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Recently, flipped classrooms (FCs) have gradually been used in Chinese higher education settings. However, few studies have focused on the effects of FCs on interdisciplinary curricula. The purpose of this study was to examine the impact of an FC on the engagement, performance, and perceptions of students and on teacher-student interaction in a pharmaceutical marketing course. A clustered randomized controlled study was conducted, with 137 junior-year pharmacy undergraduates using an FC serving as the intervention group, in contrast to students using lecture-based learning (LBL) as the control group. Flanders' interaction analysis system (FIAS) was used to measure teacher-student interaction, and questionnaires regarding attitudes toward and satisfaction with the teaching model were administered. The students in the FC group scored significantly higher than those in the LBL group (88.21±5.95 vs. 80.05±5.59, t = -8.08, p = 0.000) on pharmaceutical marketing. The multiple linear regression results showed that the FC model had a significant impact on student performance (β = 8.16, p<0.0001). The percentages of teacher talk in the FC and LBL groups were 21% and 96%, respectively (χ2 = 2170.274, p = 0.000); however, the percentages of student talk in the FC and LBL groups were 75% and 2.6%, respectively (χ2 = 2012.483, p = 0.000). Compared with the LBL group, most students in the FC group held more positive attitudes toward the teaching model; the mean scores for the 8 attitude attributes in the FC group were significantly higher than those in the LBL group (p = 0.000). There were significant differences in the ratings of satisfaction with teacher-student interaction (p = 0.000), the students' learning attitude (p = 0.000), the teacher's preparatory work (p = 0.000), the teaching objective (p = 0.000), and the teaching effect (p = 0.000) between the two groups. Compared with LBL methods, implementing the FC model improved student performance, increased teacher-student interaction and generated positive student attitudes toward the experience. As an effective pedagogical model, it can also stimulate pharmacy students' learning interest and improve their self-learning abilities.

Atrial fibrillation (AF) recurrence following radiofrequency ablation (RFA) remains a significant clinical challenge, and pacemaker implantation may influence outcomes. However, the underlying molecular mechanisms are not well understood. This study aimed to investigate whether pacemaker implantation is statistically associated with of AF recurrence and to identify proteomic biomarkers associated with this statistical relationship. We conducted a propensity score-matched cohort study and performed Cox proportional hazards analysis to assess the association between pacemaker implantation and AF recurrence. Exploratory plasma proteomic profiling was performed to identify differentially expressed proteins (DEPs) in a pilot cohort of paroxysmal AF patients with and without pacemakers. An independent validation cohort was used to confirm the findings via Cox analysis and biomarker quantification, followed by statistical mediation analysis. In the discovery cohort, pacemaker implantation was identified as an independent risk factor for post-ablation AF recurrence (adjusted hazard ratio: 2.349, 95% CI: 1.065–5.179, p  = 0.034). Exploratory proteomic analysis revealed significantly elevated levels of cartilage intermediate layer protein 1 (CILP1) in pacemaker patients. In the validation cohort, pacemaker implantation remained an independent predictor of AF recurrence, and plasma CILP1 levels were significantly statistically associated with recurrence risk (AUC: 0.737, 95% CI: 0.633–0.842, p  = 0.0005). Statistical mediation analysis indicated that elevated CILP1 levels were indirectly associated with the relationship between pacemaker implantation and AF recurrence ( p  = 0.022). Pacemaker implantation is associated with an increased risk of AF recurrence following RFA. This risk appears to be partially statistically mediated by elevated plasma CILP1 levels. These findings suggest CILP1 may serve as a potential biomarker for AF risk stratification. Given the pilot nature of the proteomic screening, further large-scale studies are warranted to validate these associations.

Atrial fibrillation (AF) is an age-related disease, while telomeres play a central role in aging. But the relationship between AF and telomere length (LTL) is still controversial. This study aims to examine the potential causal association between AF and LTL by using Mendelian randomization (MR). Bidirectional two-sample MR, expression and protein quantitative trait loci (eQTL and pQTL)-based MR were performed using genetic variants from United Kingdom Biobank, FinnGen, and a meta-analysis study, which comprised nearly 1 million participants in the Atrial Fibrillation Study and 470,000 participants in the Telomere Length Study. Apart from the inverse variance weighted (IVW) approach as the main MR analysis, complementary analysis approaches and sensitivity analysis were applied. The forward MR revealed a significant causal estimate for the genetically predicted AF with LTL shortening [IVW: odds ratio (OR) = 0.989,  = 0.007; eQTL-IVW: OR = 0.988,  = 0.005; pQTL-IVW: OR = 0.975,  < 0.005]. But in the reverse MR analysis, genetically predicted LTL has no significant correlation with AF (IVW: OR = 0.995,  = 0.916; eQTL-IVW: OR = 0.999,  = 0.995; pQTL-IVW: OR = 1.055,  = 0.570). The FinnGen replication data yielded similar findings. Sensitivity analysis ensured the stability of the results. The presence of AF leads to LTL shortening rather than the other way around. Aggressive intervention for AF may delay the telomere attrition.

