive strategies and health policies. To build a dementia classification model for middle-aged and older adults Brazilians combining variable selection and multivariable analysis, using low-cost variables, including variables potentially modifiable and non-modifiable sociodemographic variables. Observational study employed a cross-sectional design and a classification modeling approach to estimate probable dementia and analyze the odds of dementia, using data from the Brazilian Longitudinal Study of Aging, involving 9,412 participants. Dementia was determined based on neuropsychological assessment and informant-based cognitive function. Analyses were performed with Random Forest (RF) and multivariable Logistic Regression (LR). The prevalence of dementia was 9.6%. The highest odds of dementia were observed in illiterate individuals (Odds Ratio (OR) = 7.42; 95% Confidence Interval (CI): 4.04-13.62), individuals aged 90 years or older (OR = 11.00; 95% CI: 5.05-23.95), low weight (OR = 2.11; 95% CI: 1.12-3.97), low handgrip strength (OR = 2.50; 95% CI: 1.09-5.76), self-reported black skin color (OR = 1.47; 95% CI: 1.07-2.00), physical inactivity (OR = 1.61; 95% CI: 1.25-2.08), self-reported hearing loss (OR = 1.65; 95% CI: 1.16-2.37), and presence of depressive symptoms (OR = 1.72; 95% CI: 1.36-2.16). In contrast, higher education (OR = 0.44; 95% CI: 0.21-0.94), greater life satisfaction (OR = 0.72; 95% CI: 0.52-0.99), and being employed (OR = 0.78; 95% CI: 0.61-1.00) were protective factors. The RF model outperformed LR, achieving an area under the ROC curve of 0.776 (95% CI: 0.740-0.811), with sensitivity of 0.708, specificity of 0.702, precision of 0.201, Precision-Recall Area Under the Curve (PR-AUC) of 0.261 (95% CI: 0.217-0.319), F1-score of 0.311, G-means of 0.705, and accuracy of 0.703. The findings reinforce the multidimensional nature of dementia and the importance of accessible factors for supporting screening/triage and prioritization in primary care. Strengthening public policies focused on promoting brain health can contribute significantly to the efficient allocation of resources in primary care and dementia prevention in Brazil.
Polygenic risk scores (PRSs) improve prediction of the development of type 2 diabetes over the use of clinical risk factors alone; however, they perform poorly in populations of non-European ancestry, limiting their global clinical utility. We aimed to deliver comprehensive and rigorously tested multi-ancestry PRSs for prediction in type 2 diabetes. We conducted meta-analyses using data from type 2 diabetes genome-wide association studies (GWAS) across cohorts from five major global ancestries: European, African or African American, Admixed American, South Asian, and East Asian. We used summary statistics from the GWAS to construct single-ancestry PRSs (using the continuous-shrinkage PRS-CS method) and multi-ancestry PRSs (using the PRS-CSx method), and constructed ancestry-specific linkage disequilibrium panels to model pairwise correlations between single-nucleotide polymorphisms in GWAS during PRS construction. Models were validated for association with type 2 diabetes in at least four independent cohorts per ancestry. The effect sizes of PRSs were estimated as the odds ratio (OR) per SD of the PRS, and ORs for individuals at the 90th, 95th, and 97·5th PRS percentiles were compared with the IQR as a reference. We also tested our PRS models for prediction of diabetes incidence with or without additional clinical factors, as well as microvascular complications and comorbidities. Our analysis used data from 409 959 individuals with type 2 diabetes and 1 983 345 controls: respectively, 359 819 and 1 825 729 indivduals were included in the GWAS dataset, with 10 992 and 31 792 individuals in the training dataset and 39 148 and 125 824 individuals in the validation dataset. The best predictive performance for the single-ancestry PRSs was in European (incremental AUC 0·07-0·14) and East Asian (0·02-0·16) ancestries, whereas prediction was poorer for African or African American (0·02-0·03), Admixed American (0·02-0·04), and South Asian (0·02-0·04) ancestries, correlating with sample sizes in the GWAS. Compared with single-ancestry PRSs, our multi-ancestry PRSs showed higher effect sizes and smaller 95% CIs across all ancestries: OR per SD 1·73 (95% CI 1·67-1·80) in African or African American, 2·82 (2·67-2·97) in Admixed American, 2·45 (2·36-2·54) in East Asian, 2·36 (2·32-2·41) in European, and 2·23 (2·05-2·42) in South Asian ancestries. Individuals in the 97·5th PRS percentile had a 3-7 times increased risk of type 2 diabetes compared with those in the IQR (OR 3·43 [95% CI 2·80-4·21] in African or African American, 7·47 [5·64-9·89] in Admixed American, 6·62 [5·58-7·85] in East Asian, 