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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
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Effects of Chemicals: Overview01:27

Effects of Chemicals: Overview

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Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...
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Agonism and Antagonism: Quantification01:14

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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...
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Pharmacokinetics: Drug–Drug Interactions01:25

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Drug interactions occur when the pharmacological effect of one drug is altered by another substance, either enhancing or diminishing its activity. The drug whose activity is altered is known as the object drug, and the substance causing the alteration is called the agent drug or the precipitant. The net effects of these interactions are mostly undesirable, leading to decreased effectiveness or increased adverse effects. In rare cases, interactions can be beneficial, such as the enhanced...
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Therapeutic Drug Monitoring: Affecting Factors01:29

Therapeutic Drug Monitoring: Affecting Factors

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Therapeutic Drug Monitoring (TDM) is the clinical practice of measuring specific drug levels in a patient's blood or body tissues to manage and optimize therapy. TDM is crucial for drugs with narrow therapeutic windows, like warfarin and phenytoin, where incorrect doses can lead to treatment failure or severe side effects. This monitoring ensures the dosage administered is within a safe and effective range. The factors affecting therapeutic drug monitoring include:Patient-Specific Factors:a.
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Combined Effects of Drugs: Antagonism01:30

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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...
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Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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因果知识图分析确定了药物的不良影响.

Sumyyah Toonsi1, Paul N Schofield2, Robert Hoehndorf1,3,4

  • 1Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia.

Bioinformatics (Oxford, England)
|December 12, 2025
PubMed
概括
此摘要是机器生成的。

我们介绍了因果知识图 (CKG),将生物医学知识图与因果推理结合起来. 这种方法成功地确定了已知的和新的不良药物影响,改善了药物适用性预测.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 因果推理因果推理
  • 知识表示 知识表示

背景情况:

  • 生物医学知识图和因果模型是有价值的,但没有联系.
  • 知识图缺乏概率语义;因果模型缺乏背景知识整合.

研究的目的:

  • 弥合知识图和因果模型之间的差距.
  • 开发因果知识图 (CKG) 用于原则性因果推断和假设制定.

主要方法:

  • 扩展了具有正式因果语义的知识图,创建了因果知识图 (CKG).
  • 构建了一个药物-疾病 CKG (DD-CKG) 整合疾病路径,药物指示和副作用.
  • 应用DD-CKG到英国生物银行和MIMIC-IV进行大规模中介药物效应分析.

主要成果:

  • 使用背景知识,CKG使解惑和假设制定成为可能.
  • 成功复制已知的不良药物反应,并确定了新的候选效应.
  • 通过将预测的效果与现有数据库相结合,改善了共享药物指示的预测.

结论:

  • CKG为可扩展的因果推理提供了一个可概括的,基于知识的框架.
  • 该方法证明了临床相关性,并支持自动化大规模调解分析.
  • 这种方法提高了对药物效应和疾病进展的理解.