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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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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: Jun 10, 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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Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.

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Area of Science:

  • Digital health
  • Pharmacovigilance
  • Machine learning in medicine

Background:

  • Opioid exposure causes physiological adaptations that can indicate risk for opioid-use disorder (OUD).
  • Digital pharmacovigilance using noninvasive sensors offers a novel approach to monitor opioid use and promote safer prescribing.
  • Identifying early signs of dependence is crucial for managing OUD risk.

Purpose of the Study:

  • To identify wearable sensor-derived features associated with opioid dependence.
  • To compare physiological adaptations in opioid-naïve individuals versus chronic opioid users.
  • To develop a machine learning model for distinguishing between these groups.

Main Methods:

  • A longitudinal observational study collected continuous physiological data from participants with acute pain receiving opioid analgesia.
  • Data were gathered during hospitalization and for 7 days post-discharge, combined with electronic health record and self-reported opioid administration.
  • Machine learning models were trained and validated using 30 sensor-derived features and 9 demographic/clinical features.

Main Results:

  • Forty-one participants and 169 opioid administrations were analyzed.
  • Four interpretable decision tree-based machine learning models were developed using 14 sensor and 5 clinical features.
  • Participant-level model performance showed accuracy ranging from 70% to 90%, sensitivity from 67% to 100%, and specificity from 67% to 100%.

Conclusions:

  • Wearable sensor data can generate digital biomarkers to predict opioid use status (naïve vs. chronic).
  • The identified features may indicate opioid dependence.
  • Further research should explore opioid dependence, withdrawal, and transition states in repetitive opioid exposure.