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Cardiac self efficacy and Mediterranean diet adherence in patients with coronary heart disease
Correction: Does menopause influence the association between atherogenic index of plasma and prediabetes? A cross-sectional study in middle-aged Chinese women
EphrinB2-engineered cPDLSCs/EPCs 3D cell sheets accelerate peri-implant osseointegration via coupled angiogenesis-osteogenesis regulation in canine model
Retraction: GATA4 inhibits cell differentiation and proliferation in pancreatic cancer
KDM8/c-Myc axis-mediated glucose metabolism reprogramming promotes the progression of ovarian cancer
Abstract Both KDM8 and c-Myc have been implicated in regulating tumor glucose metabolism. However, whether there is an interaction between KDM8 and c-Myc, and whether KDM8 function is dependent on c-Myc in ovarian cancer (OC) remains unclear. Paired cancerous and paracancerous tissues from five OC patients were analyzed for KDM8 and c-Myc expression using reverse transcription quantitative polymerase chain reaction and Western blot. Co-Immunoprecipitation assays were conducted to verify their potential interaction. Stable OVCAR3 and SKOV3 cell lines overexpressing KDM8 or c-Myc were established. Functional assays (CCK-8, Transwell, colony formation, wound healing, and flow cytometry) were performed to assess proliferation, migration, invasion, colony formation, and apoptosis. Metabolic changes were evaluated by measuring glucose uptake and lactate accumulation using colorimetric and ELISA kits, respectively. Finally, a nude mouse subcutaneous xenograft model was constructed to observe the growth and metabolic levels of OC in vivo. Both mRNA and protein levels of KDM8 and c-Myc were significantly upregulated in ovarian cancer (OC) tissues compared to paracancerous tissues. Furthermore, a direct interaction between KDM8 and c-Myc was identified. Functionally, KDM8 and c-Myc synergistically promoted the malignant behavior of OC cells, including enhanced proliferation, migration, invasion, and colony formation capacities. Additionally, they promoted metabolic reprogramming, as evidenced by increased glucose uptake and lactate accumulation, while concurrently inhibiting apoptosis. However, siRNA-mediated knockdown of c-Myc significantly attenuated these oncogenic effects, reversing the enhanced proliferative, migratory, and metabolic capacities of OC cells. In vivo experiments further verified that the KDM8 /c-Myc axis affects OC growth and metabolic levels. Collectively, our findings indicate that KDM8 and c-Myc cooperate to promote OC progression, which may be mediated through the regulation of glucose metabolism. Notably, KDM8 function is partially dependent on c-Myc in this context. These results provide preliminary evidence supporting KDM8 as a potential candidate target for OC diagnosis and treatment, with further validation required in larger cohorts.
Retraction: Do natural environmental protection, regional innovation climate, entrepreneurs’ cognition of green development positively influence the sustainable development of small rural businesses
Study on the vehicular load effect and the influence of top plate longitudinal cracks on box girder chamber based on static load test
Opposing patterns in eating behaviors following bariatric surgery versus lifestyle-induced weight loss
Objective This study aimed to investigate 12-month changes in eating behaviors and metabolic outcomes following bariatric surgery or lifestyle-induced weight loss. Methods This study is a longitudinal secondary analysis comparing data from two independent prospective cohorts: bariatric surgery (n = 19) and lifestyle-induced weight loss intervention (n = 19). Body weight, body composition (Dual-energy X-ray absorptiometer – DEXA), and metabolic parameters (blood samples, Oral Glucose Tolerance test – OGTT) were measured, and eating behaviors were assessed using validated questionnaires (Three-Factor Eating Questionnaire, Dutch Eating Behavior Questionnaire, Binge-Eating Scale) at baseline, at 5–6 months, and at 12 months after the intervention initiation. Results Bariatric surgery produced greater weight loss (surgery −28.1 ± 8.1 kg vs lifestyle −8.9 ± 7.9 kg; p < 0.001) and larger improvements in metabolic markers than lifestyle-induced weight loss. Despite these differences, eating-behavior trajectories diverged. Bariatric surgery was from baseline to 12 months associated with stable or decreased eating restraint, whereas lifestyle-induced weight loss led to an increase in restraint (cognitive restraint: surgery −0.05 ± 0.7 vs lifestyle +6.4 ± 0.9; p < 0.001; restrained eating: surgery −0.7 ± 0.1 vs lifestyle +0.6 ± 0.2; p < 0.001). Both interventions reduced disinhibited eating, binge eating, and external eating. Hunger-related outcomes also improved in both interventions, but with different signatures: surgery was accompanied by reduced hunger perception and cue-reactivity, whereas lifestyle-induced weight loss was characterized by increased reliance on deliberate cognitive/behavioral control strategies. When changes in eating behaviors were analyzed per 1% body weight lost, the opposing pattern in restraint remained significant, and the lifestyle group showed a larger increase in restraint and a greater relative reduction in susceptibility to hunger compared with surgery. Exploratory item-level patterns supported these profiles, suggesting fewer cue-driven triggers to overeat after surgery and greater use of conscious restraint strategies after lifestyle-induced weight loss. Conclusions Lifestyle-induced weight loss was associated with increased cognitive restraint, whereas bariatric surgery resulted in stable or decreased restraint. Both interventions decreased hunger sensations, likely through adaptive learning in the lifestyle group and physiological changes after surgery.
