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.

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.

Odor Fingerprinting of Chitosan and Source Identification of Commercial Chitosan: HS-GC-IMS, Multivariate Statistical Analysis, and Tracing Path Study.

Chitosan samples were prepared from the shells of marine animals (crab and shrimp) and the cell walls of fungi (agaricus bisporus and aspergillus niger). Fourier-transform infrared spectroscopy (FT-IR) was used to detect their molecular structures, while headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was employed to analyze their odor composition. A total of 220 volatile organic compounds (VOCs), including esters, ketones, aldehydes, etc., were identified as the odor fingerprinting components of chitosan for the first time. A principal component analysis (PCA) revealed that chitosan could be effectively identified and classified based on its characteristic VOCs. The sum of the first three principal components explained 87% of the total variance in original information. An orthogonal partial least squares discrimination analysis (OPLS-DA) model was established for tracing and source identification purposes, demonstrating excellent performance with fitting indices R2X = 0.866, R2Y = 0.996, Q2 = 0.989 for independent variable fitting and model prediction accuracy, respectively. By utilizing OPLS-DA modeling along with a heatmap-based tracing path study, it was found that 29 VOCs significantly contributed to marine chitosan at a significance level of VIP > 1.00 (p < 0.05), whereas another set of 20 VOCs specifically associated with fungi chitosan exhibited notable contributions to its odor profile. These findings present a novel method for identifying commercial chitosan sources, which can be applied to ensure biological safety in practical applications.

The role of uncoupling protein 2 in experimental periodontitis-associated renal injury in rats.

OBJECTIVES: This study aims to explore changes in uncoupling protein 2 (UCP2) in experimental periodontitis-associated renal injury induced by ligation and investigate the effect of UCP2 on renal injury induced by periodontitis. METHODS: Twelve Wistar male rats were randomly divided into two groups: control and periodontitis groups. A periodontal model was built by ligating the maxillary first molars area with 0.2 mm orthodontic ligature wire. After 8 weeks, the intraoral condition of the rats was observed and periodontal clinical indices such as gingival bleeding index (BI), periodontal probing depth (PD), and tooth mobility (TM) were detected. The maxillary bone was scanned by Micro CT to observe the alveolar bone resorption. The tissue mineral density (TMD), bone mineral density (BMD), bone volume fraction (BV/TV), trabecular thickness (Tb.Th), trabecular bone separation (Tb.Sp) were recorded, and the distance from the enamel bone boundary to the alveolar crest (CEJ-ABC) of the maxillary first molar was measured. The oxidative stress indexes such as malondialdehyde, glutathione (GSH), and superoxide dismutase (SOD) were detected using frozen rat kidney tissue. The gene expression of UCP2, nuclear factor erythroid 2-related factor 2 (Nrf2), and peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) was observed by quantitative real-time polymerase chain reaction (qRT-PCR) test. The gingival tissue of the rats was used for immunohistochemical staining to observe the expression of the UCP2 protein. The fixed rat kidney tissue was used for hematoxylin-eosin (HE), periodic acid-schiff (PAS), MitoSOX Red, JC-1, and immunohistochemical staining to observe the renal histopathology, the level of reactive oxygen species (ROS), the level of mitochondrial membrane potential, and the expression of UCP2, Nrf2, and PGC-1α protein. Rat serum was collected to detect renal function indices, namely, blood urea nitrogen (BUN), creatinine (Cre), and albumin (Alb). RESULTS: Compared with the control group, the periodontitis group showed red, swollen, and soft gingival tissue, with gingival probing bleeding, periodontal PD increased, tooth loosening, alveolar bone resorption, decreased TMD, BMD, BV/TV, and Tb.Th indices, and increased Tb.Sp index, CEJ-ABC, and gingival UCP2 protein expression. Compared with the control group, the levels of MDA and ROS in the kidney tissue of periodontitis rats and the gene and protein expression of UCP2 increased, and the levels of MMP, GSH, and SOD and the gene and protein expression of Nrf2 and PGC-1α decreased. Renal functional indices, namely, BUN, Cre, and Alb, were not significantly different between the two groups. CONCLUSIONS: UCP2 may play a role in renal injury induced by periodontitis through oxidative stress.

Impact of circadian clock protein Bmal1 on experimentally-induced periodontitis-associated renal injury.

OBJECTIVES: To investigate the mechanism of circadian clock protein Bmal1 (Bmal1) on renal injury with chronic periodontitis, we established an experimental rat periodontitis model. METHODS: Twelve male Wistar rats were randomly divided into control and periodontitis groups (n=6, each group). The first maxillary molars on both sides of the upper jaw of rats with periodontitis were ligated by using orthodontic ligature wires, whereas the control group received no intervention measures. After 8 weeks, clinical periodontal parameters, including probing depth, bleeding index, and tooth mobility, were evaluated in both groups. Micro-CT scanning and three-dimensional image reconstruction were performed on the maxillary bones of the rats for the assessment of alveolar bone resorption. Histopatholo-gical observations of periodontal and renal tissues were conducted using hematoxylin-eosin (HE) and periodic acid-Schiff (PAS) staining. Renal function indicators, such as creatinine, albumin, and blood urea nitrogen levels, and oxidative stress markers, including superoxide dismutase, glutathione, and malondialdehyde levels, were measured using biochemical assay kits. MitoSOX red staining was used to detect reactive oxygen species (ROS) content in the kidneys. The gene and protein expression levels of Bmal1, nuclear factor erythroid 2-related factor 2 (Nrf2), and heme oxygenase-1 (HO-1) in rat renal tissues were assessed using real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemical staining. RESULTS: Micro-CT and HE staining results showed significant bone resorption and attachment loss in the maxillary first molar region of the periodontitis group. Histological examination through HE and PAS staining revealed substantial histopathological damage to the renal tissues of the rats in the periodontitis group. The findings of the assessment of renal function and oxidative stress markers indicated that the periodontitis group exhibited abnormal levels of oxidative stress, whereas the renal function levels showed abnormalities without statistical significance. MitoSOX Red staining results showed that the content of ROS in the renal tissue of the periodontitis group was significantly higher than that of the control group, and RT-qPCR and immunohistochemistry results showed that the expression levels of Bmal1, Nrf2, and HO-1 in the renal tissues of the rats in the periodontitis group showed a decreasing trend. CONCLUSIONS: Circadian clock protein Bmal1 plays an important role in the oxidative damage process involved in the renal of rats with periodontitis.

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.

Data Augmentation of a Corrosion Dataset for Defect Growth Prediction of Pipelines Using Conditional Tabular Generative Adversarial Networks.

Due to corrosion characteristics, there are data scarcity and uneven distribution in corrosion datasets, and collecting high-quality data is time-consuming and sometimes difficult. Therefore, this work introduces a novel data augmentation strategy using a conditional tabular generative adversarial network (CTGAN) for enhancing corrosion datasets of pipelines. Firstly, the corrosion dataset is subjected to data cleaning and variable correlation analysis. The CTGAN is then used to generate external environmental factors as input variables for corrosion growth prediction, and a hybrid model based on machine learning is employed to generate corrosion depth as an output variable. The fake data are merged with the original data to form the synthetic dataset. Finally, the proposed data augmentation strategy is verified by analyzing the synthetic dataset using different visualization methods and evaluation indicators. The results show that the synthetic and original datasets have similar distributions, and the data augmentation strategy can learn the distribution of real corrosion data and sample fake data that are highly similar to the real data. Predictive models trained on the synthetic dataset perform better than predictive models trained using only the original dataset. In comparative tests, the proposed strategy outperformed other data generation methods.