Development New Pain Targets in the Genomic Era
Bibliographic record
Abstract
Musculoskeletal pain conditions, such low back pain, are characterized by a report of pain greater than expected based on the results of a standard physical evaluation. The pathophysiology of these conditions is largely unknown, we lack biological markers for accurate diagnosis, and conventional therapeutics have limited effectiveness. Growing evidence suggests that chronic pain conditions are associated with both physical and psychological triggers, which initiate pain amplification and psychological distress; thus, susceptibility is dictated by complex interactions between genetic and environmental factors. Herein, we will review phenotypic and genetic markers of common musculoskeletal pain conditions, selected based on their association with musculoskeletal pain in previous research. The phenotypic markers of greatest interest include measures of pain amplification and “psychological” measures (such as emotional distress, somatic awareness, psychosocial stress, and catastrophizing). Genetic polymorphisms reproducibly linked with musculoskeletal pain are found in genes contributing to serotonergic and adrenergic pathways. Elucidation of the biological mechanisms by which these markers contribute to the perception of pain in these patients will enable the development of novel effective drugs and methodologies that permit better diagnoses and approaches to personalized medicine. Acknowledgments for Grant Support • 1K12DSE022793/PHS HHS/United States • AG033906/AG/NIA NIH HHS/United States • P01 NS045685/NS/NINDS NIH HHS/United States • R01-DE16558/DE/NIDCR NIH HHS/United States • U01-DE017018/DE/NIDCR NIH HHS/United States Disclosure of Interest Substantial shareholders: Attagene, Inc., Algynomics, Inc.; Consultant: Clontech, Inc., Algynomics, Inc.; Officer position: Algynomics, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".