Henan University Huaihe Hospital and Huaihe Clinical Institute

In the field of camouflaged object detection (COD), effectively distinguishing the intrinsic similarity between objects and their backgrounds is a critical factor for improving detection performance. Existing approaches typically leverage boundary constraints to provide additional auxiliary information during the training phase. To capture more discriminative detailed cues, we introduce texture labels as supervisory signals and propose a context- and texture-aware hierarchical interaction network (CTHINet) for COD. In the coding phase, the network is divided into two separate branches, a context and a texture encoder. Specifically, a context encoder is employed to generate contextual information. Subsequently, the features at different scales are refined by implementing a Multi-head Feature Aggregation Module (MFAM). The diversity of features is subsequently enhanced by leveraging the interactions among their distinct feature receptive fields, facilitating the matching of candidate areas for camouflaged objects with varying sizes and shapes. Following this, the enhanced features are combined with texture features generated by the texture encoder, fully exploiting imperceptible cues within candidate objects through utilizing the Hierarchical mixed-scale Interaction Modules (HMIM). This module continuously integrates texture cues with contextual information within a single feature scale, aiming for more accurate detection. Extensive experiments conducted on three challenging benchmark datasets, e.g., CAMO, COD10K, and NC4K, illustrate that our model has superior performance compared to state-of-the-art methods. Furthermore, the evaluation results on the polyp segmentation dataset underscore the promising potential of CTHINet for downstream applications.

Acute coronary syndrome (ACS) is a high-risk disease among cardiovascular diseases. This study is to screen for lncRNA ceRNA regulatory network in acute coronary syndrome associated with percutaneous coronary intervention (PCI) and to validate the XIST/miR-212-3p/CALCOCO2/OPTN axes. lncRNA, miRNA, and mRNA expression microarray data were retrieved and downloaded from the GEO database. Dysregulated lncRNAs and miRNAs were collected using the GEO database. Target genes were predicted and subjected to KEGG pathway analysis. The serum levels of lncRNA XIST, miR-212-3p, CALCOCO2, and OPTN were detected by RT-qPCR. The H9C2 hypoxia-reoxygenation (H/R) injury model was established. The cell viability was detected. The related indicators of mitophagy were detected by flow cytometry and Western blot. Six PCI-related lncRNAs were identified in ACS, linked to 44 PCI-associated miRNAs and 159 PCI-associated mRNAs. KEGG pathway analysis suggested the mitophagy pathway, which involved XIST/miR-212-3p/CALCOCO2/OPTN axes. The levels of XIST, CALCOCO2, and OPTN were increased while the level of miR-212-3p was decreased in PCI patients. Taken together, our results suggest that lncRNA-miRNA-mRNA networks associated with PCI in ACS were presented. In the H/R cell model, XIST promoted mitophagy through miR-212-3p/CALCOCO2/OPTN. XIST can promote mitophagy through the miR-212-3p/CALCOCO2/OPTN axis, thereby alleviating myocardial cell injury.

BackgroundNurses' innovative behaviors play an irreplaceable role in advancing nursing practice, improving healthcare service quality, and driving transformation in the nursing profession. However, existing studies on the factors influencing nurses' innovative behaviors and their underlying mechanisms remain limited, necessitating further investigation.ObjectiveThis study investigated the impact of ethical leadership on nurses' innovative behaviors, examining the mediating roles of innovative climate and self-efficacy.Research designThis is a multicenter longitudinal study with three-wave data collection (February-August 2024) following STROBE guidelines. Structural equation modeling was employed to examine mediation effects.Participants and research contextA total of 1,522 nurses from 16 tertiary hospitals in China completed baseline assessments (T1), with 1,409 (T2) and 1,298 (T3) completing follow-ups. Participants were registered nurses with ≥1 year of experience, sampled through stratified cluster sampling across clinical departments.Ethical considerationsapproved by the Henan University Biomedical Research Ethics Committee (HUSOM2023-478). Participants provided informed consent, data were collected anonymously, and confidentiality was maintained throughout.ResultsEthical leadership significantly predicted innovative behavior (β = 0.334, 95% CI [0.284,0.386]). Both innovative climate (β = 0.059 [0.046, 0.072]) and self-efficacy (β = 0.017 [0.011, 0.023]) served as partial mediators, with a significant chain mediation effect (β = 0.021 [0.015, 0.027]) accounting for 4.87% of total effect.ConclusionEthical leadership not only directly promotes nurses' innovative behaviors but also strengthens this effect indirectly through the chain-mediated roles of innovative climate and self-efficacy. Enhancing ethical leadership can optimize the innovative climate in nursing departments and improve nurses' self-efficacy, thereby effectively fostering their innovative behaviors.

