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  1. Zhao J, Liu E
    Front Psychol, 2022;13:1031615.
    PMID: 36578679 DOI: 10.3389/fpsyg.2022.1031615
    INTRODUCTION: In 2020, COVID-19 forced higher education institutions in many countries to turn to online distance learning. The trend of using online education has accelerated across the world. However, this change in the teaching mode has led to the decline of students' online learning quality and resulted in students being unable to do deep learning. Therefore, the current research, aimed at promoting deep learning in the online environment, constructed a theoretical model with learning self-efficacy and positive academic emotions as mediators, deep learning as the dependent variable, perceived TPACK support, peer support, technical usefulness, and ease of use as independent variables.

    METHODS: The theoretical model was verified by SPSS26.0 and smartPLS3.0, and to assess the measurement and structural models, the PLS approach to structural equation modeling (SEM) was performed.

    RESULTS: The study found that (a) positive academic emotions play a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning; (b) learning self-efficacy plays a mediating role between perceived TPACK support and deep learning, perceived peer support and deep learning, and perceived technology usefulness and ease of use and deep learning.

    DISCUSSION: The findings of this study fill the gaps in the research on the theoretical models of deep learning in the online environment and provide a theoretical basis for online teaching, learning quality, and practical improvement strategies.

  2. Liu E, Zhao J, Sofeia N
    Front Psychol, 2021;12:793548.
    PMID: 35095678 DOI: 10.3389/fpsyg.2021.793548
    In recent years, deep learning as the requirement of higher education for students has attracted the attention of many scholars, and previous studies focused on defining deep learning as the deep processing of knowledge of the brain, however, in the process of knowledge processing, the brain not only involves the deep processing of information but also participates in learning consciously and emotionally. Therefore, this research proposed a four-factor model hypothesis for deep learning that includes deep learning investment, deep cognitive-emotional experience, deep information processing, and deep learning meta-cognitive. In addition, the research proposed teachers' emotional support perceived by students has an effect on the four factors of deep learning. Through SPSS 26 and AMOS 24, this research has verified the four-factor model of deep learning applying exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) and verified that the perceived teacher emotional support has an impact on the four factors of students' deep learning using the SEM.
  3. Ngamphiw C, Assawamakin A, Xu S, Shaw PJ, Yang JO, Ghang H, et al.
    PLoS One, 2011;6(6):e21451.
    PMID: 21731755 DOI: 10.1371/journal.pone.0021451
    The HUGO Pan-Asian SNP consortium conducted the largest survey to date of human genetic diversity among Asians by sampling 1,719 unrelated individuals among 71 populations from China, India, Indonesia, Japan, Malaysia, the Philippines, Singapore, South Korea, Taiwan, and Thailand. We have constructed a database (PanSNPdb), which contains these data and various new analyses of them. PanSNPdb is a research resource in the analysis of the population structure of Asian peoples, including linkage disequilibrium patterns, haplotype distributions, and copy number variations. Furthermore, PanSNPdb provides an interactive comparison with other SNP and CNV databases, including HapMap3, JSNP, dbSNP and DGV and thus provides a comprehensive resource of human genetic diversity. The information is accessible via a widely accepted graphical interface used in many genetic variation databases. Unrestricted access to PanSNPdb and any associated files is available at: http://www4a.biotec.or.th/PASNP.
  4. Kanagalingam J, Wahid MIA, Lin JC, Cupino NA, Liu E, Kang JH, et al.
    Support Care Cancer, 2018 Jul;26(7):2191-2200.
    PMID: 29387994 DOI: 10.1007/s00520-018-4050-3
    PURPOSE: This descriptive cross-sectional survey aims to assess the level of concordance between the perspectives of oncologists and those of patients regarding oral mucositis (OM) symptoms, and the impact of OM on various aspects of daily living and concurrent cancer management.

    METHODS: Oncologists involved in OM management (n = 105), and patients who developed OM during cancer treatment (n = 175), were recruited from seven Asian countries. Oncologists completed a face-to-face, quantitative interview; patients completed a face-to-face interview, and a self-reported questionnaire.

    RESULTS: Oncologists and patients ranked treatment-induced OM among the three most important toxicities of cancer therapy requiring intervention. The most frequent OM symptoms reported by patients were oral ulcers (74%), dry mouth (73%), and difficulty swallowing (62%). Oncologists expected mild OM symptoms to last slightly longer than 1 week, whereas patients reported mild symptoms for more than 2 weeks. In mild-to-moderate OM, oncologists underestimated patients' pain experience. Overall, only 45% of oncologists said they would initiate OM prophylaxis when cancer therapy started. Of the 87% of patients who said they used their prescribed medications, only 16% reported using prophylactically prescribed medication. While oncologists' concerns related to the delays and interruptions of cancer treatment, patients tended to focus on the effects of OM on eating, drinking, and talking.

    CONCLUSIONS: Oncologists' and patients' perceptions about treatment-induced OM differ. To overcome discordant perspectives, there is a need to raise general awareness and improve proactive management of OM. As noted in recent guidelines, supportive cancer care is critical for ensuring optimal therapy and for improving the patient's experience.

  5. Kanis JA, Harvey NC, McCloskey E, Bruyère O, Veronese N, Lorentzon M, et al.
    Osteoporos Int, 2020 Apr;31(4):797-798.
    PMID: 32065251 DOI: 10.1007/s00198-020-05297-0
    The article 'Algorithm for the management of patients at low, high and very high risk of osteoporotic fractures',written by J. A. Kanis, was originally published Online First without Open Access. After publication in volume [#], issue [#] and page [#-#], the author decided to opt for Open Choice and to make the article an Open Access publication.
  6. Kanis JA, Harvey NC, McCloskey E, Bruyère O, Veronese N, Lorentzon M, et al.
    Osteoporos Int, 2020 Jan;31(1):1-12.
    PMID: 31720707 DOI: 10.1007/s00198-019-05176-3
    Guidance is provided in an international setting on the assessment and specific treatment of postmenopausal women at low, high and very high risk of fragility fractures.

    INTRODUCTION: The International Osteoporosis Foundation and European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis published guidance for the diagnosis and management of osteoporosis in 2019. This manuscript seeks to apply this in an international setting, taking additional account of further categorisation of increased risk of fracture, which may inform choice of therapeutic approach.

    METHODS: Clinical perspective and updated literature search.

    RESULTS: The following areas are reviewed: categorisation of fracture risk and general pharmacological management of osteoporosis.

    CONCLUSIONS: A platform is provided on which specific guidelines can be developed for national use to characterise fracture risk and direct interventions.

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