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

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

Targeted IL-4 nanoparticles for osteal macrophages modulation in osteoporosis.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences·2026
Same author

ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.

Plant methods·2026
Same author

A Reproducible Workflow for Macrophage Membrane Isolation and Nanocore Selection Toward Bioinspired Nanoparticles Targeting Immunologically Cold Tumors.

Small methods·2026
Same author

3D-printed chitosan-starch mesh filled with minocycline-alginate hydrogel for dual anti-Staphylococcus aureus and osteogenic effects.

Carbohydrate polymers·2025
Same author

Novel Core-Shell Aerogel Formulation for Drug Delivery Based on Alginate and Konjac Glucomannan: Rational Design Using Artificial Intelligence Tools.

Polymers·2025
Same author

Design of Clofazimine-Loaded Lipid Nanoparticles Using Smart Pharmaceutical Technology Approaches.

Pharmaceutics·2025

相关实验视频

Updated: May 29, 2026

Solid Lipid Nanoparticles SLNs for Intracellular Targeting Applications
08:19

Solid Lipid Nanoparticles SLNs for Intracellular Targeting Applications

Published on: November 17, 2015

17.8K

通过使用混合人工智能工具进行顺序设计,通过工程方法使功能化的纳米结构脂质载体具有功能.

Rebeca Martinez-Borrajo1, Patricia Diaz-Rodriguez2, Mariana Landin1

  • 1Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, Grupo I+D Farma (GI-1645), Facultad de Farmacia, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Instituto de Materiais da Universidade de Santiago de Compostela (iMATUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.

Drug delivery and translational research
|May 29, 2024
PubMed
概括

人工智能优化了基化纳米结构脂质载体 (NLCs) 进行向药物输送. 这种方法增强了巨细胞的吸收,改善了炎症疾病的治疗方法.

关键词:
人工智能的人工智能是人工智能.人工神经网络的人工神经网络碳水化合物的表面功能化.遗传算法 遗传算法 遗传算法曼诺西拉的优化优化纳米结构的脂质载体神经模糊逻辑是指神经模糊逻辑.从设计开始的质量.

更多相关视频

Facile Preparation of Internally Self-assembled Lipid Particles Stabilized by Carbon Nanotubes
09:47

Facile Preparation of Internally Self-assembled Lipid Particles Stabilized by Carbon Nanotubes

Published on: February 19, 2016

9.6K
Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device
06:02

Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device

Published on: March 22, 2022

9.0K

相关实验视频

Last Updated: May 29, 2026

Solid Lipid Nanoparticles SLNs for Intracellular Targeting Applications
08:19

Solid Lipid Nanoparticles SLNs for Intracellular Targeting Applications

Published on: November 17, 2015

17.8K
Facile Preparation of Internally Self-assembled Lipid Particles Stabilized by Carbon Nanotubes
09:47

Facile Preparation of Internally Self-assembled Lipid Particles Stabilized by Carbon Nanotubes

Published on: February 19, 2016

9.6K
Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device
06:02

Production of siRNA-Loaded Lipid Nanoparticles using a Microfluidic Device

Published on: March 22, 2022

9.0K

科学领域:

  • 生物材料科学 生物材料科学
  • 纳米技术纳米技术
  • 药物运输 药物运输 药物运输

背景情况:

  • 纳米结构脂质载体 (NLC) 是有前途的药物递送系统 (DDS),因为它们的尺寸和药物载荷能力.
  • 表面功能化,就像曼诺化一样,增强了纳米粒子与巨细胞的相互作用,以实现有针对性的传递.
  • 开发功能化的NLC是复杂的,需要优化多个变量,缺乏功能化效率评估.

研究的目的:

  • 采用混合人工智能 (AI) 技术来设计含药物载荷的曼诺西拉NLC.
  • 优化NLC复杂的功能化过程,以改善药物输送.
  • 第一次使用化学改性mannose量化和优化功能化效率.

主要方法:

  • 利用人工神经网络与模糊逻辑或遗传算法相结合,以建模NLC形成.
  • 使用AI优化了各种功能化步骤的变量组合.
  • 化学修饰的曼诺斯,以实现功能化效率量化和优化.

主要成果:

  • 开发了稳定的曼诺化NLCs的强有力的序列方法.
  • 达到小颗粒大小 (<100 nm) 均分布和高正泽塔电位 (>20 mV).
  • 在NLC表面上实现了超过85%的功能化效率,用于Mannose的整合.

结论:

  • 人工智能驱动的设计提供了一个强大的程序,用于创建优化的曼诺西拉NLCs.
  • 高功能的效率提高了巨细胞的识别和NLCs的内部化.
  • 这种方法促进了针对性药物输送,用于治疗慢性炎症疾病.