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Related Experiment Video

Updated: May 21, 2026

Network Pharmacology and Validation of the Antidepressant Mechanisms of Qiangzhifang in a Chronic Restraint Stress-induced Depression Rat Model
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Probabilistic pathways from chronic pain to suicidal ideation in Qatar: a Bayesian-Network study.

Aisha Al-Khinji1, Dhafer Malouche2

  • 1College of Medicine, Qatar University, Doha, Qatar. aalkhinji@qu.edu.qa.

Journal of Translational Medicine
|May 20, 2026
PubMed
Summary
This summary is machine-generated.

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Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study.

Frontiers in pain research (Lausanne, Switzerland)·2025

This study mapped probabilistic links between chronic pain, mental health, and suicidal thoughts in Qatar. Self-regret and psychomotor changes were strongly associated with suicidal ideation, highlighting key targets for integrated screening.

Area of Science:

  • Epidemiology
  • Mental Health Research
  • Computational Psychiatry

Background:

  • Chronic pain is linked to increased risk of depressive, anxiety, and suicidal symptoms.
  • Understanding these associations in Middle Eastern populations is limited.
  • This study addresses the gap by examining probabilistic links in Qatari adults.

Purpose of the Study:

  • To map probabilistic interdependencies between chronic pain, mental health symptoms, and demographics in Qatari adults.
  • To identify factors associated with suicidal thoughts.
  • To inform integrated screening and intervention strategies.

Main Methods:

  • Utilized questionnaire data from 2363 Qatari adults (Qatar Biobank).
  • Employed data-driven Bayesian Network (BN) modeling with hill-climbing and BIC scoring.
Keywords:
Bayesian NetworksChronic painCross-sectional studyDepression and anxietyMental-health comorbidityProbabilistic graphical modellingQatar BiobankSuicidal ideation

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Last Updated: May 21, 2026

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  • Constrained BN with domain knowledge and validated arc stability via bootstrap resampling.
  • Main Results:

    • Identified 38 stable directed edges in the BN, revealing complex associations.
    • Self-regret (OR=9.45) and psychomotor change (OR=6.68) were strongest direct associates of suicidal thoughts.
    • A symptom cluster (sleep problems → anhedonia → depression → self-regret) showed strong links to elevated suicide risk; fatigue linked pain and self-regret.

    Conclusions:

    • Bayesian Network modeling elucidated complex, non-linear associations between pain, mental health, and suicidality in Qatar.
    • Cross-sectional design limits causal inference; longitudinal studies are needed.
    • Identified high-risk symptom clusters (self-regret, psychomotor disturbance) as targets for integrated pain-mental health screening, with potential for demographic tailoring.