A multicenter cross-sectional study in China revealing the intrinsic relationship between medical students' grade and their perceptions of the learning environment.

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.

[Analysis of Reproductive Tract Microecological Changes During the Frozen-Thawed Embryo Transfer Cycle and Clinical Pregnancy Outcomes]. / æ¿€ç´ æ›¿ä»£å†»èƒšç§»æ¤å‘¨æœŸç”Ÿæ®–é“å¾®ç”Ÿæ€å˜åŒ–ä¸Žå¦Šå¨ ç»“å±€åˆ†æž.

Objective: This study aims to analyze the relationship between reproductive tract microecological changes, metabolic differences, and pregnancy outcomes at different time points in the frozen-thawed embryo transfer cycle while patients are undergoing hormone replacement therapy, which will be a breakthrough point for improving outcomes. Methods: A total of 20 women undergoing frozen-thawed single blastocyst transfer for the first time at the Reproductive Medicine Center of Fujian Maternal and Child Health Hospital between July 2022 and January 2023 were recruited for this study. Their vaginal and cervical secretions were collected for 16S rRNA sequencing and non-targeted metabolomics analysis on days 2-5 of menstruation, day 7 after estrogen replacement therapy started, the day when progesterone was added, and the day of transplantation. The subjects were divided into different groups according to their clinical pregnancy status and the sequencing results were analyzed using bioinformatics methods. Results: 1) The alpha-diversity index of the vaginal and cervical microbiota was higher on days 2-5 of menstruation (P<0.01), but did not differ significantly on day 7 after oral estrogen replacement therapy started, the day of progesterone administration, and the day of transplantation (P≥0.1). 2) Both the pregnant group and the non-pregnant group showed a variety of microorganisms and metabolites with significant differences in the lower reproductive tract at different time points. 3) Microbial analysis at different time points showed that there were significant differences in vaginal flora, including Peptoniphilus, Enterocloster, Finegoldia, Klebsiella, Anaerobutyricum, Agathobaculum, Sporanaerobacter, Bilophila, Prevotella, and Anaerococcus in the pregnant group (P<0.05). 4) Metabolite analysis at different time points showed that there were significant differences in 3-hydroxybenzoic acid, linatine, (R)-amphetamine, hydroxychloroquine, and L-altarate in the vaginal secretions of the pregnant group (P<0.05), and that there were significant differences in isocitric acid, quassin, citrinin, and 12(R)-HETE in the cervical secretions (P<0.05). 5) Metabolite analysis at different time points showed that, in the non-pregnant group, there were significant differences in linatine, decanoyl-L-carnitine, aspartame, sphingosine, and hydroxychloroquine in the vaginal secretions (P<0.05), and the isocitric acid, quassin, ctrinin, and 12(R)-HETE in the cervical secretions (P<0.05). 6) Combined microbiome and metabolomics analysis showed that certain metabolites were significantly associated with microbial communities, especially Klebsiella. Conclusions: Significant differences in the microbiota genera and metabolites at different time points were found during the frozen-embryo transfer cycle of hormone replacement therapy, which may be used as potential biomarkers to predict pregnancy outcomes of embryo transfer.

Recent progress in retinoblastoma: Pathogenesis, presentation, diagnosis and management.

Retinoblastoma, the primary ocular malignancy in pediatric patients, poses a substantial threat to mortality without prompt and effective management. The prognosis for survival and preservation of visual acuity hinges upon the disease severity at the time of initial diagnosis. Notably, retinoblastoma has played a crucial role in unraveling the genetic foundations of oncogenesis. The process of tumorigenesis commonly begins with the occurrence of biallelic mutation in the RB1 tumor suppressor gene, which is then followed by a cascade of genetic and epigenetic alterations that correspond to the clinical stage and pathological features of the tumor. The RB1 gene, recognized as a tumor suppressor, encodes the retinoblastoma protein, which plays a vital role in governing cellular replication through interactions with E2F transcription factors and chromatin remodeling proteins. The diagnosis and treatment of retinoblastoma necessitate consideration of numerous factors, including disease staging, germline mutation status, family psychosocial factors, and the resources available within the institution. This review has systematically compiled and categorized the latest developments in the diagnosis and treatment of retinoblastoma which enhanced the quality of care for this pediatric malignancy.

A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma.

BACKGROUND: Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. METHOD: Three databases, including NCBI-GEO, TCGA, and MET-500, separately provide single-cell RNA sequencing data, bulk RNA sequencing profiles of MESO, and RNA sequencing information on bone metastatic tumors. Dimensionality reduction and clustering analysis were leveraged to acquire fibroblast subtypes in the MESO microenvironment. The fibroblast differentiation-related genes (FDGs), which were associated with survival and subsequently utilized to generate the MESO categorization and prognostic prediction model, were selected in combination with pseudotime analysis and survival information from the TCGA database. Then, regulatory network was constructed for each MESO subtype, and candidate inhibitors were predicted. Clinical specimens were collected for further validation. RESULT: A total of six fibroblast subtypes, three differentiation states, and 39 FDGs were identified. Based on the expression level of FDGs, three MESO subtypes were distinguished in the fibroblast differentiation-based classification (FDBC). In the multivariate prognostic prediction model, the risk score that was dependent on the expression level of several important FDGs, was verified to be an independently effective prognostic factor and worked well in internal cohorts. Finally, we predicted 24 potential drugs for the treatment of MESO. Moreover, immunohistochemical staining and statistical analysis provided further validation. CONCLUSION: Fibroblast differentiation-related genes (FDGs), especially those in low-differentiation states, might participate in the proliferation and invasion of MESO. Hopefully, the raised clinical subtyping of MESO would provide references for clinical practitioners.

Good learning environment of medical schools is an independent predictor for medical students' study engagement.

Background: Study engagement is regarded important to medical students' physical and mental wellbeing. However, the relationship between learning environment of medical schools and the study engagement of medical students was still unclear. This study was aimed to ascertain the positive effect of learning environment in study engagement. Methods: We collected 10,901 valid questionnaires from 12 medical universities in China, and UWES-S was utilized to assess the study engagement levels. Then Pearson Chi-Square test and Welch's ANOVA test were conducted to find the relationship between study engagement and learning environment, and subgroup analysis was used to eradicate possible influence of confounding factors. After that, a multivariate analysis was performed to prove learning environment was an independent factor, and we constructed a nomogram as a predictive model. Results: With Pearson Chi-Square test (p < 0.001) and Welch's ANOVA test (p < 0.001), it proved that a good learning environment contributed to a higher mean of UWES scores. Subgroup analysis also showed statistical significance (p < 0.001). In the multivariate analysis, we could find that, taking "Good" as reference, "Excellent" (OR = 0.329, 95%CI = 0.295-0.366, p < 0.001) learning environment was conducive to one's study engagement, while "Common" (OR = 2.206, 95%CI = 1.989-2.446, p < 0.001), "Bad" (OR = 2.349, 95%CI = 1.597-3.454, p < 0.001), and "Terrible" (OR = 1.696, 95%CI = 1.015-2.834, p = 0.044) learning environment only resulted into relatively bad study engagement. Depending on the result, a nomogram was drawn, which had predictive discrimination and accuracy (AUC = 0.680). Conclusion: We concluded that learning environment of school was an independent factor of medical student's study engagement. A higher level of learning environment of medical school came with a higher level of medical students' study engagement. The nomogram could serve as a predictive reference for the educators and researchers.