Changes in Aβ Non-Nociceptive Primary Sensory Neurons in a Rat Model of Osteoarthritis Pain
Bibliographic record
Abstract
BACKGROUND: Pain is a major debilitating factor in osteoarthritis (OA), yet few mechanism-based therapies are available. To address the need to understand underlying mechanisms the aim of the present study was to determine changes in sensory neurons in an animal model of OA pain. RESULTS: The model displayed typical osteoarthritis pathology characterized by cartilage degeneration in the knee joint and also manifested knee pathophysiology (edema and increased vasculature permeability of the joint) and altered nociception of the affected limb (hind paw tenderness and knee articulation-evoked reduction in the tail flick latency). Neurons included in this report innervated regions throughout the entire hind limb. Abeta-fiber low threshold mechanoreceptors exhibited a slowing of the dynamics of action potential (AP) genesis, including wider AP duration and slower maximum rising rate, and muscle spindle neurons were the most affected subgroup. Only minor AP configuration changes were observed in either C- or Adelta-fiber nociceptors. CONCLUSION: Thus, at one month after induction of the OA model Abeta-fiber low threshold mechanoreceptors but not C- or Adelta-fiber nociceptors had undergone changes in electrophysiological properties. If these changes reflect a change in functional role of these neurons in primary afferent sensory processing, then Abeta-fiber non-nociceptive primary sensory neurons may be involved in the pathogenesis of OA pain. Further, it is important to point out that the patterns of the changes we observed are consistent with observations in models of peripheral neuropathy but not models of peripheral inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".