Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
Site-Targeted Drug Delivery Systems: Polymeric Carriers01:24

Site-Targeted Drug Delivery Systems: Polymeric Carriers

Polymeric carriers enhance targeted drug delivery by increasing efficacy while minimizing off-target effects. These carriers comprise a biodegradable polymeric backbone integrated with functional elements that enable targeting, improve physicochemical properties, and regulate drug release.Targeting MechanismsThe targeting ability of polymeric carriers is mediated by a homing device, which is a molecular recognition component designed to selectively bind to specific tissues or cells. Monoclonal...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.

Biomacromolecules·2026
Same author

Mitochondrial transfer from glia to neurons protects against peripheral neuropathy.

Nature·2026
Same author

Thermally Driven Release of Oxycodone from Poly(ester urea) Thin Films by Printed Microheaters for Transdermal Delivery.

ACS applied materials & interfaces·2025
Same author

Additive Accelerated Meloxicam Release from Poly(Ester Urea) Fiber Implants for Acute Pain Management.

Advanced healthcare materials·2025
Same author

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.

Machine learning: science and technology·2025
Same author

Polysialic Acid-Functionalized MAP Scaffolds Promote Regulatory Immune Responses After Ischemic Stroke.

bioRxiv : the preprint server for biology·2025

相关实验视频

Updated: Jul 6, 2026

Polymer Microarrays for High Throughput Discovery of Biomaterials
13:37

Polymer Microarrays for High Throughput Discovery of Biomaterials

Published on: January 25, 2012

14.6K

应用机器学习作为聚合物生物材料设计的驱动因素

Samantha M McDonald1, Emily K Augustine2, Quinn Lanners3

  • 1Department of Chemistry, Duke University, Durham, NC, USA.

Nature communications
|August 10, 2023
PubMed
概括

机器学习 (ML) 可以加速用于医疗用途的新型聚合物生物材料的发现. 然而,标准化医疗相关数据的挑战阻碍了先进的医疗级聚合物的机器学习辅助设计.

更多相关视频

Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold
09:37

Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold

Published on: October 23, 2015

12.7K
Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
09:22

Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications

Published on: August 28, 2015

19.2K

相关实验视频

Last Updated: Jul 6, 2026

Polymer Microarrays for High Throughput Discovery of Biomaterials
13:37

Polymer Microarrays for High Throughput Discovery of Biomaterials

Published on: January 25, 2012

14.6K
Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold
09:37

Fabrication of a Bioactive, PCL-based "Self-fitting" Shape Memory Polymer Scaffold

Published on: October 23, 2015

12.7K
Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications
09:22

Fabricating Superhydrophobic Polymeric Materials for Biomedical Applications

Published on: August 28, 2015

19.2K

科学领域:

  • 生物材料科学 生物材料科学
  • 聚合物化学 聚合物化学
  • 医疗器械开发 医疗器械开发

背景情况:

  • 聚合物在现代医学中至关重要,但可用的医疗级聚合物的多样性是有限的.
  • 大量投资旨在开发新的聚合物生物材料,以满足临床需求.
  • 目前的医疗级聚合物存在一些限制,新材料设计旨在克服这些限制.

研究的目的:

  • 探索机器学习 (ML) 在加速新型聚合物生物材料设计方面的潜力.
  • 确定阻碍在生物医学聚合物设计中应用ML的关键挑战.
  • 为 ML 和生物材料的交叉点提供未来研究方向的前景.

主要方法:

  • 关于在聚合物设计中应用机器学习的当前文献的综述.
  • 分析用于解决ML驱动材料发现中的数据可用性的策略.
  • 识别用于医疗应用的聚合物表征的关键数据缺口.

主要成果:

  • 机器学习为减少聚合物开发中的试错合成提供了一个有希望的途径.
  • 在聚合物设计中,现有的ML方法通常依赖于组合和高通量实验方法.
  • 用ML辅助的生物材料设计的一个重大障碍是缺乏标准化,与药物相关的表征数据 (例如降解,生物相容性).

结论:

  • 在应用机器学习和生物医学聚合物设计的具体需求之间存在差距.
  • 标准化表征数据对于实现ML在开发先进的医疗级聚合物方面的全部潜力至关重要.
  • 应对数据标准化挑战将是未来在机器学习驱动的生物材料发现创新的关键.