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Related Concept Videos

Analgesia and Pain Management01:25

Analgesia and Pain Management

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...
Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

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...
Opioid Analgesics: Morphine and Other Natural Cogeners01:20

Opioid Analgesics: Morphine and Other Natural Cogeners

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...
Opioid Receptors: Overview01:22

Opioid Receptors: Overview

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, D-Pen5]-enkephalin or DPDPE for...

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Related Experiment Video

Updated: May 14, 2026

Assessment of Morphine-induced Hyperalgesia and Analgesic Tolerance in Mice Using Thermal and Mechanical Nociceptive Modalities
07:23

Assessment of Morphine-induced Hyperalgesia and Analgesic Tolerance in Mice Using Thermal and Mechanical Nociceptive Modalities

Published on: July 29, 2014

Machine Learning Applications for Opioid Use Management in Chronic Cancer Pain: A Systematic Scoping Review.

Anastasia Zompola1, Thalis Asimakopoulos2, Christina Iosifidou3

  • 1Biostatistics, National and Kapodistrian University of Athens School of Medicine, Athens, GRC.

Cureus
|May 13, 2026
PubMed
Summary

Machine learning models show promise in predicting opioid adherence and misuse in cancer patients. However, limited data restricts current clinical application, necessitating larger, more diverse studies for safer opioid management.

Keywords:
ai and machine learningchronic cancer painopioid misuseopioid usesystematic scoping review

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Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery
09:38

Determining Pain Detection and Tolerance Thresholds Using an Integrated, Multi-Modal Pain Task Battery

Published on: April 14, 2016

Area of Science:

  • Oncology
  • Pain Management
  • Health Informatics
  • Machine Learning

Background:

  • Chronic pain significantly impacts cancer patients' quality of life.
  • Opioid overuse for cancer pain management contributes to a global crisis.
  • Machine learning (ML) offers potential for improved opioid consumption monitoring.

Purpose of the Study:

  • To review the literature on ML techniques for managing opioid consumption in chronic cancer pain.
  • To evaluate the effectiveness of ML in monitoring opioid use in oncology patients.
  • To identify gaps and future directions for ML in cancer pain management.

Main Methods:

  • Systematic literature search (PubMed, Google Scholar) following PRISMA guidelines (2010-2024).
  • Included studies using healthcare data from cancer patients, applying ML techniques.
  • Extracted data on study goals, datasets, ML models, and evaluation metrics.

Main Results:

  • Four studies met the criteria, demonstrating high performance (AUC >0.8) in predicting opioid adherence, misuse, and long-term use.
  • ML models showed promising accuracy in predicting opioid-related outcomes.
  • Limited sample sizes and lack of external validation restrict generalizability.

Conclusions:

  • ML models show potential for accurate prediction of opioid adherence and misuse in cancer patients.
  • Current findings are limited by small sample sizes and lack of external validation.
  • Future research should focus on larger, diverse cohorts and integrated data for personalized opioid management.