用保护生物学的综合思想教授遗传学。

Q3 Medicine 遗传 Pub Date : 2024-07-01 DOI:10.16288/j.yczz.24-127
Jian-Qing Lin, Shao-Pan Ye, Shu-Qi Wang, Hong Du
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引用次数: 0

摘要

随着全球人口的急剧增长和人类对自然资源的过度开发,生物多样性也在不断丧失。遗传因素在保护濒危物种方面发挥着重要作用。保护遗传学是遗传学和保护生物学的交叉学科。高校没有开设保护遗传学课程,遗传学课程也没有直接体现生物保护的内容。我们在遗传学教学中融入了保护生物学的思想。我们以案例分析的形式,将相关领域的最新研究进展和技术融入遗传学课堂。由此,提高了本科生主动学习的积极性和兴趣,引发了 "遗传学基础知识、生态保护意识、跨学科思维培养 "的相互促进,为培养既掌握扎实基础知识,又具有生态文明理念的跨学科人才奠定了基础。
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Teaching genetics with integrative thoughts of conservation biology.

Biodiversity losses along with the exponential growth of global human population and human-provoked over-exploitation of natural resources. Genetic factors played an important role in the conservation of endangered species. Conservation genetics is a cross-field disciplinary of genetics and conservation biology. The course of conservation genetics is not available in colleges and universities, and the course of genetics does not directly reflect the content of biological conservation. We have taught genetics with integrative thoughts of conservation biology. In the form of case studies, we have integrated recent advances of research and technology in the relevant fields into the genetics classroom. As a result, we improved the undergraduates' motivation and interest in active learning, provoked the mutual promotion of "basic knowledge of genetics, awareness of ecological protection, and cultivate interdisciplinary thinking", and set up the groundwork for cultivating interdisciplinary talents who not only master solid basic knowledge, but also have the concept of ecological civilization.

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来源期刊
遗传
遗传 Medicine-Medicine (all)
CiteScore
2.50
自引率
0.00%
发文量
6699
期刊介绍:
期刊最新文献
Advancements and prospects in reconstructing the genetic genealogies of ancient and modern human populations using ancestral recombination graphs. Advances in high throughput sequencing methods for DNA damage and repair. Application of Mendelian randomization analysis in investigating the genetic background of blood biomarkers for colorectal cancer. Computational dissection of the regulatory mechanisms of aberrant metabolism in remodeling the microenvironment of breast cancer. Gut metagenome-derived image augmentation and deep learning improve prediction accuracy of metabolic disease classification.
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