Objective: The COVID-19 caused a world pandemic, posing a huge threat to global health. Widespread vaccination is the most effective way to control the pandemic. Vaccination with the third dose of the COVID-19 vaccine is currently underway. We aimed to determine the attitude of adolescents toward the third dose of COVID-19 vaccine. Methods: A structured questionnaire was administered between 16 August and 28 October 2021 among adolescents aged 12–17 years in three provinces of eastern region of China based on convenience sampling. The questionnaire was specifically developed to assess the adolescents’ attitude toward and willingness to accept a third dose of the COVID-19 vaccine. Results: In total, 94.3% (1742/1847) of the adolescents intended to accept the third dose of the COVID-19 vaccine. Age between 15–17 years, no worry about vaccine safety, confidence for vaccine effectiveness, and supporting opinion from parents were independently associated with acceptance of the third dose (p < 0.05). Conclusions: It is necessary for governments and school administrators to raise adolescents’ and parents’ awareness of the benefits and safety of the third dose of vaccination, which should be effective to increase the vaccination coverage among adolescents.

We have used a uniform design to explore the most effective directed differentiation medium (MEDDM) for differentiating mouse bone marrow mesenchymal stem cells (mMSCs) into hepatocytes. Our methods involved arranging eight differentiation medium groups following uniform design. Flow cytometry was used to evaluate the percentage of ALB+ and CK18+ cells in each group. Factors and their concentrations in the MEDDMs were then identified. The MEDDMs were evaluated by their ability to differentiate mMSCs into hepatocytes by RNA and protein expressions and synthesis functions. FGF at 35 ng/ml and OSM at 30 ng/ml in the medium yielded the highest percentage of ALB+ and CK18+ cells. During directed differentiation using MEDDMs, ALB, CK18, TTR, AFP mRNAs were expressed. ALB and CK18 proteins were detected in the cells. The differentiated cells produced albumin and urea in a time dependent manner. Uniform design was adequate for choosing the MEDDM of mMSCs. MEDDM containing 35 ng/ml FGF and 30 ng/ml OSM was effective in differentiating mMSCs into hepatocytes.

Object. This study is aimed at constructing a deep learning architecture of the autoencoder to integrate multiomics data and identify the risk of patients with stomach adenocarcinoma. Methods. Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k -means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model’s robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints. Results. The patients were divided into low-risk and high-risk groups according to the k -means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80 e − 06 ; adjusted   hazard   ratio = 2.386 , 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository. Conclusions. A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.

Aims: To investigate whether peripheral neuropathy scale scores are associated with myocardial infarction (MI) in patients with type 2 diabetes mellitus (T2DM). Materials and Methods: In this cross-sectional study, 32,463 T2DM patients were enroled from 103 tertiary hospitals in 25 Chinese provinces. Based on a history of MI, participants were divided into the MI group (n = 4170) and the non-MI group (n = 28,293). All patients were assessed using four neuropathy scales, namely, Neurological Symptom Score (NSS), Neurological Disability Score (NDS), Toronto Clinical Scoring System (TCSS), and Michigan Neuropathy Screening Instrument (MNSI), and some of the patients underwent evaluation of nerve conduction velocity (NCV) (n = 20,288). The relationship between these scores and myocardial infraction was analysed. Results:The neuropathy scale scores in the MI group were higher than those in the non-MI group (p < 0.001). After dividing patients into four groups based on the grading criteria, our results showed that, in addition to aggravating the degree of neuropathy signs, the incidence of MI increased (p < 0.001). Logistic regression analysis results showed that neuropathy scale scores and NCV were both independent risk factors for MI (p < 0.001). Furthermore, among the scales used, MNSI presented a higher odds ratio and area under the curve (AUC; 0.625, p < 0.001) than the other three scales (AUC NSS = 0.575, AUC NDS = 0.606, and AUC TCSS = 0.602, p < 0.001) for MI.

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