6·25 [5·72-6·82] in European, and 4·50 [2·70-7·53] in South Asian ancestries). These PRSs were also associated with earlier onset of type 2 diabetes, higher risk of developing microvascular complications, and provide additional predictive value beyond clinical factors. In individuals with type 2 diabetes, the association between multi-ancestry PRSs and risk of microvascular complications and comorbidity was studied in populations of African, Admixed American, and European ancestries and was significant in all three ancestry groups for diabetic retinopathy (ORs per SD 1·28-1·57), diabetic nephropathy (1·25-1·58), proliferative diabetic retinopathy (1·39-2·08), and end-stage diabetic nephropathy (1·44-1·87); PRS was associated with coronary artery disease in the Admixed American ancestry group only (1·16 [95% CI 1·08-1·25]). These validated, publicly available PRSs can improve risk stratification for type 2 diabetes onset and complications across diverse ancestries, supporting their further evaluation in clinical settings. The National Human Genome Research Institute of the US National Institutes of Health.
This observational study was designed to establish and validate a stigma prediction model for patients with polycystic ovary syndrome (PCOS). The stigma risk scoring table for overweight and obese patients with PCOS has good predictive ability. When an overweight or obese patient with PCOS presents, the prediction model allows clinic staff to rapidly grade hirsutism, acne, and acanthosis, determine fertility desire, and quantify anxiety. Low-risk patients then receive standard care, whereas high-risk patients receive precision interventions. Unlike the traditional approach, this clinical prediction model incorporates not only laboratory values but also body-image concerns and psychological well-being, providing more comprehensive management for women with PCOS. To preliminarily explore the associations among stigma, PCOS signs, anxiety, and depression. A total of 124 overweight and obese patients with PCOS were selected using convenience sampling. The patients in the order of clinic visit time were divided into a modeling set and a validation set at a ratio of 3:1. Univariate analysis was first performed, normally distributed continuous variables were compared using t-tests, non-normally distributed continuous variables with nonparametric rank-sum tests, and categorical variables with χ2 tests. Independent risk factors for stigma were identified using multivariable logistic regression, and a nomogram was constructed. The model's discrimination and calibration were evaluated with the receiver operating characteristic curve and calibration curve. Internal validation was subsequently conducted on the validation data set to assess model performance comprehensively. Hirsutism (odds ratio [OR]=0.075, 95%Cl: 0.015-0.368) , acne (OR=0.210, 95%Cl: 0.050-0.878) , acanthosis nigricans (OR=0.184, 95%Cl: 0.044-0.073) , fertility requirements (OR=0.212, 95%Cl: 0.051-0.890) , and anxiety (OR=1.217, 95%Cl: 1.074-1.378) were independent influencing factors for stigma in these patients (P < .05). The constructed prediction model also demonstrated good predictive ability, with area under the curve values of 0.941 and 0.803 for the modeling and validation sets, respectively. Internal validation using 1000 bootstrap resamples revealed a mean area under the receiver operating characteristic curve area under the curve of 0.941.
To meet the demands of high-rise buildings for long-span spatial layout, low-carbon design and energy conservation, and to break through the application limitations of reinforced concrete open-web sandwich slab structures in high-rise buildings, this study proposes a structure similar to the box-type structure, namely the reinforced concrete open-web sandwich slab-column (RCOSSC) structure. Presently, there is insufficient research evidence to verify whether the RCOSSC can satisfy the overall seismic performance requirements of buildings. To address this issue, this paper verifies the seismic performance of the material and structure through scaled structural tests and multi-software modeling verification. Additionally, the damage resistance of the structure and material is validated via both experimental and simulation methods. Through comparative modeling analysis, the RCOSSC structure maintains its integrity under rare earthquakes, with the inter-story drift angle not exceeding the collapse limit. Engineering simulations confirm that it meets the Grade C high-performance seismic standard, remaining elastic under frequent earthquakes, avoiding collapse under rare earthquakes, and featuring controllable damage. This study provides an industrialized and green structural solution for high-rise long-span buildings.
