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  1. Mapping gender networks of music self-concept and music emotions: A network analysis study of music majors in China
    Yang X, Song L, Zhao Y, Cheng D
    Br J Educ Psychol, 2024 Jun;94(2):642-660.
    PMID: 38418284 DOI: 10.1111/bjep.12671
    BACKGROUND: Students' music self-concept and music emotions are becoming prominent topics within the area of music education.

    AIMS, SAMPLES AND METHODS: The majority of previous research on self-concept and music emotions has examined the two constructs independently and focused on gender differences in externalizing behaviours in music learning, but has neglected the internal interactions between individual music self-concept and music emotions. Network analysis is a promising method for visually examining music self-concept and music emotions as part of a network of interactions to identify core features and interrelationships among nodes in the network. In this study, 515 students majoring in music from a Chinese university were recruited.

    RESULTS: The results showed that high music self-concept and boredom were the common features at the core of the network for both men and women college students. The boredom exhibited by women differed from that of men in that men's boredom was directed at the entire music course, while boredom in women manifested as daydreaming and boredom with learning materials.

    CONCLUSIONS: This study is the first to explore gender differences in the music self-concept and music emotions from a holistic perspective. The findings could help music teachers gain insight into the complex system of music self-concept and music emotions. Music teachers could capture the respective features of men and women to design individualized teaching strategies.

  2. Fulltext The one health perspective to improve environmental surveillance of zoonotic viruses: lessons from COVID-19 and outlook beyond
    Leifels M, Khalilur Rahman O, Sam IC, Cheng D, Chua FJD, Nainani D, et al.
    ISME Commun, 2022;2(1):107.
    PMID: 36338866 DOI: 10.1038/s43705-022-00191-8
    The human population has doubled in the last 50 years from about 3.7 billion to approximately 7.8 billion. With this rapid expansion, more people live in close contact with wildlife, livestock, and pets, which in turn creates increasing opportunities for zoonotic diseases to pass between animals and people. At present an estimated 75% of all emerging virus-associated infectious diseases possess a zoonotic origin, and outbreaks of Zika, Ebola and COVID-19 in the past decade showed their huge disruptive potential on the global economy. Here, we describe how One Health inspired environmental surveillance campaigns have emerged as the preferred tools to monitor human-adjacent environments for known and yet to be discovered infectious diseases, and how they can complement classical clinical diagnostics. We highlight the importance of environmental factors concerning interactions between animals, pathogens and/or humans that drive the emergence of zoonoses, and the methodologies currently proposed to monitor them-the surveillance of wastewater, for example, was identified as one of the main tools to assess the spread of SARS-CoV-2 by public health professionals and policy makers during the COVID-19 pandemic. One-Health driven approaches that facilitate surveillance, thus harbour the potential of preparing humanity for future pandemics caused by aetiological agents with environmental reservoirs. Via the example of COVID-19 and other viral diseases, we propose that wastewater surveillance is a useful complement to clinical diagnosis as it is centralized, robust, cost-effective, and relatively easy to implement.
  3. Integrated image-based deep learning and language models for primary diabetes care
    Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, et al.
    Nat Med, 2024 Jul 19.
    PMID: 39030266 DOI: 10.1038/s41591-024-03139-8
    Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P 
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