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Rising burden of pancreatic cancer in China: Trends, drivers, and future projections
Pancreatic cancer is one of the most lethal malignancies globally, with increasing incidence and mortality trends. In China, the disease burden has escalated over the past three decades, yet comprehensive national assessments remain limited. This study aims to evaluate the long-term trends, driving factors, and future projections of pancreatic cancer burden in China from 1990 to 2021. Data on incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) were extracted from the Global Burden of Disease (GBD) Study 2021. Temporal trends were analyzed using Joinpoint regression and age-period-cohort models, while decomposition analysis quantified the contributions of population aging, growth, and epidemiological change. Bayesian age-period-cohort (BAPC) models were applied to project disease burden trends through 2030. In 2021, pancreatic cancer accounted for 118,665 new cases and over 2.9 million DALYs in China, with a significantly higher burden among males. Age-standardized rates of incidence, prevalence, and DALYs increased markedly between 1990 and 2021, outpacing global trends. The burden was concentrated in older age groups and driven primarily by years of life lost. Joinpoint regression identified periods of accelerated increase after 2015. Age-period-cohort analysis revealed that disease burden rises sharply after age 50 and is highest among more recent birth cohorts. Decomposition analysis showed that population aging and epidemiological transitions were the main contributors to increased burden. Projections using BAPC models indicate that incidence, prevalence, and DALY rates will continue to rise through 2030. The burden of pancreatic cancer in China has increased substantially over the past three decades and is projected to rise further. These findings highlight the need for intensified public health interventions focused on prevention, early detection, and effective treatment strategies.
Investigation of wire arc additive manufacturing of cylindrical components by using cold metal transfer arc welding process
Pan-cancer analysis of oncogenic role of MAGOH and experiment validation in hepatocellular carcinoma
Bearing fault diagnosis based on cross image multi-attention mechanism
Evolutionary game analysis of the longitudinal integration of electronic health record based on prospect theory
Comparison of the 2021 ESC guideline with the HFA-PEFF and H2FPEF scores in diagnosing heart failure with preserved ejection fraction
A Pythagorean fuzzy MCDM model for evaluating career happiness in sports by selecting a suitable sport
Association between glucose-to-lymphocyte ratio and mortality in patients with heart failure from the MIMIC-IV database: a retrospective cohort study
Abstract The glucose-to-lymphocyte ratio (GLR) has been associated with prognosis in various inflammatory diseases. However, its relationship with mortality among critically ill patients with heart failure admitted to the intensive care unit (ICU) remains poorly understood. This study aims to assess the association between GLR levels and mortality in this population and to evaluate the prognostic predictive value of GLR. This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV3.0) database, encompassing heart failure patients admitted between 2008 and 2022. The GLR was calculated as fasting glucose (mg/dL) divided by absolute lymphocyte count (K/µL). To evaluate the association between GLR levels and 30-day and 365-day mortality risk in heart failure patients, we employed RCS analysis, multivariable Cox regression, K-M survival curves, subgroup analyses, and ROC curves. These methods were used to assess both the prognostic relationship and the predictive accuracy of GLR. This study included a total of 14,417 patients. The association between GLR levels and all-cause mortality was found to be non-linear. In our analysis, GLR was found to be an independent predictor of both 30-day (HR 1.57, 95% CI 1.45–1.70) and 365-day mortality (HR 1.48, 95% CI 1.40–1.56). K-M survival curve analyzes showed that patients with elevated GLR levels had worse survival outcomes than patients with lower levels. Furthermore, the predictive utility of GLR appeared to exceed that of glucose or lymphocyte counts alone. GLR may be a useful tool for early identification and treatment of high-risk populations in clinical practice and may also be a potential predictor of the prognosis of patients with heart failure.
Concentration gradient of plastic debris larger than 500 μm detected across the Southwest Indian ocean
Cryptic antigens flag cancer cells to T cell therapy
Structural and functional insights into Listeria monocytogenes phage endolysin PlyP100: A promising food safety tool
Local heterogeneous dipolar structures drive gigantic capacitive energy storage in antiferroelectric ceramics
Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy
Abstract Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes largely from tissue and cell models analyzed after fractionation, and that fail to reveal the spatial characteristics of cell metabolism, and how these aspects relate to the location of cells and organelles within tissue microenvironments. Here we show the application of multi-scale microscopy, machine learning-based image segmentation, and spatial analysis tools to quantitatively map the fate of nutrient-derived 13 C atoms across spatiotemporal scales. This approach reveals the cellular and organellar features underlying the spatial pattern of glucose 13 C flux in hepatocytes in situ, including the timeline of mitochondria-ER contact dynamics in response to changes in blood glucose levels, and the discovery of the ultrastructural relationship between glycogenesis and lipid droplets.
Tailored Polymer‐Zeolite Imidazolate Framework Membranes for Aperture‐Matched C4 Hydrocarbon Separation
Abstract The integration of metal‐organic frameworks (MOFs) with polymers for efficient C4 hydrocarbon separation membranes remains challenging, primarily due to inherent phase incompatibility. This work presents an in‐situ synthesis strategy for polymer‐zeolitic‐imidazolate frameworks (polyZIF), utilizing polymer‐metal‐ligand coordination bonds combined with structural directing agents to produce crystalline microporous frameworks. With an accessible BET surface area of 261 m 2 g −1 and a permanent porosity of ca. 4.42 Å, polyZIF membrane demonstrates butadiene permeance (∼332 GPU) and the selectivity over n ‐butene, n ‐butane, iso‐butene, and iso‐butane (17.2, 28.1, 21.5, and 34.8) under mixed gas conditions, respectively. Demonstrating practical scalability, the improved dispersibility and reduced interfacial defects in polyZIF facilitates fabrication of large‐area defect‐free membranes (up to 80 cm 2 ) while maintaining robust C4 separation performance. This advancement not only establishes a novel preparation for constructing polyZIF membranes with C4 hydrocarbon molecular‐sieving capability but also provides critical insights into industrial application of ZIF‐based polymer membranes.