This study explores the valorization of ceramic waste (CW) and waste tire particles in the development of eco-friendly cementitious tiles for outdoor roof shielding applications. CW, sourced from industrial byproducts and demolition debris, offers promising hydraulic properties and cost-effectiveness. Two waste samples, collected during the renovation of sanitary facilities in an aged building and waste iron powder (WIP) were incorporated into cement formulations comprising Portland cement, fine aggregates, water, and recycled materials. The waste components were characterized via particle size distribution analyses which was found in the order of 34.15 μm for waste wall ceramic while it was 49.06 μm for waste floor one. The chemical composition analysis using X-ray fluorescence (XRF) was measured. The bulk density after a cure period of 28 days, water absorption was also evaluated. The compressive strength and flexural strength data revealed enhancement by the addition of WIP particles. This enhancement is attributed to the strong interfacial bonding between WIP particles and the cementitious matrix. Powder X-ray diffraction was used to measure crystalline phase composition. The results demonstrate that ceramic and rubber wastes reduce density and increase water absorption due to enhanced porosity, while the inclusion of WIP significantly improves matrix densification, mechanical strength, and electrical conductivity. Composites containing 10 wt% WIP exhibited optimal performance, achieving enhanced compressive and flexural strengths. Electrical conductivity measurements revealed values ranging from 10− 13 to 10− 11 S/cm, aligning with the requirements for antistatic applications. Consequently, the tiles are recommended for use as antistatic roof shielding materials. Besides, Electromagnetic interference (EMI) shielding tests demonstrated that samples incorporating a metal mesh achieved attenuation levels exceeding 20 dB, effectively blocking over 99% of incident electromagnetic waves. Further enhancement was observed with the addition of waste conducting particles (WIP), suggesting that composites integrating both WIP and metal mesh can achieve EMI shielding efficiencies up to 99.999%, making them suitable for industrial and commercial applications demanding high-performance shielding. The developed tiles comply with Egyptian and European standards for external cement tiles, demonstrating their suitability for sustainable construction applications, particularly for roofing and flooring in environments exposed to electromagnetic pollution. This work highlights an effective pathway for converting multiple waste streams into high-value, multifunctional building materials.
The prevalence of obesity varies by racial and ethnic group in the U.S., with non-Hispanic Black and Hispanic adults having higher rates than non-Hispanic White and Asian adults. Unhealthy diet, sedentary lifestyle, and short sleep duration are associated with greater likelihoods of obesity. Using the 2022 National Health Interview Survey Public Use Sample Adult File, this study constructed measurements of obesity, diet, physical activities, walk, and sleep duration. Multivariable logistic regressions were performed for each racial/ethnic group to assess associations between behavioral factors and the likelihood of obesity, adjusting for demographic and socioeconomic characteristics. Obesity prevalence was the highest in non-Hispanic American Indian and Alaska Native (44.0%; 95% CI=38.2%, 50.1%), followed by 42.7% (95% CI=40.5%, 44.9%) in non-Hispanic Black adults. Non-Hispanic Asian adults reported the highest fruit and vegetable intake than other groups. Non-Hispanic American Indian and Alaska Native adults had diets low in fruits and vegetables and high in sugar-sweetened beverages. One of 4 adults who are non-Hispanic Whites or other/multiple races met the recommendations for strength-building and aerobic activities. More than half of Hispanics failed to meet either physical activity criterion. More than one third of non-Hispanic American Indian and Alaska Natives, Blacks, and other/multiple races reported short sleep duration. Adjusted odds of obesity were 1.359 (95% CI=1.167, 1.581) higher for Hispanics, 1.338 (95% CI=1.240, 1.445) higher for non-Hispanic Whites, and 1.285 (95% CI=1.087, 1.519) higher for non-Hispanic Blacks with <7 hours of sleep than for their counterparts of the same race/ethnicity with adequate sleep. Although there is a strong association between obesity and behavioral factors, the effects vary substantially across racial/ethnic groups, adjusting for demographic and socioeconomic characteristics. Policies and programs addressing obesity should consider the heterogeneous effects of behavioral factors on obesity among racial/ethnic groups.