Identifying factors affecting lifespan, including genes or proteins, enables effective interventions. We prioritized potential drug targets and provided insights into biological pathways for healthy longevity by integrating Mendelian randomization, cohort, and experimental studies. We identified causal effects of tissue‐specific genetic transcripts and serum protein levels on three longevity outcomes: the parental lifespan, the top 1% and 10% extreme longevity, utilizing Mendelian randomization and multi‐traits colocalization, combining the latest genetics data of gene expression (eQTLGen and GTEx) and proteomics (4746 proteins from five studies). We then evaluated associations of these potential genetic targets with mortality risk and life expectancy in the UK Biobank cohort. We performed in vitro cellular senescence experiments to confirm their effects. Fourteen plasma proteins and nine transcripts in whole blood had independent causal effects on longevity, where a cascading effect of both the tissue‐specific transcripts and plasma proteins of LPA, PDAP1, DNAJA4, and TMEM106B showed negative effects on longevity. PDAP1 (PDGFA‐associated protein 1) with the strongest genetic evidence might reduce lifespan by modifying sex hormones, adiposity, and epigenetic aging acceleration. In the prospective cohort, blood PDAP1 levels were significantly associated with higher all‐cause mortality and more years of loss. In vitro, cellular senescence is accompanied by upregulation of PDAP1 expression. Exogenous PDAP1 stimulation accelerates cellular senescence while the deficiency of PDAP1 attenuates replicative senescence. This study facilitates the discovery of potential drug targets and provides a broader understanding of the biological processes of longevity, where PDAP1 emerged as a star for modifying human lifespan. Integration of multi‐omics analyses reveals PDAP1 as one of the key regulators of longevity and aging. Mendelian randomization, colocalization, prospective studies, and in vitro experiments highlight PDAP1's effects on mortality, lifespan, and cellular senescence.

Objective: Exploring how the theory of planned behavior (TPB), social capital theory (SCT), cervical cancer knowledge (CCK), and demographic variables predict behavioral intentions (BI) related to cervical cancer screening among Chinese women. Methods: Self-administered questionnaires were distributed to 496 women, followed by a path analysis. Results: The three-level model was acceptable, χ2(26, 470) = 26.93, p > 0.05. Subjectively overcoming difficulties, support from significant others, screening necessity, and the objective promotion factor promoted BI, with effect sizes of 0.424, 0.354, 0.199, and 0.124. SCT and CCK promoted BI through TPB, with effect sizes of 0.262 and 0.208. Monthly income, education, age, and childbearing condition affected BI through TPB, SCT, and CCK, with effect sizes of 0.269, 0.105, 0.065, and −0.029. Conclusion: The three-level model systematically predicted behavioral intentions relating to cervical cancer screening.

Stock market prediction has long attracted the attention of academia and industry due to its potential for substantial financial returns. Despite the availability of various forecasting methods, such as CNN, LSTM, BiLSTM, GRU, and Transformer, the hyperparameter optimization of these models often faces limitations, particularly in single-objective optimization, where they can easily fall into local optima. To address this issue, this paper proposes an innovative multi-objective optimization algorithm—the Multi-Objective Escape Bird Algorithm (MOEBS)—and introduces the MOEBS-Transformer architecture to enhance the efficiency and effectiveness of hyper-parameter optimization for Transformer models. This study first validates the performance of MOEBS through a series of multi-objective benchmark tests on standard problem sets such as ZDT, DTLZ, and WFG, comparing it with other multi-objective optimization algorithms (e.g., MOMVO, MSSA, and MOEAD) using evaluation metrics such as GD, Spacing, IGD, and HV for comprehensive analysis. In the context of stock price prediction, we select the closing price datasets of Amazon, Google, and Uniqlo, using MOEBS to optimize the core hyper parameters of the Transformer while considering multiple objectives, including training set RMSE, testing set RMSE, and testing set error variance. In the experiments, this paper first compares CNN, LSTM, BiLSTM, GRU, and traditional Transformer models to establish the Transformer as the optimal model for stock market prediction. Subsequently, the study compares the MOEBS-Transformer with Transformer models optimized using various hyperparameter optimization methods, including MOMVO-Transformer, MSSA-Transformer, and MOEAD-Transformer. Additionally, it evaluates Transformer models optimized through conventional methods: Random Search (RS-Transformer), Grid Search (GS-Transformer), and Bayesian Optimization (BO-Transformer). By assessing the performance of these models using R 2 , RMSE, and RPD metrics on both training and testing sets, the results demonstrate that the Transformer model optimized by MOEBS significantly outperforms the other methods in terms of prediction accuracy and prediction stability. This research offers a new solution for complex optimization scenarios and lays a foundation for advancements in stock market prediction technologies.

In the context of global economic transformation, high-quality enterprise development (HQED) is crucial for driving economic growth, particularly through enhancing Total Factor Productivity (TFPLP). Digital Inclusive Finance (DIF), as a classical financial model, plays an important role in promoting high-quality enterprise development. To explore the relationship between TFP and DIF, we first applied traditional double fixed-effects models, along with robustness and heterogeneity tests, for modeling experiments. This series of tests effectively revealed the theoretical linear relationships between economic variables. However, the double fixed-effects model has limitations in capturing nonlinear relationships and making predictions. Given the growing body of research on existing hybrid models, we acknowledge the importance of exploring and contributing to this evolving area. To address this issue, based on the results of traditional economic analysis, we introduced improved time series models. These advanced deep learning models allow us to better capture the complex nonlinear relationship between DIF and TFP. The experiment initially explored the preliminary structural relationship between DIF and TFP using double fixed-effects models combined with robustness and heterogeneity tests. Then, based on the results of these tests, we selected deep learning features and combined Kolmogorov–Arnold Neural Network (KAN), Graph Neural Network (GNN) models with classic time series deep learning models (Transformer, LSTM, BiLSTM, GRU) to capture the latent nonlinear features in the data for prediction. The results show that, compared to traditional time series forecasting methods, the improved deep learning models perform better in capturing the nonlinear relationships of economic variables, improving prediction accuracy, and reducing prediction errors. Finally, paired t -tests and Cohen’s d effect size tests were used to evaluate error metrics, and the results indicate that the introduction of KAN and GNN models significantly improved the performance of time series forecasting models.