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相关概念视频

Opioid Analgesics: Morphine and Other Natural Cogeners01:20

Opioid Analgesics: Morphine and Other Natural Cogeners

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Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
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Analgesia and Pain Management01:25

Analgesia and Pain Management

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Pain is critical to various clinical pathologies, provoking an urgent need for effective management. Pain, whether acute or chronic, is a complex neurochemical process. Its alleviation depends on the type, with nonopioid analgesics effective for mild to moderate pain, such as musculoskeletal or inflammatory pain, while neuropathic pain responds best to anticonvulsants, tricyclic antidepressants, or serotonin/norepinephrine reuptake inhibitors. For severe acute or chronic pain, opioids may be...
665
Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

346
Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
346
Opioid Receptors: Overview01:22

Opioid Receptors: Overview

1.1K
Opioid receptors, including the mu (μ, MOR), delta (δ, DOR), and kappa (κ, KOR) types, belong to the rhodopsin family of G protein-coupled receptors. These receptors are located throughout the central and peripheral nervous systems and in non-neuronal tissues such as macrophages and astrocytes. Opioid receptor ligands can be categorized into agonists or antagonists. Highly selective agonists include [d-Ala2, MePhe4, Gly(ol)5]-enkephalin or DAMGO for MOR, [D-Pen2,...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

789
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
789
Drug Abuse and Addiction: Pharmacological Phenomena01:15

Drug Abuse and Addiction: Pharmacological Phenomena

552
Drug dependence, abuse, and addiction are complex phenomena that can precipitate various abnormal states. Physical dependence refers to a state of pharmacological adaptation to a drug. This adaptation often results in tolerance—a reduced response to the drug after repeated administrations. When the drug use is abruptly stopped, withdrawal symptoms occur due to the body's need to readjust from the pharmacologically induced imbalance. However, tolerance and withdrawal symptoms do not...
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Updated: Jul 24, 2025

Combining Laser Capture Microdissection and Microfluidic qPCR to Analyze Transcriptional Profiles of Single Cells: A Systems Biology Approach to Opioid Dependence
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多目标分子优化用于阿片类药物使用障碍治疗使用生成网络复杂的治疗.

Hongsong Feng, Rui Wang, Chang-Guo Zhan

    ArXiv
    |July 3, 2023
    PubMed
    概括

    研究人员开发了一种新的AI模型,用于生成阿片类药物使用障碍 (OUD) 的潜在药物候选者. 这种深度生成方法有助于发现针对阿片类受体的新药,并优化其类似药物的特性.

    科学领域:

    • 计算化学和药理学计算化学和药理学
    • 人工智能在药物发现中的作用

    背景情况:

    • "阿片类药物使用障碍 (OUD) 是一个严重的全球健康挑战,有效的治疗选择有限.
    • 迫切需要新药来治疗OUD和相关疾病.

    结论:

    • 开发的机器学习平台是设计和优化用于OUD治疗的新分子的宝贵工具.
    • 需要进一步的实验验证,以确认产生的化合物的药理作用和治疗潜力.

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