Epidemiological evidence on prenatal exposure to household environmental factors (gas stoves, mold, and dampness) and adverse birth outcomes remains limited and inconsistent. We examined associations of these exposures with gestational duration and fetal growth in 11,483 mother-infant dyads from 31 sites in the United States Environmental influences on Child Health Outcomes (ECHO) Cohort. Participants reported presence of a gas stove, mold/mildew (outside the shower/bathtub), and water damage in the home where they lived for the longest duration during their pregnancy (2000-2023). Gestational age at birth and birth-weight-for-gestational-age z-scores were primarily abstracted from medical records and analyzed continuously and categorically. We used covariate-adjusted linear or logistic regression models with a random effect for site to examine primary associations. We conducted exploratory analyses to assess effect modification by maternal race/ethnicity, education, hood/fan presence/use, and home construction year. Among participants, 53% reported having a gas stove; 13% reported mold/mildew; and 15% reported water damage. Overall, these exposures were not associated with birth outcomes. Several instances of effect modification were observed, particularly between mold/mildew and water damage and shorter gestation. Adverse associations with mold/mildew were more pronounced in newer homes (early-term odds ratio [OR] = 1.35, 95% confidence interval [CI]: 0.98-1.84) and among mothers with higher educational attainment (preterm OR = 1.44, 95% CI: 1.01-2.06). Conversely, the inverse association between water damage and preterm birth was stronger among non-Hispanic White participants (OR = 0.70, 95% CI: 0.50-0.97). This large multi-site study observed no consistent associations between the measured household environmental exposures during pregnancy and gestational duration or fetal growth.
This single-center cross-sectional study quantified segmental hemodynamic parameters of carotid bifurcation plaques by integrating high-frame-rate Vector Flow (V-Flow) imaging, Plaque-Reporting and Data System (Plaque-RADS) grading and clinical factors, to identify determinants of symptomatic status and enhance risk stratification of carotid atherosclerosis. From Feb-Aug 2025, 160 consecutive patients with carotid bifurcation plaques were enrolled. Symptomatic patients had ipsilateral transient ischemic attack/ischemic stroke within 180 days. B-mode ultrasound and V-Flow quantified wall shear stress (WSS), oscillatory shear index (OSI), and time-averaged turbulence intensity (TATur) at proximal/middle/distal plaque segments; plaques were Plaque-RADS-graded. Associations with symptomatic status were analyzed via multivariable logistic regression. Model performance was evaluated using ROC curve analysis and decision curve analysis; interobserver reproducibility was assessed using Bland-Altman analysis. Compared with the asymptomatic group patients (n = 98), symptomatic patients (n = 62) were older and had higher prevalence of hypertension, coronary heart disease, elevated triglycerides, and greater plaque length/thickness. They exhibited more Plaque-RADS grades 3-4, higher OSI/TATur at middle segments, and increased WSSmax/WSSmean, OSI, and TATur at distal segments. Multivariable analysis identified triglycerides, plaque length, TATur-Mid, WSSmax-Distal, and Plaque-RADS as independent predictors. The combined model (clinical + Plaque-RADS + V-Flow) achieved the highest discrimination (AUC = 0.826) and outperformed the base model. V-Flow-derived hemodynamic metrics, particularly OSI and TATur in middle/distal plaque segments, are strongly associated with symptomatic carotid disease. Combined with Plaque-RADS and clinical factors, these parameters enhance prediction of high-risk plaques, support individualized risk stratification, and may help identify patients who could benefit from closer surveillance.
