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

Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

3.9K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
3.9K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

783
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
783
Combined Effects of Drugs: Antagonism01:30

Combined Effects of Drugs: Antagonism

8.5K
The combined effects of drugs can result in various interactions, of which an important type is antagonism. Antagonism is a mechanism where one drug inhibits or counteracts the effects of another drug. Antagonism can occur through various means, including receptor binding, allosteric modulation, functional interaction, chemical reactions, and pharmacokinetic processes.
The most common type is receptor antagonism, where one drug acts as an antagonist to block the effects of another drug by...
8.5K
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

5.2K
Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
5.2K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

70
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
70
Agonism and Antagonism: Quantification01:14

Agonism and Antagonism: Quantification

377
When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to...
377

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

Bundle care interventions for patients with perianal pain: A scoping review.

Medicine·2026
Same author

Hypoxia and lactate metabolism-related gene COL5A3 promotes triple negative breast cancer progression via DDR1/FAK/PI3K/AKT pathway.

Biology direct·2026
Same author

Overexpression of β-carboxysomes increases photosynthesis and growth in Synechocystis sp. PCC 6803.

Plant physiology·2026
Same author

Genes From Epithelial-Mesenchymal Transition Predict Overall Survival Effectively in Breast Cancer: A Novel Risk Model Based on Initial Step of Tumor Metastasis.

Breast cancer : basic and clinical research·2026
Same author

M2 macrophages predict response to neoadjuvant chemotherapy in triple negative breast cancer patients.

Scientific reports·2026
Same author

Recognition of immunogenomic signature and prognostic value of the subtype of epithelial-mesenchymal transition in breast cancer.

Biochemistry and biophysics reports·2026

相关实验视频

Updated: Jul 6, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

DMGL-MDA:一种用于微生物与药物关联预测的双模态图形学习方法.

Bei Zhu1, Hao-Yang Yu1, Bing-Xue Du1

  • 1School of Life Sciences, Northwestern Polytechnical University, Xi'an 710072, China.

Methods (San Diego, Calif.)
|January 6, 2024
PubMed
概括

识别微生物与药物关联 (MDA) 对药物安全至关重要. 一个新的计算模型,用于微生物药物协会预测的双模态图形学习 (DMGL-MDA),为预测这些关键相互作用提供了一种卓越,具有成本效益的方法.

科学领域:

  • 微生物学 微生物学
  • 药理学 药理学是指药理学的学科.
  • 计算生物学 计算生物学

背景情况:

  • 微生物与药物相互作用显著影响人类健康.
  • 预测微生物药物协会 (MDAs) 对于安全的药物管理至关重要.
  • 传统的MDA预测实验方法昂贵且耗时.

研究的目的:

  • 开发一种新的计算方法,以高效,准确地预测微生物与药物协会 (MDA).
  • 克服现有的图形神经网络 (GNN) 模型的局限性,例如过度平滑和过度压,以及相似性矩阵依赖的问题.

主要方法:

  • 提出了一种新的图形表示学习模型,命名为微生物药物协会预测的双模态图形学习 (DMGL-MDA).
  • DMGL-MDA包含一个双模态嵌入模块,一个二分位图形网络嵌入模块和一个预测模块.
  • 通过交叉验证对两个基准数据集进行了DMGL-MDA与最先进的方法的评估.

主要成果:

  • 与现有方法相比,DMGL-MDA显示出更高的性能.
  • 交叉验证证实了拟议模型的有效性.
  • 废弃实验和案例研究进一步验证了该模型的预测能力.
关键词:
注意力网络的注意力网络.深度学习是一种深度学习.双模式嵌入方式链接预测链接预测微生物药物协会 微生物药物协会

更多相关视频

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K

相关实验视频

Last Updated: Jul 6, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
07:51

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Published on: May 21, 2018

11.8K
Diagonal Method to Measure Synergy Among Any Number of Drugs
12:08

Diagonal Method to Measure Synergy Among Any Number of Drugs

Published on: June 21, 2018

18.6K

结论:

  • DMGL-MDA提供了一个强大的,高效的计算解决方案,用于预测微生物与药物之间的关联.
  • 该模型解决了现有的基于GNN的方法中的关键挑战,提供了更好的准确性和可靠性.
  • 这项工作促进了潜在的MDA的低成本,高通量选,帮助药物开发和个性化医疗.