Publications by authors named "J Q He"

The knowledge of "clinical molecular biology testing technology" is complex, conceptual expressions are abstract and difficult to understand, and the student's interest in learning is low. This study aimed to evaluate the effectiveness of a cyclic teaching method based on case analysis combined with an exploratory teaching method using mind mapping as an assignment. Students from the 2019 cohort of medical laboratory technology at Hunan University of Chinese Medicine served as the control group and received conventional lecture-based teaching methods.

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Aims: Previous studies have shown that eGDR and TyG, as indicators of insulin resistance (IR), were key risk factors for cardiovascular disease (CVD). Our study further explored the relationship between eGDR change and new-onset CVD, and compared the predictive value of eGDR change, eGDR and TyG.

Materials And Methods: A total of 2895 participants without CVD at baseline from the China Health and Retirement Longitudinal Study (CHARLS) were included, using K-means clustering and cumulative eGDR to measure eGDR change between 2012 and 2015.

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Water vapor is a significant component in real volatile organic compounds (VOCs) exhaust gas and has a considerable impact on the catalytic performance of catalysts for VOC oxidation. Important progress has been made in the reaction mechanisms of HO and water resistance strategies for VOC oxidation in recent years. Despite advancements in catalytic technology, most catalysts still exhibit low activity under humid conditions, presenting a challenge in reducing the adverse effects of HO on VOC oxidation.

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Horn is a defensive weapon of sheep, consisting of a horny sheath and a bony core. The KRT2 gene is related to keratinization of the epidermis, so it is likely to be one of the contributor genes affecting horn type in sheep. In this study, we first analyzed the species-specific and tissue-specific expression of the KRT2 gene using transcriptome sequencing data.

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The consumption forecasting of oil and coal can help governments optimize and adjust energy strategies to ensure energy security in China. However, such forecasting is extremely challenging because it is influenced by many complex and uncertain factors. To fill this gap, we propose a hybrid deep learning approach for consumption forecasting of oil and coal in China.

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