Atrial fibrillation (AF) recurrence remains a challenge after Cryoballoon ablation (CBA). To date, few studies have investigated the impact of immunosenescence, particularly exhausted T cells, on AF recurrence after CBA. This study aims to investigate the association between exhausted T cells and AF recurrence after CBA, and to develop a novel model for the prediction of AF recurrence. Clinical data of patients undergoing CBA treatment for AF at Tianjin Medical University Second Hospital from March to November 2022 were collected, and AF recurrence was followed up. Flow cytometry was employed to evaluate the levels of inhibitory receptors (IR) on exhausted T cells, including killer cell lectin-like receptor G1 (KLRG1), programmed death-1 (PD-1), T cell immunoglobulin and mucin domain-containing molecule 3 (Tim-3), lymphocyte activation gene 3 (LAG-3), CD27, and CD57. Variables selected through Lasso regression analyses were incorporated into a multivariable Cox proportional hazards model to validate and identify independent risk factors for AF recurrence, building two predictive models. The efficacy of the two models was assessed using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). Cox multivariate regression analysis revealed that KLRG1 of CD8+ T central memory cell (Tcm), PD-1 of CD8+ naive T cell (Tnaive), mean corpuscular volume (MCV), γ-Glutamyl Transferase (γ-GGT), glucose, and mitral valve maximum blood flow velocity (MV Vmax) were significant predictors. Two models were developed based on Cox multivariate regression analysis. Model 1 includes MCV, γ-GGT, glucose, and MV Vmax, while Model 2 includes all risk factors: MCV, γ-GGT, glucose, MV Vmax, KLRG1 of CD8+ Tcm, and PD-1 of CD8+ Tnaive. Validation of both models revealed that Model 2 showed better predictive performance, and a nomogram was created to present the results visually. The KLRG1 of CD8+ Tcm and PD-1 of CD8+ Tnaive are novel independent risk factors for AF recurrence after CBA and play a significant role in the early recurrence of AF. We constructed a new predictive nomogram incorporating KLRG1 of CD8+ Tcm and PD-1 of CD8+ Tnaive as key variables, which can enhance the predictive value for AF recurrence in patients after CBA surgery.
The aim of this study was to build and test a nomogram model integrating clinical factors and multimodal MRI radiomics features to predict the risk of cervical lymph node metastasis (CLNM) in oral tongue squamous cell carcinoma (OTSCC) patients in different tumor stages. A total of 326 enrolled patients made up group Ⅰ (including T1WI and FS-T2WI) and group Ⅱ (including T1WI, FS-T2WI, and CE-MRI). Patients were divided into a training set, an internal test set, and an external test set. Radiomics features from the three MRI sequences and clinical factors were extracted and selected. Radiomics models, clinical models, and the combined models (nomogram models) integrating clinical factors and multimodal MRI radiomics features were constructed, respectively, in the two groups. cN0 subgroup and cT1-T2 subgroup analyses were performed. The combined model integrating clinical factors and multimodal MRI radiomics features in group Ⅱ produced a better performance than group Ⅰ. cN0 subgroup and cT1-T2 subgroup analyses also confirmed a superior performance of the combined model in group Ⅱ compared with group Ⅰ. The combined model based on clinical factors and multimodal MRI radiomics features including T1WI, FS-T2WI, and CE-MRI has potential utility in predicting the risk of CLNM in OTSCC patients.
Photodynamic therapy (PDT) is a clinically approved therapeutic modality that uses photosensitizers (PSs) to generate reactive oxygen species (ROS) upon light irradiation, enabling disease treatment with minimal invasiveness and excellent spatiotemporal precision. Despite these advantages, conventional PDT is fundamentally constrained by the mismatch between its oxygen dependence and the intrinsically hypoxic tumor microenvironment, which markedly compromises therapeutic outcomes. In this context, type I PSs offer a promising solution because they can produce cytotoxic radicals through electron transfer pathways, thereby reducing dependence on oxygen (O2) and improving efficacy under hypoxic conditions. Organic framework materials have recently emerged as powerful and versatile platforms for constructing type I PSs, owing to their programmable structures, high porosity, and efficient photoinduced charge separation and electron transfer. Importantly, the modular nature of these frameworks enables rational tuning of both structural motifs and compositional building blocks, allowing systematic regulation of light absorption, redox properties, and ROS generation pathways to maximize type I PDT performance. Moreover, organic frameworks can simultaneously function as nanocarriers for therapeutics, facilitating co-delivery and synergistic combinations (e.g., chemotherapy, immunotherapy, or catalytic therapies) that may achieve more durable and comprehensive tumor control. However, current studies remain fragmented, and there is still a lack of an integrated and mechanistically grounded overview that connects framework design principles with type I ROS generation mechanisms and performance optimization strategies. To address this unmet need, this review provides a comprehensive summary of the design strategies, mechanistic insights, and recent progress in organic framework-based type I PSs. We first outline the fundamental principles of type I photochemistry and the key physical and chemical processes underlying type I PDT. We then highlight rational design and modulation strategies to enhance optical properties, promote charge separation, and strengthen oxygen independence. Next, we summarize representative in vivo/in vitro disease models to demonstrate emerging diagnostic and therapeutic applications. Finally, we discuss current challenges and future opportunities for clinical translation, offering practical guidance for the development of next-generation phototherapeutic agents based on these innovative framework systems.
