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This study aims to investigate the effect of mindfulness on flow at the organizational and individual levels. Based on perseverative cognition theory, we introduced work-related rumination (affective rumination and problem-solving pondering) as the transmitter in these processes. This study conducted a three-wave longitudinal survey. The data of 458 employees and 114 leaders were collected from three software parks in China. Multilevel structural equation modeling and the Markov Chain Monte Carlo method were adopted to test all hypotheses. Employee mindfulness and leader mindfulness help reduce affective rumination by employees and increase their problem-solving pondering and flow experiences. Affective rumination and problem-solving pondering partially mediate the relationship between leader and employee mindfulness and flow. Leader mindfulness moderates the effects of employees' mindfulness on their affective rumination and problem-solving pondering. Our findings contribute to the current literature on mindfulness, work-related rumination and flow experience and extend the understanding of the effect boundary of mindfulness. This study also helps guide organizations to better design and carry out mindfulness and flow interventions.

Studies reported periodontal disease (PD) periodontal disease is associated with many systemic diseases, including cardiovascular outcomes and all-cause mortality. However, the precise mechanistic link for these relationship remained unclear. We therefore performed a meta-analysis of cohort studies to investigate the association of PD with the risk of cardiovascular outcomes and all-cause mortality. We systematically searched the databases of PubMed, EmBase, and the Cochrane library to identify eligible studies until April 2023. The investigated outcomes included major adverse cardiovascular events (MACEs), coronary heart disease (CHD), myocardial infarction (MI), stroke, cardiac death, and all-cause mortality. The summary relative risk (RR) with 95% confidence interval (CI) were calculated using the random-effects model. Thirty-nine cohort studies with 4,389,263 individuals were selected for final meta-analysis. We noted PD were associated with elevated risk of MACEs (RR: 1.24; 95%CI: 1.15–1.34; P <0.001), CHD (RR: 1.20; 95%CI: 1.12–1.29; P <0.001), MI (RR: 1.14; 95%CI: 1.06–1.22; P = 0.001), stroke (RR: 1.26; 95%CI: 1.15–1.37; P <0.001), cardiac death (RR: 1.42; 95%CI: 1.10–1.84; P = 0.007), and all-cause mortality (RR: 1.31; 95%CI: 1.07–1.61; P = 0.010). Sensitivity analyses indicated the pooled conclusions for cardiovascular outcomes and all-cause mortality are robustness. The associations of PD with the risk of ardiovascular outcomes and all-cause mortality could affected by region, study design, PD definition, follow-up duration, and study quality. This study found the risk of cardiovascular outcomes and all-cause mortality were elevated in PD patients, and the intervention for PD should be applied to prevent the risk of cardiovascular outcomes.

Head and neck cancer (HNC), a condition that is both disfiguring and potentially fatal, has become a critical public health challenge. This study seeks to evaluate the trends in HNC burden and predict its future trajectory in China. Utilizing data from the 2021 Global Burden of Disease database, we focused on the incidence, mortality, and disability-adjusted life years related to lip and oral cavity, nasopharyngeal, and laryngeal cancers within the country. We analyzed changes in incidence and mortality rates using the estimated annual percentage change, age-period-cohort (APC) analysis, and decomposition analysis. Additionally, an Autoregressive Integrated Moving Average model was employed to forecast the future burden of HNC. In 2021, China’s incidence rates for lip and oral cavity, nasopharyngeal, and laryngeal cancers were higher than those in 40.98%, 98.05%, and 50.73% of countries worldwide, respectively. The burden of HNC increases significantly with age, particularly among men. The APC analysis indicates a rising incidence of HNC among younger adults. Decomposition analysis comparing 2021–2019 highlighted that ASIR and aging were the primary factors influencing the number of cases and deaths. Projections indicate that the burden of HNC in China is expected to continue rising. To combat this growing issue, it is imperative to enhance public health strategies that focus on prevention, early detection, and efficient resource allocation.

