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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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Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

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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...
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Analgesia and Pain Management01:25

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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...
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Opioid Receptors: Overview01:22

Opioid Receptors: Overview

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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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Pharmacovigilance01:19

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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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相关实验视频

Updated: Jan 12, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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使用自然语言处理和大型语言模型监控与阿片类药物相关的社交媒体聊天:时间分析.

Grigori Sidorov1, Muhammad Ahmad1, Pierpaolo Basile2

  • 1Instituto Politécnico Nacional, Centro de Investigación en Computación, Av. Juan de Dios Bátiz S/N, Nueva Industrial Vallejo, Gustavo A. Madero, Mexico City, 07738, Mexico, 52 5534859107.

JMIR infodemiology
|November 4, 2025
PubMed
概括
此摘要是机器生成的。

像Reddit这样的社交媒体平台可以用于实时毒物监测. 使用大型语言模型 (LLM) 的自动化系统准确地跟踪了阿片类药物滥用讨论,与死亡率数据相关联.

关键词:
这就是为什么CDC CDC CDC.疾病控制和预防中心.法学士 (LLM) 是一个专业.在Reddit上,我们可以看到Reddit是什么.人工智能的人工智能是人工智能.数据挖掘是数据挖掘的一个方法.药物分析 药物分析医疗保健 医疗保健 医疗保健大型语言模型.过量服用阿片类药物过量服用社交媒体 社交媒体时间分析时间分析.

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科学领域:

  • 公共卫生监督 公共卫生监督
  • 计算社会科学 计算社会科学
  • 毒理学 毒理学 毒理学

背景情况:

  • 过量服用阿片类药物是一个关键的全球健康问题,传统的监测系统缺乏实时功能.
  • 社交媒体平台,特别是Reddit,提供了丰富的用户生成内容来源,用于及时的毒物监测.
  • 现有的方法很难提供对不断变化的阿片类药物使用模式和相关风险的立即见解.

研究的目的:

  • 评估Reddit作为一个高容量,实时的毒素监测数据源.
  • 开发一个自动化系统来分类和分析与阿片类药物相关的社交媒体帖子.
  • 监测关于阿片类药物使用的公共话语,并检测行为模式和趋势.

主要方法:

  • 收集了Reddit帖子的6年数据集 (2018-2023年) 使用全面的阿片类药物词典.
  • 开发了一个自然语言处理 (NLP) 管道,包括机器学习和微调的大型语言模型 (LLM; OpenAI GPT-3.5 Turbo).
  • 分析了分类帖子中的时间趋势,并将其与疾病控制和预防中心 (CDC) 的死亡数据相关联.

主要成果:

  • 微调的GPT-3.5 Turbo LLM实现了0.93的分类准确度,超过了基线模型的性能.
  • 时间分析显示了与阿片类药物相关的讨论和用户行为随着时间的推移而变化的趋势.
  • 在社交媒体讨论阿片类药物滥用和CDC死亡数据之间发现了显著的正相关性 (r=0.854,P<.001).

结论:

  • 将NLP和LLM与社交媒体数据相结合,可实现有效的实时公共卫生监测.
  • 雷迪特作为一个有价值的平台,用于识别阿片类药物使用和过量风险的新兴趋势.
  • 开发的系统为了解和应对阿片类药物危机提供了一个主动的工具.