How does social support influence autonomous physical learning in adolescents? Evidence from a chain mediation and latent profile analysis
Purpose This study examines how social support influences adolescents’ autonomous physical learning behavior, exploring the mediating roles of self-efficacy and exercise motivation, and the moderating effects of gender and behavioral typologies. The goal is to provide insights into how social support can enhance adolescents’ engagement in physical activities and inform intervention strategies. Methods A total of 2,359 junior high school students (1,208 males and 1,151 females; mean age = 13.21 ± 0.96 years) from three public schools in Shandong Province were surveyed between October and December 2024. Participants completed the Chinese versions of the Perceived Social Support Scale (PSSS), General Self-Efficacy Scale (GSES), Motivation for Physical Activity Measure–Revised (MPAM-R), and Autonomous Physical Learning Behavior Scale (APLBS). Data were analyzed using SPSS 25.0 for descriptive statistics, Pearson correlations, t-tests, one-way ANOVAs, and hierarchical regression; Mplus 8.3 for Latent Profile Analysis (LPA); and AMOS 27.0 for Structural Equation Modeling (SEM) with bootstrapping (10,000 resamples). Results indicated significant positive correlations among social support, self-efficacy, exercise motivation, and autonomous physical learning (all p < .01). Hierarchical regression showed that social support accounted for 27.3% of the variance in autonomous learning behavior (β = 0.524, p < .001), self-efficacy explained an additional 5.8% (β = 0.279, p < .001), and exercise motivation explained a further 4.0% (β = 0.219, p < .001), resulting in a total R2 = 0.373. LPA identified four behavioral profiles—Highly Engaged (26.3%), Positively Regulated (35.7%), Selectively Participative (22.2%), and Passively Participative (15.8%)—with the four-class model demonstrating optimal fit (entropy = 0.921; AIC = 37,951.9; BIC = 38,081.6; BLRT p < .001; LMR p = .0235). SEM results (CMIN/DF = 3.546; GFI = 0.970; CFI = 0.985; TLI = 0.976; NFI = 0.979; RMSEA = 0.060) showed that social support had a direct effect on autonomous learning behavior (β = 0.325, p < .001) and a total indirect effect of 0.191 (total effect = 0.516, p < .001). Specifically, self-efficacy mediated 22.19% (β = 0.114, 95% CI [0.093, 0.137]), exercise motivation mediated 11.18% (β = 0.057, 95% CI [0.043, 0.074]), and the chain pathway (social support → self-efficacy → exercise motivation → learning) accounted for 3.49% (β = 0.018, 95% CI [0.012, 0.024]) of the total effect. Multi-group SEM indicated that the SS → SE → EM mediation path was stronger for females (β = 0.127, 95% CI [0.104, 0.152]) than for males (β = 0.088, 95% CI [0.070, 0.109]). Across latent profiles, the full sequential mediation (SS → SE → EM → APLB) was significant for Positively Regulated (β = 0.074, 95% CI [0.051, 0.101]) and Selectively Participative (β = 0.066, 95% CI [0.042, 0.089]) groups, marginally significant for Passively Participative (β = 0.038, 95% CI [0.015, 0.062]), and non-significant for Highly Engaged (β = 0.013, 95% CI [–0.004, 0.031]). Conclusion These findings demonstrate that social support enhances adolescents’ autonomous physical learning both directly and indirectly through self-efficacy and exercise motivation. Psychological resilience processes accounted for approximately 37.1% of behavioral variance, and gender differences and latent profiles moderated these pathways. Interventions should therefore focus on strengthening perceived support to boost self-efficacy and motivation—especially among female and moderately engaged students—while tailoring strategies to each behavioral profile to foster sustained autonomous learning and lifelong physical activity engagement.Keywords: Machine Learning, Neural Networks, artificial intelligence.
FSID: a novel approach to human activity recognition using few-shot weight imprinting
Histopathological evaluation of the efficacy of carbon nanoparticle staining in detecting lymph node metastasis in colorectal cancer surgery
Synthesis of magnetic borosilicate zeolite/graphene quantum dots nanocomposites for removal of nitrate and organic pollutants from water
Abstract Natural and synthetic zeolites have been considered as capable candidates for removal of pollutants from water and wastewater due to their high surface area and porous structure. In this work, borosilicate zeolite (BZ) with ZSM-5 structure, graphene quantum dots (GQDs)/BZ and Fe3O4/BZ as two component, and Fe3O4-GQDs/BZ as three component nanocomposites were prepared using hydrothermal, solid state dispersion, and ultrasound-assisted co-precipitation methods. The prepared samples were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), field emission scanning electron microscopy (FE-SEM), energy dispersive X-Ray spectroscopy (EDX), N2 adsorption-desorption, and vibrating sample magnetometer (VSM) techniques. The synthesized composites were used for the removal of nitrate, methylene blue (MB), 4-nitrophenol (4-NPh), biological oxygen demand (BOD), and chemical oxygen demand (COD) from water and industrial wastewater. Among the studied adsorbents, the three-component nanocomposites demonstrated superior performance in removing nitrate and cationic and anionic dyes, achieving removal efficiencies of 85.5% for nitrate, 98.4% for MB, 91.0% for 4-NPh, 100.0% for BOD, and 60.0% for COD. The kinetics studies revealed that all adsorbents obey the pseudo-second-order kinetic model. The prepared new multifunctional magnetic nanocomposites composed of graphene quantum dots is expected to be promising adsorbents for the removal of pollutants from water and wastewater.