Facial fractures significantly impair functions related to respiration, vision, and speech, while also posing long-term cosmetic and psychological challenges. Regional disparities in the burden of facial fractures reflect variations in risk factors, healthcare accessibility, and preventive measures. This study investigates recent causes, trends, and the burden of facial fractures in China and the United States. Utilizing the Global Burden of Disease 2021 dataset, the study analyzed epidemiological data on facial fractures in China and the United States, focusing on age-standardized incidence rates and years lived with disability from 2010 to 2021. Estimated annual percentage changes (EAPC) were calculated to assess trends, while age- and sex-specific analyses provided further insights into population-specific patterns. Additionally, the primary etiologies of facial fractures in both countries were examined. Between 2010 and 2021, the incidence of facial fractures increased in both China and the United States, with a more pronounced rise in China (EAPC: 1.56%) compared to the United States (EAPC: 0.38%). In 2021, the highest incidence in China was observed among males aged 30–34 years, while in the United States, it was among males aged 20–24 years. Males consistently exhibited higher rates than females in both countries. Falls and road injuries were the leading causes of facial fractures in China, whereas falls and mechanical forces were predominant in the United States. The rising incidence of facial fractures in China and the United States highlights the need for targeted preventive strategies tailored to each country’s specific risk factors and demographic patterns. These findings underscore the importance of addressing facial fractures as a global public health priority, with implications for policy-making and resource allocation to reduce the burden of these injuries worldwide.

The service reliability of critical catenary components is strongly influenced by damage evolution at dynamic contact interfaces. In this study, a numerical framework is developed to simulate the dynamic contact behavior and wear progression of catenary droppers by coupling Archard’s wear law with an adaptive remeshing strategy. Surface degradation is explicitly incorporated into the contact formulation through an improved boundary representation, enabling a quantitative linkage between interface damage and the corresponding mechanical responses. The simulations indicate that, after geometric reconstruction of the worn surface, the contact interface exhibits a pronounced stress-gradient evolution. The most severe damage is predicted at the contact region between the central strand and one outer strand, and the spatial damage pattern is primarily governed by discontinuous contact. Moreover, thermally induced material softening has a limited effect on the peak contact stress, which is dominated instead by the applied load and local contact geometry. The proposed framework provides a computational basis for assessing dropper wear and estimating catenary lifetime, thereby supporting reliability-oriented maintenance and safer rail operations.

Forest management influences key ecosystem services essential for societal well-being. However, how these human management activities affect forest resilience under different environmental conditions remains poorly understood. This study investigates how different forest management practices interact with biophysical factors and explores their joint effect on forest resilience globally during 2001–2015. To address this gap, we develop a framework that integrates Critical Slowing Down theory, satellite-based vegetation indices, and machine learning. Results show that natural forests without management exhibit the highest resilience among all forest management types, followed by natural forests with management and planted forests. Overall, forest management practices weaken resilience prominently under intensive anthropogenic pressures. However, specific biophysical conditions can reverse this pattern. Planted forests show higher resilience than natural forests when the ratio of precipitation to potential evapotranspiration exceeds 1.5 in wet climates, whereas natural forests remain more resilient in drier climates below this threshold. This threshold reflects the point where water availability becomes sufficient to compensate for the detrimental effects originating from human pressures. Cold temperatures, dense vegetation, and high soil fertility may further enhance the resilience of managed forests to levels typical of natural forests. Human management is vital for ecosystem health, yet its global impact on forest resilience remains unclear. This study shows that while natural forests are generally most resilient, managed forests can be more stable in wet or fertile regions.

Propionic acidemia (PA) is a rare autosomal recessive metabolic disorder caused by functional deficiency of propionyl-CoA carboxylase, clinically characterized by life-threatening ketoacidosis, hyperammonemia, and multiorgan dysfunction. Due to its nonspecific clinical manifestations, PA is frequently misdiagnosed or only identified during severe metabolic crises. This study reports a Chinese family with a history of offspring affected by PA. Through whole-exome sequencing and Sanger validation of fetal amniotic fluid and parental peripheral blood samples, two novel compound heterozygous variants in the PCCB gene were identified in the fetus, initially classified as variants of uncertain significance (VUS) per ACMG guidelines. Subsequent functional studies and amniotic fluid metabolomic analyses were performed. The results demonstrated that the paternal PCCB c.366_372 + 7del variant caused exon 3 skipping(p.Phe102_Gln124del) or exon 2–3 skipping (p.Gly62_Gln124del), while the maternal c.183 + 6T > G variant resulted in intron 1 retention (p.Gly62Valfs*10), both leading to protein truncation and aberrant mRNA splicing. Metabolomic analysis demonstrated significantly elevated C3 levels and an increased C3/C2 ratio, consistent with PA diagnosis. These novel PCCB splicing variants expand the mutational spectrum of PA and demonstrate the clinical utility of integrated genomic-metabolomic analysis for prenatal diagnosis and genetic counseling in high-risk PA families.

