Global spinal cord peptidome profiling in response to osteoarthritis in rats
Bibliographic record
Abstract
Abstract Osteoarthritis (OA) is a complex and increasingly prevalent condition, affecting an estimated 600 million people worldwide and significantly reducing quality of life. Understanding OA as both an inflammatory and neurological disease presents challenges for diagnosis and treatment, as no curative therapy is currently available. The neuroactive effects of OA pain on neuropeptide systems, particularly within the spinal cord, remain underexplored, impeding therapeutic advances. Mass spectrometry (MS) was employed to characterize the spinal cord peptidome in a validated rat model of OA pain. The peptidome was analyzed longitudinally over 84 days using the Montreal Induction of Rat Arthritis Testing (MI-RAT©; n = 20) model, compared to arthrotomic (Sham; n = 4) and healthy (Naive; n = 9) groups. Label-free peptidome profiling using liquid chromatography coupled with tandem MS (LC-MS/MS) revealed dynamic changes in endogenous spinal peptides during OA progression, leading to the identification of 624 peptides derived from 29 prohormone precursors. The findings reveal substantial changes in peptide levels in the spinal cord, particularly involving neuropeptide substance P and peptides derived from proenkephalin, calcitonin gene-related peptide, and somatostatin. These results provide novel insights into the molecular mechanisms underlying OA-associated pain and identify potential targets for new therapeutic interventions in neurological pain conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".