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  1. Abdalla MMI, Azzani M, Rajendren R, Hong TK, Balachandran Y, Hassan TR, et al.
    Am J Med Sci, 2021 Oct 01.
    PMID: 34606752 DOI: 10.1016/j.amjms.2021.07.015
    BACKGROUND: Traditional teaching methods via faculty lectures and text-based materials lack interactivity. Hence, this research aimed to compare the effectiveness of story-based audiovisual mnemonics and the conventional text-reading method on medical students' memory consolidation.

    METHODS: A single-center, systematically randomly sampled, single-blinded, controlled study was conducted among 80 first-year medical students. The students were randomly assigned to the text-reading (control) or story-based audiovisual mnemonics (intervention) group. After completing the learning session, the participants immediately took a test that consisted of ten multiple-choice questions, each of which had one correct single-response answer, and an oral recall test that consisted of ten keywords based on the given topics. The test was repeated at 1 week, 2 weeks, and 4 weeks post intervention. Descriptive and inferential statistics were utilized to assess the participants' responses. The mean score difference between the audiovisual mnemonics and control groups was determined by using a two-tailed unpaired t-test.

    RESULTS: The participants in the intervention group had statistically significant higher marks in the multiple-choice test as compared to the participants in the control group. The intervention group also spent a statistically significant shorter time to recall keywords in the oral recall test in comparison to the control group.

    CONCLUSION: The story-based audiovisual mnemonics method is more effective than the conventional text-reading method in promoting memory retention among medical students.

  2. He F, Aebersold R, Baker MS, Bian X, Bo X, Chan DW, et al.
    Nature, 2024 Dec;636(8042):322-331.
    PMID: 39663494 DOI: 10.1038/s41586-024-08280-5
    The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.
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