Optimization of train real-time dynamic scheduling under unexpected events in high-speed railway based on MAPPO
Association between sleep duration and sarcopenic obesity: The mediating role of hemoglobin level
Background Sarcopenic obesity (SO) is a major global health concern linked to sleep duration. This study examined the association between sleep duration and SO in middle-aged and older Chinese adults, with hemoglobin level as a mediating factor. Methods This research examined data from the 2015 China Health and Retirement Longitudinal Study (CHARLS), focusing on individuals aged 45 years and older. We investigated the link between sleep duration, hemoglobin level, and SO using multivariate logistic regression, adjusting for confounders. A mediation model assessed hemoglobin’s role in the sleep duration-SO relationship. Nonlinear relationships were explored with restricted cubic spline fitting, and likelihood ratio tests evaluated threshold effects. Subgroup analysis examined group heterogeneity. Results After adjusting for covariates, both sleep duration and hemoglobin level were negatively associated with SO. For each unit increase in sleep duration, the association with SO decreased by 9% (OR = 0.91, 95%CI: 0.87–0.95, p < 0.001). For each unit increase in hemoglobin level, the association was reduced by 25% (OR = 0.75, 95%CI: 0.71–0.80, p < 0.001). Hemoglobin had an indirect effect on the relationship between sleep duration and SO, accounting for 11.01% of the total effect variation (−1.20 × 10⁻³, 95%CI: −2.20 × 10⁻³ to 3.30 × 10⁻⁴). Conclusion The study found that both sleep duration and hemoglobin level were negatively associated with SO, and hemoglobin level mediated the relationship between sleep duration and SO.
Transcriptomic study of WSSV infection in Litopenaeus vannamei lymphoid organ via single nuclei RNA sequencing
Background Aquaculture is the fastest growing farmed food sector and is a key part of global food security. Crustacean aquaculture, while being one of the most profitable sectors, is threatened by pathogenic diseases such as white spot disease (WSD), which results in severe stock losses and threatens animal health and welfare. In this study, we used novel techniques to study the impact of white spot syndrome virus (WSSV), the causative agent of WSD, on Pacific whiteleg shrimp ( Litopenaeus vannamei ), and uncover additional information that may be used in creating WSSV-resistant shrimp stocks. Results We successfully developed a novel nuclei isolation protocol optimized for shrimp tissues to prepare our samples for single nuclei RNA-sequencing following a pathogen challenge comprising 32 adult whiteleg shrimp infected with WSSV either through their feed or by injection. We constructed the first penaeid shrimp lymphoid organ cell atlas to improve our understanding of the characteristics of this immune organ, constructed UMAPs and identified marker genes that define distinct cell clusters to reveal the biological functions of different cell types within this organ. Conclusion By comparing gene expression between the control and WSSV-infected samples we uncovered multiple genes of interest that have the potential to be used as targets for CRISPR gene editing for WSSV-resistance in Pacific whiteleg shrimp. These genes have the potential to be valuable assets for future gene editing studies in commercially important penaeid shrimp species.
