使用机器学习来识别影响抗体产生和COVID-19疫苗接种后不良反应的因素.
Nahomi Miyamoto1, Tohru Yamaguchi2, Yoshinori Tamada2
1Research Institute of Health Innovation, Hirosaki University, 5 Zaifu-cho, Hirosaki-shi 036-8562, Aomori, Japan.
Vaccines
|February 26, 2026
概括
根据个人因素,COVID-19疫苗的反应有所不同. 女性性别,年轻年龄和特定的健康标志物影响不良反应和抗体产生,绿茶可能会提高免疫力.
科学领域:
- 免疫学 免疫学 免疫学
- 疫苗学 疫苗学 疫苗学
- 生物统计学 生物统计学
背景情况:
- COVID-19 疫苗使用封装在脂质纳米颗粒中的mRNA技术.
- 肌肉内注射是这些疫苗的标准输送方法.
- 了解影响疫苗反应的因素对于公共卫生至关重要.
研究的目的:
- 为了调查影响COVID-19疫苗接种后抗体生产的因素.
- 为了确定与疫苗接种后不良反应相关的变量.
- 通过使用多omics,探索健康数据和疫苗反应之间的因果关系.
主要方法:
- 在Iwaki健康促进项目 (IHPP) 的211名参与者中,对抗体标位和不良反应进行了调查.
- 机器学习算法的应用 (回归,弹性网,光梯度增强,神经网络) 用于变量提取.
- 贝叶斯网络分析以确定健康数据,多omics和疫苗结果之间的因果关系.
主要成果:
- 自由丸激素较低的女性和年轻人表现出更多的不良反应和更高的抗体产生.
- 斯派克瓦克斯疫苗接种与发烧有关,抗体产生较高;绿茶消费与抗体水平增加有关.
- 影响副作用风险的因素包括自然杀手细胞数量,肌肉质量,血代谢组,肠道微生物群和叶酸摄入量.
结论:
- 确定了影响COVID-19疫苗反应的因素,包括性别,年龄,生活方式和特定生物标志物.
- 研究结果表明,针对个性化的疫苗策略和生活方式干预措施有潜力,以提高疫苗的疗效.
- 进一步的研究可以为新疫苗的开发提供信息,并优化公共卫生建议.
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