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相关概念视频

Probiotics01:22

Probiotics

Probiotics are live, non-pathogenic microorganisms that confer health benefits by modulating the gut microbiota. The human gastrointestinal tract harbors a complex microbial ecosystem, and the balance of this microbiota is crucial for digestive and systemic health. Among the most extensively studied and utilized probiotics are species formerly classified within the genera Lactobacillus and Bifidobacterium. These organisms not only naturally colonize the human gut but are also consumed through...
Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
Designing Growth Media for Bioreactors01:30

Designing Growth Media for Bioreactors

Growth media provide essential nutrients that support cell growth and metabolism, thereby enhancing the yield of valuable products such as enzymes, antibiotics, and biomass. Designing an effective growth medium involves balancing all components to prevent nutrient limitations or toxic excesses, both of which can impair growth and reduce product yields.Composition of a Typical Growth MediumA typical growth medium contains carbon and nitrogen sources, salts, vitamins, trace elements, and...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Scale-Up Processes01:14

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
Upstream Processing01:27

Upstream Processing

Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...

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相关实验视频

Updated: May 13, 2026

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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有针对性的益生菌片剂:一种混合的积极学习和有限元素建模方法,用于流程优化.

Bide Wang1, Xilu Wang2, Oleksiy V Klymenko1

  • 1School of Chemistry and Chemical Engineering, University of Surrey, Guildford GU2 7XH, UK.

International journal of pharmaceutics: X
|November 10, 2025
PubMed
概括

优化益生菌片的生产是一项挑战. 一种使用主动学习和有限元素建模的新方法快速识别出理想的压缩设置,以最大限度地提高平板时益生菌的生存率.

关键词:
积极学习是指积极学习.有限元素方法 有限元素方法.高斯过程回归的高斯过程回归.益生菌是一种益生菌.药片制造是指使用药片制造的.

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相关实验视频

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科学领域:

  • 制药科学 制药科学
  • 计算建模 计算建模
  • 生物技术是生物技术.

背景情况:

  • 药片是益生菌的有效输送方法.
  • 之前的研究确定了压缩压力,速度和压缩前作为益生菌生存的关键因素.
  • 实验研究是耗时的,限制了个别参数的优化.

研究的目的:

  • 开发一种系统的方法,以确定在片剂时益生菌生存的最佳过程参数.
  • 克服制药配方中传统实验方法的局限性.
  • 为了加快优化益生菌平板制剂的流程.

主要方法:

  • 集成的积极学习 (AL) 与高斯过程回归 (GPR) 和有限元素 (FE) 建模.
  • 利用FE模型生成数据来预测在药片注射期间的益生菌活力.
  • 采用全球随机抽样和值过来确定最佳参数区域.

主要成果:

  • 在78次代后实现了益生菌生存率的高预测性能 (R2=0.96).
  • 成功确定了近乎最佳的益生菌生存区域.
  • 生成的生存率图显示了生存率和平板电脑机械性能之间的相互作用.

结论:

  • 混合数据驱动和第一原则建模为优化益生菌片剂的优化提供了一个强大的战略.
  • 这种方法通过实现高效的流程优化来加速制药开发.
  • 该研究强调了计算方法在提高药物输送系统设计方面的潜力.