Background: Medical school learning environment (MSLE) has a holistic impact on students’ psychosomatic health, academic achievements, and personal development. Students in different grades perceive MSLE in different ways. Thus, it is essential to investigate the specific role of student’s grade in the perception of MSLE. Methods: Using the Johns Hopkins Learning Environment Scale (JHLES) as a quantification instrument for the perception level of MSLE, 10,901 medical students in 12 universities in China were categorized into low or high JHLES group according to their questionnaires. We investigated the relationship between student’s grade and JHLES category by univariate analysis employing Pearson Chi-square test and Welch’s ANOVA. Then multivariable logistic regression analysis confirmed the predictive efficacy of student’s grade. A nomogram concerning the prediction of low JHLES score probability in medical students was also constructed. Results: A significant difference between two JHLES categories among students in different grades was observed (p < 0.001), with the proportion of the high JHLES group dominating in grade 1, 5, and the graduate subgroups (p < 0.001). The mean JHLES score declined especially in the third and fourth graders compared to freshmen (p < 0.001), while the mean score among the fifth graders had a remarkable rebound from the third graders (p < 0.001). Most imperatively, identified by multivariable logistic regression analysis, students in grade 3 (OR = 1.470, 95% CI = 1.265–1.709, p < 0.001) and 4 (OR = 1.578, 95% CI = 1.326–1.878, p < 0.001) perceived more negatively than freshmen. The constructed nomogram provided a promising prediction model for student’s low JHLES score probability, with accuracy, accordance, and discrimination (area under the curve (AUC) = 0.627). Conclusion: The student’s grade was a significant influencing factor in medical students’ perception of MSLE. The perceptions among the third and fourth graders got worse, probably due to the worrying changes in various aspects of MSLE during that period. The relevant and appropriate interventions to improve medical students’ perceptions are urgently needed.

Empathy of medical students is crucial, yet it tends to decline as students enter later academic years. Empathy appeared to be affected by the learning environment (LE), which could be a potential contributor. We conducted a cross-sectional study to investigate the association between LE and empathy. The study comprised overall 10,901 medical students from 12 Chinese medical schools. Jefferson Scale of Empathy (JSE) was utilized to assess empathy, while a 5-point question and the Johns Hopkins Learning Environment Scale were employed to evaluate LE. JSE scores were compared across LE levels using Pearson Chi-square, Welch’s ANOVA, and univariable linear regression. After adjusting for covariates, multivariable linear and logistic regression were used to assess the independent connection between LE and empathy. Confounders were controlled by stratified subgroup analysis. LE was significantly associated with medical students’ empathy, as shown by the Pearson Chi-square test (p < 0.001) and Welch’s ANOVA (p < 0.001). In multivariable logistic regression analysis, lower JSE scores were linked to common, bad, and terrible LE (OR = 2.226, 95% CI = [2.011, 2.465], p < 0.001; OR = 2.558, 95% CI = [1.745, 3.751], p < 0.001; OR = 1.889, 95% CI = [1.131, 3.153], p = 0.015), while excellent LE was associated with higher JSE scores (OR = 0.348, 95% CI = [0.312, 0.388], p < 0.001). Linear regression and stratified subgroup analysis confirmed logistic regression’s findings. LE was a significant predictor for empathy. Better LE was associated with higher empathy and worse LE was related to lower empathy. This inspired us to improve LE to enhance medical students’ empathy.

(1) Background: Non-small cell lung cancer (NSCLC) is the most common lung cancer. Enhancer RNA (eRNA) has potential utility in the diagnosis, prognosis and treatment of cancer, but the role of eRNAs in NSCLC metastasis is not clear; (2) Methods: Differentially expressed transcription factors (DETFs), enhancer RNAs (DEEs), and target genes (DETGs) between primary NSCLC and metastatic NSCLC were identified. Prognostic DEEs (PDEEs) were screened by Cox regression analyses and a predicting model for metastatic NSCLC was constructed. We identified DEE interactions with DETFs, DETGs, reverse phase protein arrays (RPPA) protein chips, immunocytes, and pathways to construct a regulation network using Pearson correlation. Finally, the mechanisms and clinical significance were explained using multi-dimensional validation unambiguously; (3) Results: A total of 255 DEEs were identified, and 24 PDEEs were selected into the multivariate Cox regression model (AUC = 0.699). Additionally, the NSCLC metastasis-specific regulation network was constructed, and six key PDEEs were defined (ANXA8L1, CASTOR2, CYP4B1, GTF2H2C, PSMF1 and TNS4); (4) Conclusions: This study focused on the exploration of the prognostic value of eRNAs in the metastasis of NSCLC. Finally, six eRNAs were identified as potential markers for the prediction of metastasis of NSCLC.

Background. Herein, we tried to develop a prognostic prediction model for patients with LUAD based on the expression profiles of lipid metabolism-related genes (LMRGs). Methods. Molecular subtypes were identified by non-negative matrix factorization (NMF) clustering. The overall survival (OS) predictive gene signature was developed and validated internally and externally based on online data sets. Time-dependent receiver operating characteristic (ROC) curve, KaplanMeier curve, nomogram, restricted mean survival time (EMST), and decision curve analysis (DCA) were used to assess the performance of the gene signature. Results. We identified three molecular subtypes in LUAD with distinct characteristics on immune cells infiltration and clinical outcomes. Moreover, we confirmed a seven-gene signature as an independent prognostic factor for patients with LUAD. Calibration and DCA analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. In addition, the nomogram showed higher robustness and clinical usability compared with four previously reported prognostic gene signatures. Conclusions. Findings in the present study shed new light on the characteristics of lipid metabolism within LUAD, and the established seven-gene signature can be utilized as a new prognostic marker for predicting survival in patients with LUAD.