Jiaying University

This study examines how classroom climate and feedback quality/internalization shape the development of entrepreneurial creativity among undergraduates in China, and whether narrative re-authoring practice amplifies this process. We surveyed students enrolled in entrepreneurship courses across four innovation ecosystems-Shenzhen, Hangzhou, Wuhan, and Xi'an-and paired surveys with expert ratings of artifact novelty and usefulness across course milestones. After distributing 634 questionnaires, 496 valid responses were retained (in-person proctoring; tokenized QR to prevent duplicates). Established, Chinese-adapted scales showed strong measurement quality (loadings ≥0.70; α=0.84-0.92; CR=0.89-0.94; AVE=0.61-0.71; SRMR=0.058; HTMT <0.85). Using SmartPLS 4 with 5000 bootstrap resamples, we found that supportive classroom climate directly promoted entrepreneurial creativity development (β=0.12, p=.009) and strongly enhanced feedback quality/internalization (β=0.58, p<.001). Feedback quality/internalization, in turn, predicted creativity growth (β=0.41, p<.001). The indirect effect of classroom climate on entrepreneurial creativity via feedback was significant and substantial (β_indirect=0.24, p<.001; partial mediation), and narrative re-authoring practice positively moderated the feedback→creativity path (β=0.09, p=.007). Framed by Self-Determination Theory and the Componential Theory of Creativity, the findings identify an actionable mechanism-ignite motivation with climate, steer progress with high-quality feedback, and stabilize iteration with narrative practice-offering theory-driven guidance for scaling China's innovation-oriented entrepreneurship education.

This study investigated the effects of boiling, roasting, and frying on the quality and flavor characteristics of Shiqi pigeon (Columba livia domestica) meat. Changes in color, texture, microstructure, and volatile profiles were systematically evaluated using colorimetry, texture profile analysis, scanning electron microscopy, electronic nose analysis, and Fourier transform infrared spectroscopy (FTIR). Thermal processing significantly influenced physicochemical properties and flavor profiles. Fried samples exhibited the highest hardness (27.79 N), chewiness (33.13 mJ), and maximum shear force (30.23 N), while boiled samples showed the lowest hardness (22.12 N) and puncture hardness (12.20 N), indicating improved tenderness. Electronic nose PCA explained 85.4% of total variance (PC1: 59.5%; PC2: 25.9%), clearly discriminating the three treatments. Color measurements showed that frying induced the greatest total color difference (ΔE > 1, p < 0.05), followed by roasting and boiling. FTIR analysis revealed pronounced shifts in amide I bands in fried samples, indicating stronger protein secondary structure alterations. Overall, different thermal processing methods produced distinct quality and flavor characteristics in pigeon meat, providing scientific guidance for process optimization and product development.