Correction: Genetic variations associated with immediate hypersensitivity reactions to iodinated contrast media: A whole exome sequencing study
Correction: Internally displaced persons’ ed utilization and disease patterns: A single center retrospective chart review
Vegetated landscape coherence and complexity shape psychophysiological stress recovery in campus courtyards
University students across urban campuses face sustained stress loads, making everyday restorative opportunities paramount. We ran a randomized virtual-reality experiment (N = 282) crossing landscape coherence (low, moderate, high) and complexity (low, moderate, high) in teaching-building courtyards, plus a non-vegetated control, to interrogate how designable visual attributes govern recovery. Complexity was parameterized with Shannon entropy while coherence reflected planting order. Heart rate, RMSSD, skin conductance level (SCL), and the Perceived Restorativeness Scale (PRS) were collected across baseline, stress, and recovery phases. Vegetated scenes improved SCL and PRS relative to the control. PRS rose monotonically with complexity and peaked at moderate-to-high coherence. Crucially, coherence moderated SCL responses: biodiversity-rich scenes only delivered autonomic settling when ordered, revealing a conditional mechanism that ties information-processing theory to stress-reduction pathways. The entropy-guided framework therefore refines prevailing restorative theories and yields a portable VR protocol that international teams can adapt with local species palettes. The findings translate into globally relevant guidance for campus and urban designers seeking micro-restorative infrastructure in dense settings.
Outside Back Cover: pH‐Mediated Strong Metal‐Support Interaction Construction Through Dynamic Fermi Level Tuning (Angew. Chem. Int. Ed. 18/2026)
MedSpectralNet: A lightweight convolutional neural network architecture for multi-modal image classification
Medical image classification requires models that effectively capture both fine-grained local patterns and global anatomical structures while maintaining computational efficiency for clinical deployment. Although state-of-the-art models such as MedMamba utilize State-Space Models (SSMs) to balance accuracy and efficiency, their sequential operations limit parallelism and increase runtime. To overcome these limitations, we propose MedSpectralNet, a lightweight Convolutional Neural Network (CNN) architecture that approximates self-attention with linear complexity to efficiently extract multi-frequency features. The model introduces a dual-stream feature extractor that processes global and local information in parallel, and a ContextGate block that adaptively fuses multi-scale representations. MedSpectralNet is evaluated across six benchmark datasets from MedMNIST (including BloodMNIST, BreastMNIST, DermaMNIST, PneumoniaMNIST, OrganCMNIST, and OrganSMNIST), MedSpectralNet achieves an average accuracy of 93.7% on OrganCMNIST and 98.0% on BloodMNIST, showing 1–4.3% relative accuracy gains when compared to larger transformer-based models. Importantly, it delivers this performance with only 8.5 million parameters, representing approximately 60% fewer parameters than MedMamba-T, which requires 14.5 million parameters. MedSpectralNet has also achieved high AUC values up to 0.999 across multiple classes, demonstrating state-of-the-art accuracy with substantially reduced computational cost and improved parallelization, which makes MedSpectralNet well-suited for real-time and resource-constrained classification-based medical applications.
Prevalence of perinatal depression in Ethiopia: An umbrella review of systematic review and meta-analysis studies
Background Perinatal depression is a significant public health concern that affects women during pregnancy and the postpartum period. Despite being acknowledged globally, the burden of perinatal depression is particularly profound in low and middle-income countries, such as Ethiopia. This umbrella review is therefore intended to systematically consolidate findings on perinatal depression among Ethiopian women to better understand its prevalence, thereby highlighting the gaps in current research and informing future interventions. Methods This umbrella review used the PRIOR checklist for the reviews of systematic review and meta-analytic studies. The review protocol has been registered on PROSPERO: CRD42023495174. PubMed, EMBASE, and PsycINFO databases were searched for the presence of systematic review and meta-analysis studies. The quality of included articles has been evaluated with a measurement tool to assess systematic review and meta-analysis studies (AMSTAR). A novel graphic approach with an estimated corrected covered area (CCA) has been used to determine the degree of overlap of primary studies in the systematic review and meta-analysis studies. The weighted random effect model was used during the meta-analysis. Result A total of 28 unique primary studies and 8 systematic reviews, and meta-analysis studies with 15,592 participants were included in this umbrella review. The pooled prevalence of perinatal depressive symptoms in the included systematic review and meta-analysis studies ranges from 20.1% to 25.8%. The pooled umbrella prevalence of perinatal depressive symptoms among women in Ethiopia was 22.49% (95 CI%:21.38, 23.59). The pooled umbrella analysis revealed that the antenatal and postnatal depressive symptoms were 22.76% (95% CI: 19.9, 25.62) and 21.75% (95% CI: 21.03, 22.48), respectively. In addition, the pooled prevalence of perinatal depression in studies that included 10 or below primary studies is 22.86% (95%CI:20.39, 25.33), and in those that included below 10 primary studies, it was 22.10% (95%CI: 21.55, 22.65). The novel graphic presentation depicted a very high degree of overlap of primary studies in the included systematic reviews and meta-analysis studies; corrected covered area (CCA) of 25.5%. Four of the included studies (fifty percent) had high methodological quality, and the remaining four relied on a moderate quality range. Conclusion The pooled overall, antenatal, and postnatal prevalence of depression symptoms was high in Ethiopia, with no significant difference during the antenatal and postnatal period. An improved understanding of perinatal depression will therefore guide policymakers and health practitioners in developing targeted strategies to alleviate this mental health challenge.
