five

Comprehensive risk assessment revealed some physiological indicators responding to various GM-crop consumption

收藏
DataCite Commons2025-12-20 更新2026-02-09 收录
下载链接:
https://tandf.figshare.com/articles/dataset/Comprehensive_risk_assessment_revealed_some_physiological_indicators_responding_to_various_GM-crop_consumption/30925366
下载链接
链接失效反馈
官方服务:
资源简介:
Genetically modified (GM) crops have been provided as food and feed in over 70 countries in the world. But the concern is persisting on their comprehensive effects on human health status as feedstock. Physiological indicators detected in human beings or animals were explored to assess the health status after GM crop consumption. Here, a mammalian physiological indicators data set with seven metrics containing 25 physiological indicators was constructed by extracting the experimental raw data from the open access research articles published from January 2000 to September 2024 on GM maize, rice, and soybean consumption. To overcome the experimental heterogeneity in disparate model animals, limited animal number in each independent research, and statistical errors caused by different statistical methods, the multi-sourced data correlation analysis with DerSimonian and Laird random-effect model was employed. The result revealed that the concentration of glucose increased after nutritionally changed maize consumption (GLU, <i>p</i> &lt; .01), but within the safe reference concentration range; the relative weight of liver increased after non-nutritional GM maize consumption (<i>p</i> &lt; .05); the relative weight of kidney was the physiological indicators that significantly increased after nutritionally changed GM rice consumption (<i>p</i> &lt; .05). No pathological characterizations in respective organs were reported. The findings indicated no pathological risks from GM crop consumption, though they emphasized the need for continued research into their metabolic and biochemical effects to ensure comprehensive food safety.
提供机构:
Taylor & Francis
创建时间:
2025-12-20
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作