BackgroundPatient outcomes in rectal cancer with synchronous liver metastases remain heterogeneous, underscoring the need for reliable prognostic factors to guide individualized treatment strategies. This study sought to assess the influence of lymph node-related indices on the survival of rectal cancer patients with synchronous liver metastases who underwent preoperative chemoradiotherapy and to construct a novel nomogram for this patient population.MethodsData on rectal cancer patients with synchronous liver metastases who underwent preoperative chemoradiotherapy between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. X-tile software was employed to determine the optimal cutoff point of lymph node-related indices. The Kaplan-Meier method and log-rank test were used to assess survival differences. Cox univariate and multivariate analyses were conducted to identify the prognostic factors for overall survival (OS). We constructed a novel nomogram to predict individual survival probability. The concordance index (C-index) and calibration curve were used to determine the predictive accuracy and discriminative ability of the nomogram.ResultsA total of 547 rectal cancer patients with synchronous liver metastases were enrolled. The OS of patients with total number of lymph nodes (TLN) <7 was significantly worse than that of patients with TLN ≥7 (3-year OS rate: 46.6% vs. 62.7%; 5-year OS rate: 22.9% vs. 43.8%, P=0.001). Patients with number of positive lymph nodes (PLN) <7 had better OS than that of patients with PLN ≥7 significantly (3-year OS rate: 63.5% vs. 36.0%; 5-year OS rate: 44.1% vs. 12.6%, P<0.001). The 3-year OS rate was 49.7% for patients with number of negative lymph nodes (NLN) <11 compared with 68.6% for patients with NLN ≥11; and the 5-year OS rate was 23.9% and 53.6% for patients with NLN <11 and patients with NLN ≥11, respectively (P<0.001). Multivariate analysis showed that TLN, NLN and PLN were independent prognostic factors for OS [TLN: hazard ratio (HR) =0.571, 95% confidence interval (CI): 0.408–0.799, P=0.001; NLN: HR =0.593, 95% CI: 0.456–0.770, P<0.001; PLN: HR =1.736, 95% CI: 1.201–2.509, P=0.003]. A novel nomogram was established based on the independent prognostic factors indicated in the multivariate Cox analysis. The C-index for prognostic nomogram was 0.67 (95% CI: 0.63–0.71). The calibration plot for the probability of 3-year survival demonstrated an optimal agreement between the predicted and actual survival.ConclusionsLymph node (LN)-related indices are significant prognostic factors for OS in rectal cancer patients with synchronous liver metastases, offering insights into survival prediction and enabling personalized treatment strategies.

BackgroundTacrolimus (TAC) dosing presents a persistent challenge in postoperative care owing to its narrow therapeutic window and high inter‑patient variability, which often leads to suboptimal exposure with increasing risks of nephrotoxicity or graft rejection. Algorithm‑based personalized dosing strategies offer a promising approach to support clinical decision and improve long‑term outcomes.MethodsUnlike approaches relying on a wide range of variables and local clinical scopes, this study proposed a novel and versatile algorithm-driven strategy to predict TAC doses. A hybrid optimization method was first employed to identify a minimal set of key clinical factors. These factors were then used to construct a cascaded deep forest model capable of predicting both follow‑up and initial TAC doses in adult kidney transplant recipients.ResultsWhen validated on 615 patients using leave-one-subject-out cross-validation, it achieved predictions within ±20% of actual values, with an accuracy of 89.8% for follow-up doses and 83.2% for initial doses. Independent external validation confirmed its robustness. A Shapley additive explanation analysis revealed significant correlations between input features and predictive doses. To support real-time clinical use, an open‑access web platform was provided (http://www.jcu-qiulab.com/tacp/).ConclusionThis approach offers a practical, effective, and algorithm-driven pipeline for automated drug dose analysis and prediction in clinical practice.

Abstract Unraveling the kinetics of transcriptional regulation is a central challenge in molecular biology. Traditional inference methods based on cell-to-cell mRNA variability are limited because mRNA levels reflect not only transcriptional activity but also post-transcriptional processes. Recent advances in quantifying nascent RNAs across isogenic cells provide a more direct readout of transcriptional dynamics. Here, we present a moments-based theoretical framework that leverages nascent RNA data to infer transcriptional kinetics. We derive closed-form expressions for the first two time-dependent moments of nascent RNA across three canonical transcription models and demonstrate that nascent RNA exhibits distinct transient overshoots and higher Fano factors compared to mature mRNA. Through analysis of synthetic datasets, we show that these features enable more accurate estimation of transcriptional kinetics and allow reliable model selection, distinguishing single from multi-pathway promoter activation with over 75% accuracy even from small sample sizes. We further validated our model selection method using real experimental transcription datasets in yeast and E. coli cells, with nascent RNA demonstrating superior performance in identifying cross-talk pathway regulation. Our framework thus offers a computationally efficient and mechanistically informative approach for decoding transcriptional dynamics, emphasizing the advantages of time-dependent nascent RNA measurements over mature mRNA in capturing the true kinetics of transcriptional regulation.