Sepsis is a leading cause of death in critically ill patients. This study aimed to investigate alterations in the gut microbiota and their mechanisms of action in a mouse model of sepsis. 20 mice were exposed to saline, while 70 mice were exposed to lipopolysaccharides (LPS). The diversity, structure, and composition of the gut microbiota were examined using 16S rRNA sequencing on days 1, 3, 5, and 7. Immunohistochemistry was used to measure the expressions of the tight junction proteins zonula occludens-1 (ZO-1) and occludin to assess intestinal barrier damage. An enzyme-linked immunosorbent assay (ELISA) was used to measure the serum levels of interleukin-17 (IL-17) and interleukin-23 (IL-23) to assess the degree of inflammation. Over time, mice exposed to LPS exhibited marked dysbiosis of the intestinal microbiota, characterized by changes in microbiota composition. This was primarily due to the colonization of pathogenic bacteria belonging to the phylum Proteobacteria, and a significant decrease in the relative abundances of beneficial bacteria belonging to the phyla Firmicutes and Bacteroidetes. Additionally, the structure of the intestinal microbiota in LPS-treated mice was altered, resulting in a significant reduction in diversity. Bacteroides was identified as a biomarker through genus-level LEfSe analysis. The expressions of colonic occludin and ZO-1 were significantly downregulated, while the level of IL-17 was elevated in LPS-treated mice. The results of this study suggest that the gut microbiota undergoes changes during a week of sepsis in mice and that microbiota dysfunction may be closely related to intestinal barrier dysfunction and changes in the IL-17/IL-23 axis.

This study systematically evaluated the predictive performance of 10 international warfarin dosing algorithms (originating from the United States, China, Singapore, Thailand, India, United Kingdom, Japan, and South Korea) in 87 Chinese patients, aiming to identify optimal algorithms for warfarin dose optimization. Clinical and genetic data were analyzed using mean dose error (MDE) and ideal dose prediction (IDP) rate metrics, with sensitivity analysis stratifying patients into low-dose (≤14 mg/week, n = 21), medium-dose (14-21 mg/week, n = 43), and high-dose (≥21 mg/week, n = 23) groups based on actual weekly maintenance dose (mean: 18.9 ± 8.8 mg/week). Results revealed significant variation in MDEs (-6.6 to 11.3 mg/week) across algorithms. The Chinese-developed Huang algorithm and Thai-developed Sangviroon algorithm demonstrated superior overall accuracy, both achieving MDEs <1 mg/week and IDPs >40%. In medium-dose patients, their performance was particularly robust (Huang IDP: 65.1%; Sangviroon IDP: 74.4%). However, both algorithms showed limitations at dose extremes: they overestimated doses in 90.48% of low-dose patients and underestimated doses in 60.9%-65.2% of high-dose patients. This evidence indicates that region-specific algorithms (Huang and Sangviroon) outperform internationally recommended models (e.g., IWPC/Gage endorsed by CPIC) for warfarin dosing in Chinese populations. Locally derived algorithms may thus offer greater clinical utility despite current international guidelines.

BackgroundThe rising prevalence of high body mass index (BMI) has become a critical driver of global oncologic morbidity and mortality, yet its pan-cancer burden remains poorly characterized across socioeconomic development strata. This study investigates the geographic, temporal, and sex-specific disparities in high BMI-attributable cancer burden, stratified by the Socioeconomic Development Index (SDI), to inform precision public health strategies. MethodsLeveraging the 2021 Global Burden of Disease (GBD) dataset, we analyzed age-standardized mortality, disability-adjusted life years (DALYs), and years of life lost (YLLs) for 17–23 countries across Asia and globally. SDI-stratified analyses evaluated temporal trends (2015–2021) and cancer-type contributions, while multivariable models assessed associations between income inequality (Gini coefficient), healthcare capacity, and metabolic risk exposure. ResultsMarked disparities emerged across SDI gradients: high-SDI nations exhibited 6.7-fold higher mortality rates (e.g., Malaysia: 4.40 vs. Bangladesh: 0.65/100,000) and concentrated burdens in colorectal (40.5% DALYs) and breast cancers (27.0% DALYs), contrasting with distributed burdens in low-SDI regions (no cancer > 15.6% DALYs). Gender disparities highlighted male predominance in liver (+ 8.4 DALY difference) and colorectal cancers (+ 5.1), while female-specific malignancies (e.g., uterine cancer) retained consistent burdens across SDI levels. Temporal analyses revealed accelerated DALY reductions in middle-SDI regions (-4.5% annual percent change [APC]) but rising breast cancer burdens in low-SDI settings (+ 1.2% APC). Economic inequality (Gini > 0.40) correlated with elevated mortality (Turkey: 123.1/100,000), independent of GDP, underscoring synergistic impacts of BMI and sociodemographic inequities. ConclusionHigh BMI-driven pan-cancer burden is profoundly shaped by SDI gradients, reflecting interactions between metabolic risk, healthcare access, and socioeconomic determinants. Tailored interventions—prioritizing colorectal and breast cancers in high-SDI regions and addressing systemic inequities in low-SDI settings—are critical to mitigating the dual burden of obesity and cancer in transitioning populations.