Neoadjuvant therapies have been shown to decrease tumor burden, increase resection rate, and improve the outcomes among patients with locally advanced gastric cancer (GC). However, not all patients are equally responsive; therefore, differentiating potential respondents from nonrespondents is clinically important. To use pretreatment computed tomography (CT)-pixelated feature-difference extraction techniques to identify diagnostically relevant features that could predict patients' response to neoadjuvant chemotherapy at diagnosis. This multicenter cohort study included patients with locally advanced GC who were treated from January 2010 to July 2017 at 2 hospitals in southern China (training cohort) and 1 hospital in northern China (external validation cohort). Their clinicopathological data, pretreatment CT images, and pathological reports were retrieved and analyzed. Data analysis was conducted from December 2017 to May 2021. All patients underwent 2 to 4 cycles of fluorouracil in combination with a platinum-based neoadjuvant chemotherapy regimen. All gastrectomies were performed according to the Japanese Classification of Gastric Carcinoma (14th edition) guidelines. Reliability of clinicopathological and radiomics-based features were assessed with area under receiver operating characteristic curve (AUC) and Mann-Whitney U test. A total of 323 patients (242 [74.9%] men; median [range] age, 58 [24-82] years) were included in the study, with 250 patients (77.4%) in the training cohort and 73 (22.6%) in the validation cohort. The baseline pretreatment characteristics of the training and validation cohorts were well-balanced. The number of respondents in the training and validation cohort was 122 (48.8%) and 40 (54.8%), respectively, and the number of nonrespondents was 128 (51.2%) and 33 (45.2%), respectively. No clinicopathological variables were significantly associated with treatment response. Using radiomics, 20 low-intercorrelated features from a total of 7477 features were used to construct a radiomics signature that demonstrated significant association with treatment response. Good discrimination performance of the radiomics signature for predicting treatment response in the training (AUC, 0.736; 95% CI, 0.675-0.798) and external validation (AUC, 0.679; 95% CI, 0.554-0.803) cohorts was observed. Decision curve analysis confirmed the clinical utility of the radiomics signature. In this study, the proposed radiomics signature showed potential as a clinical aid for predicting the response of patients with locally advanced GC before treatment, thereby allowing timely planning for effective treatments for potential nonrespondents.

Background Studies demonstrate that both the flipped classroom and the bridge-in, objective, pre-assessment, participatory learning, post-assessment, summary model (the BOPPPS model) work well in medical education. However, limited research has systematically integrated these two approaches into standardized residency training program. This study explores the effectiveness of this combined teaching model in the standardized residency training for gastrointestinal surgery. Methods 68 trainees in the standardized residency training program at the Department of Gastrointestinal Surgery, Guangdong Provincial People’s Hospital, were split into two groups: a control group receiving traditional rotation-based training and an experimental group undergoing the flipped classroom approach combined with the BOPPPS model. The two groups of trainees underwent theoretical and practical assessments at the end of rotation. The instructors also evaluated the trainees’ performance through a 10-item questionnaire. Additionally, the trainees in the experimental group received a pre-quiz before entering the department, and completed a questionnaire to evaluate the new teaching model at the end of rotation. Results Both the theoretical and practical assessment scores of the experimental group were superior to those of the control group. The experimental group demonstrated superior performance in several key areas, clinical reasoning, emergency response, and surgical compliance. 97.0% of the trainees believed that the new teaching model was scientific. Furthermore, all trainees agreed that the new teaching model was worthy of promotion. Conclusion The combined application of the flipped classroom and the BOPPPS model can improve the rotation experience of trainees, enhance their performance, and boost their final grades.

Customer preferences analysis and modelling using deep learning in edge computing environment are critical to enhance customer relationship management that focus on a dynamically changing market place. Existing forecasting methods work well with often seen and linear demand patterns but become less accurate with intermittent demands in the catering industry. In this paper, we introduce a throughput deep learning model for both short-term and long-term demands forecasting aimed at allowing catering businesses to be highly efficient and avoid wastage. Moreover, detailed data collected from a business online booking system in the past three years have been used to train and verify the proposed model. Meanwhile, we carefully analyzed the seasonal conditions as well as past local or national events (event analysis) that could have had critical impact on the sales. The results are compared with the best performing forecast methods Xgboost and autoregressive moving average model (ARMA), and they suggest that the proposed method significantly improves demand forecasting accuracy (up to 80%) for dishes demand along with reduction in associated costs and labor allocation.