Pre-analytical errors during HIV viral load (HIV-VL) and early infant diagnosis (EID) testing in Cameroon
Background HIV Viral Load (HIV-VL) and Early Infant Diagnosis (EID) play a pivotal role in the laboratory surveillance, monitoring of HIV/AIDS, and its elimination as a public concern. However, sample rejection due to sample nonconformity (SNC) resulting from inadequate collection, transportation, and management, especially during the pre-analytical phase, negatively affects laboratory performance. This study aimed to characterize errors observed during the pre-analytical phase of HIV-VL and EID testing across national reference laboratories in Cameroon and to identify factors associated with rejection. Methods A descriptive and quantitative study of the nonconformities (NC) identified was collected from 11/01/2024 to 08/12/2024 in seventeen HIV reference laboratories, which constitute the national network of HIV-VL and EID testing coverage. For this study, the number of rejected samples, the reason for rejection, and the type of test ordered were recorded monthly. Results During the study period, 326,885 and 38,354 specimens received for HIV viral load and EID. Of those 12,748 (3.9%) and 2.7% (1,039) were rejected. The SNC analysis indicates the presence of multiple errors or NC in some samples. For HIV viral load, our results indicate that specimen identification errors for viral load were the most common NC (63.14%; n = 8049; P = 0.031), followed by insufficient specimen volume (43.7%; n = 5571; P = 0.049) and quality errors, including hemolyzed specimens (27.8%; n = 3543; P = 0.054), and specimen transport packaging errors (9.1%; n = 1160; P = 0.069). For HIV EID, specimen rejections were primarily attributed to missing or mismatched identification on the request forms (37.12%, n = 386; P = 0.042), sample unavailability (13.4%; n = 139; P = 0.056), and information discrepancies (44.2%; n = 459; P = 0.033). The observed significant rejection rates for both HIV viral load and EID exceeded the established national rejection rate of <2% of errors. Our results suggest that corrective action is critical, along with the establishment of policies to detect and resolve preanalytical errors in Cameroon. Conclusion Our findings highlight the high magnitude of preanalytical errors for HIV-VL and EID tests used in the testing and management of people living with HIV/AIDS in Cameroon. Therefore, the laboratory system should be strengthened to ensure high-quality patient services and support optimization. Suggestions for improvement include distributing a validated specimen-collection manual, creating electronic test-request forms, providing staff training, and regularly on-site supervising the use of available resources, all of which are necessary in this country.
Enterprise service user intent prediction based on fast K-means++ fusion algorithm
To address the low efficiency of feature mining and limited prediction accuracy in enterprise service user intent prediction, a research proposes an enterprise service user intention prediction model that integrates heuristic variants inspired by Kmeans++and Stacking ensemble learning. The model improves traditional K-means++ clustering through adaptive weighted grid information entropy optimization, solving the problems of slow convergence and uneven weight distribution in large-scale data. It also builds a weighted ensemble learner using base classifiers such as random forest to enhance intent prediction performance after multidimensional feature fusion. The experimental results show that the optimized Fast K-means++clustering algorithm achieved a contour coefficient of 0.92, a Calinski Harabasz index of 2500, and a Davies Bouldin index of 0.12 on dense point datasets, with significantly better clustering quality than the comparative algorithms. In the testing of the FK Stacking prediction model in real e-commerce scenarios, the accuracy, recall, and F1 score all exceeded 0.97, and the error rate remained stable below 2.1% in medium and long-term time series predictions; After iterative optimization, the model’s memory usage was reduced by 50% and response time was shortened by 82.5%. The results show that the proposed model offers lightweight and high-accuracy advantages in enterprise service user data analysis and intent prediction. It can help enterprises optimize resource allocation and improve service response speed.