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Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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相关实验视频

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Author Spotlight: Process Development for the Spray-Drying of Probiotic Bacteria and Evaluation of the Product Quality
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通过主动机器学习来增强精密益生菌的配方.

Anweshit Panda1, Manaswani Adhikari2, Sourya Subhra Nasker2

  • 1School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University (KIIT-DU), Bhubaneswar, 751024, India.

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概括

主动机器学习 (ML) 准确预测辅助剂如何影响益生菌生长,优化配方. 这种方法提高了通过胃肠道的益生菌活力,改善了肠道健康.

关键词:
乳酸菌植物植物 (Lactobacillus plantarum) 是一种植物菌.积极的机器学习.梯度增强可以提高梯度.增长 预测 增长 预测制药辅助剂 制药辅助剂 制药辅助剂精密益生菌 精密益生菌支持矢量机器的支持矢量机器.

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科学领域:

  • 微生物学 微生物学
  • 生物信息学是一种生物信息学.
  • 药理学 药理学是指药理学的学科.

背景情况:

  • 人的肠道微生物群在整体健康中起着至关重要的作用.
  • 微生物失衡 (失生症) 与各种疾病有关.
  • 益生菌干预的目的是恢复微生物平衡,但难以确保在胃肠道过境期间的生存.

研究的目的:

  • 采用先进的主动机器学习 (ML) 方法来预测辅助剂对*Lactobacillus plantarum*生长的影响.
  • 加强辅助剂的选择,以提高益生菌配方的稳定性和有效性.

主要方法:

  • 在三轮代过程中利用一个活跃的ML策略来预测辅助剂对*Lactobacillus plantarum*生长的影响.
  • 训练和评估了五种ML模型:神经网络,梯度增强,后勤回归,随机森林和支持向量机器.
  • 对比连续模型优化与每次代重新训练新模型.

主要成果:

  • 成功预测了116种辅助剂对益生菌生长的影响,达到接近90%的模型确定性水平.
  • 与传统方法相比,主动ML方法显示出更高的预测稳定性和更低的不确定性传播.
  • 验证的ML预测与补充的最先进的实验数据.

结论:

  • 活跃ML是预测辅助剂-益生菌相互作用的强大工具.
  • 这种方法可以显著提高选择益生菌配方辅料的准确性和效率.
  • 这些发现支持开发更有效的精确益生菌疗法.