Physical resilience is a term used to describe an individual's response to physical and physiological stressors across the fields of gerontology and rehabilitation sciences. However, the term has not yet undergone a rigorous examination of its underlying assumptions, which has hindered its theoretical development and empirical measurement. This paper reviews the existing definitions of physical resilience in the context of human physical and psychological systems to examine its underlying, implicit assumptions. The aim is to clarify the conceptual foundations of physical resilience, identify its necessary components, and propose a formal ontological framework based on them. We conducted a scoping review of peer-reviewed literature using databases including PubMed and Web of Science. Definitions were extracted from experimental and conceptual papers and analyzed for shared themes and implicit assumptions. These ranged from general notions of recovery or adaptation to specific models of post-perturbation performance trajectories. Across studies, outcomes included whole-body, physiological, and occasional psychological or cognitive measures. However, the term physical was rarely explicitly defined, making it unclear how it modifies the term resilience. Only two studies referenced formal models of resilience, and few distinguished resilience from related constructs like adaptation or robustness. Findings revealed a lack of conceptual coherence in the current literature. We propose that physical resilience may be understood as an emergent, time-dependent disposition at the whole-body level involving interactions across multiple biological systems and scales. We suggest that a formal ontological framework of resilience can help clarify perturbation, recovery, and domain-specific contributions to support consistent measurement and interdisciplinary integration.
Predicting response to induction chemotherapy (IC) in advanced laryngeal cancer (LC) remains a clinical challenge. This study aimed to develop a non-invasive, interpretable model integrating CT radiomics and clinical features to predict chemotherapy outcomes. We retrospectively analyzed 161 advanced LC patients treated with IC. From pre-treatment CT images, 1,321 radiomics features were extracted, and a radiomics score (Rad-score) was constructed using LASSO regression. Transcriptomic analysis explored the biological basis of Rad-score. Independent predictors were identified via multivariate logistic regression and used to build five machine learning models. Model performance was evaluated using AUC, accuracy, and specificity. SHAP analysis was applied to interpret the optimal model. Four robust radiomics features were selected to construct the Rad-score. The Rad-score demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.715 in the training set and 0.707 in the validation set. In multivariate analysis, the Rad-score (Odds Ratio [OR]=2.89, 95% CI: 1.29-6.48, P = 0.010), gap invasion and validation were identified as independent predictors of chemotherapy response. Among the machine learning models, the Random Forest model achieved the best performance, yielding an AUC of 0.914 in the training set, 0.856 in the validation set, and 0.810 in the external test set. Decision curve analysis confirmed the clinical utility of the model. SHAP analysis confirmed Rad-score and fat space invasion as core predictors, with synergistic effects. We developed a highly accurate and interpretable Random Forest model that integrates radiomics and clinical features to predict IC response in advanced LC. This tool enables precise risk stratification and personalized treatment decisions, sparing non-responders from ineffective therapy. Prospective studies are needed to validate its clinical utility.
Rapid digitalization in the United Arab Emirates (UAE) has reshaped everyday life, yet existing scholarship has not fully captured how digital practices are embedded within broader sociotechnical and political-economic transformations. In particular, there is a need for a framework that integrates cultural dispositions with structures of power, governance, and market logics. This paper develops a conceptual framework to explain how digital practices are produced, structured, and experienced in the UAE, with a focus on the transformation of everyday life. The study adopts a theoretical and interpretive approach, building on the concept of digital habitus derived from Pierre Bourdieu's theory of habitus. It synthesizes insights from governmentality (associated with Michel Foucault), surveillance capitalism (as articulated by Shoshana Zuboff), and scholarship on Gulf state modernity to construct an integrated analytical framework. The paper proposes a hybrid framework that conceptualizes digital habitus as shaped by the interaction of state governance, corporate digital infrastructures, and culturally specific forms of modernity. It highlights how everyday digital practices in the UAE are simultaneously enabled and constrained by systems of surveillance, data extraction, and state-led modernization, producing distinct patterns of behavior, identity formation, and social interaction. By integrating cultural, political, and economic dimensions, the proposed framework advances understanding of sociotechnical transformation in the UAE and offers a foundation for future empirical research on digital life in non-Western contexts.
As the food packaging industry advances towards green and low-carbon solutions, developing sustainable, high-performance materials is essential to mitigate white pollution and enhance food safety monitoring. This review systematically summarizes the preparation strategies for transparent cellulose films, covering physical methods, chemical modification, and green dissolution-regeneration systems. It focuses on analyzing the regulatory mechanisms of different preparation routes with respect to transparency, mechanical properties, and functional characteristics. Building on this basis, this paper proposes the research concept of physicochemical structural regulation of cellulose, which emphasizes the precise optimization of cellulose's aggregated-state structure, surface chemistry, and interfacial properties. This approach aims to enhance the comprehensive performance of film materials, constructing a new generation of "efficient, stable, and multi-scenario adaptable" intelligent packaging. Furthermore, functionalization strategies such as in situ growth, composite loading, electrospinning, and layer-by-layer self-assembly are elaborated, and their mechanisms for endowing films with antibacterial, antioxidant, UV-shielding, and intelligent response properties are discussed. Despite their advantages, challenges regarding wet stability, the trade-off between transparency and functionalization, and large-scale production bottlenecks remain. Finally, the review outlines future directions-including green processing, multifunctional integration, and industrial scalability-to accelerate the transition of cellulose-based intelligent packaging from laboratory research to commercial application.
The past decade has been a critical period for Oral Emergency Medicine in China, marked by a transition from decentralized practice to systematic development and from experience-based care to guideline-oriented approaches. With the establishment of the Society of Oral Emergency Committee of Chinese Stomatological Association in 2016 as a milestone, the discipline oral emergency medicine has achieved remarkable progress in academic development, clinical services, personnel training, and scientific research. These achievements include the establishment of a discipline connotation tailored to Chinese specific conditions, continuous improvement in diagnostic and therapeutic standards and service accessibility, steady advancement in standardization, the initial formation of a professional workforce, and a significant increase in international influence. Meanwhile, the discipline still faces challenges, including an underdeveloped theoretical framework, imbalanced regional resource allocation, a shortage of specialized professionals, and imperfect multidisciplinary collaboration mechanisms. Drawing on international practices related to oral emergencies and the development experience of emergency medicine in China, this article proposes future goals for discipline construction: establishing a clear disciplinary status, building a systematic theoretical framework, improving the clinical service chain, strengthening the personnel training mechanism, and building scientific research platforms. These efforts aim to advance oral emergency medicine in China from initial establishment to mature development, providing robust support for the"Healthy China"strategy. 过去10年是中国口腔急诊医学从分散实践到系统发展、从经验主导到规范引领的关键时期。以2016年中华口腔医学会口腔急诊专业委员会成立为里程碑，我国口腔急诊医学在学科建设、临床服务、人才培养及科学研究等领域取得显著成就，确立了符合国情的学科内涵，诊疗水平与服务可及性持续提升，规范化建设稳步推进，人才队伍初具规模，国际影响力显著增强。同时，学科发展仍面临理论体系尚未形成、区域资源配置不均衡、专业人才储备不足、多学科协同机制不完善等挑战。本文借鉴国际口腔急诊相关实践及国内急诊医学发展经验，提出未来学科建设目标：明确学科定位、构建系统理论体系、完善临床服务链条、健全人才培养机制、搭建科研创新平台，以推动中国口腔急诊医学从初步形成走向成熟发展，为“健康中国”战略提供有力支撑。.
In this paper, we study how Psychological Capital (a higher-order construct of hope, self-efficacy, resilience and optimism) affects Financial Wellbeing of working-age adults in China, where Financial Behavior is the mediator. Based on cross-sectional data of 508 valid subjects recruited by Wenjuanxing online platform, we used a two-step Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4, treating Psychological Capital as a second-order notion and weighting the population weighted by national proportions to increase representation. We found that Psychological Capital does indeed influence Financial wellbeing (β = 0.563, p < 0.001), but also indirectly affects Financial wellbeing via Financial Behavior (β = 0.081, p < 0.004), with the same mediation pathway as hypothesized. Weighted data revealed similar structure of effects with slight decrease due to the small effective sample size. The model has solid reliability and discriminant validity and good predictive performance (Stone-Geisser's Q 2_predict > 0.10; Standardized Root Mean Square Residual (SRMR) = 0.028; Normed Fit Index (NFI) = 0.973) and a smoothness across gender, education, and age. We also find that it is consistent across gender/education and age groups. All of these results support Psychological Capital to be a potentially flexible psychological tool that can positively affect financial wellbeing directly and in a more adaptive manner. This suggests that interventions embedding resilience-building, hope-enhancing, and self-efficacy-strengthening components within financial education may help individuals cultivate more secure long-term financial outcomes. By embedding Psychological Capital within a behavioral explanation framework, we complement the model of financial well-being and provide one of the first population-weighted PLS-SEM studies on the relationships between Psychological Capital and Financial Behavior in China.
The intrinsic programmability of nucleic acids has positioned them as versatile molecular building blocks for constructing nanodevices with significant diagnostic and therapeutic potential. However, the clinical translation of these constructs is severely hindered by major pharmacokinetic (PK) and biophysical limitations, including susceptibility to enzymatic degradation, short circulation half-life, and inefficient cellular uptake. Chemical modification, encompassing nucleobase engineering, backbone and sugar-ring alterations, terminal conjugation, and higher-order structural reinforcement, provides a powerful strategy to overcome these barriers by enhancing in vivo stability, prolonging circulation, improving cellular internalization, and enabling stimulus-responsive cargo release. In this review, we summarize recent advances in chemically modified nucleic acid nanodevices, focusing on how specific chemical designs modulate physicochemical properties, improve pharmacokinetics, enable organ- or cell-selective targeting, and enable spatiotemporally controlled molecular release. We further highlight their emerging applications in precision drug delivery, high-sensitivity biosensing, and integrated theranostics. Finally, we critically discuss persistent translational challenges, including batch-to-batch scalability, immunogenicity, and long-term nanotoxicity, and propose forward-looking solutions, such as AI-assisted design, to pave the way for industrial adoption and clinical implementation.
Recent advancements in radiance fields, particularly with the emergence of Gaussian splatting, have highlighted their significant potential for 3D scene reconstruction and novel view synthesis. However, existing methods encounter substantial challenges when addressing dynamic environments, especially in complex urban settings with both rigid and non-rigid participants. To tackle these challenges, we propose a geometry-aware framework that integrates Gaussian primitives with a template mesh to effectively represent dynamic objects. This integration facilitates the efficient and accurate reconstruction of urban scenes, ensuring that the geometric integrity of dynamic elements is maintained. We first decompose the scene into a dynamic scene graph and fit the template vertices to observations to construct topologically consistent 3D models. Then, we build Gaussian radiance fields for dynamic nodes based on the template meshes, optimizing the vertex offset of dynamic participants to align with their geometric surfaces. We further project the appearance attributes into the 2D texture space based on topological relationships preserved in the Gaussians, enabling finer reconstruction of small-scale details and smoother appearance generalization on unseen surfaces. To validate the effectiveness of our proposed method, we conduct extensive evaluations on the Waymo Open Dataset (Ettinger et al., 2021) and the KITTI Dataset (Geiger et al., 2013). Our results demonstrate superior performance compared to mainstream dynamic reconstruction methods. We believe our work establishes a foundation for more realistic and geometrically complete urban scene reconstruction.