Nerve growth factor in the equine joint
Bibliographic record
Abstract
Nerve growth factor (NGF) is a neurotrophin with many functions. In humans, it is involved in inflammation, nerve growth, apoptosis and pain signalling. Increased concentrations of NGF in synovial fluid has been shown in humans and dogs with osteoarthritis. Despite osteoarthritis being a common problem in horses, no studies have previously been published on NGF in the equine joint. The aim of this study was to quantify NGF in equine synovial fluid from healthy joints, acutely inflamed septic joints and joints with structural changes associated with osteoarthritis. A secondary aim was to identify the localisation of NGF and its two receptors, TrkA and p75NTR, in healthy and osteoarthritic articular cartilage. NGF concentrations in synovial fluid from osteoarthritic joints (n = 27), septic joints (n = 9) and healthy joints (n = 16) were determined by ELISA. In addition, articular cartilage from osteoarthritic and healthy joints was examined for NGF, TrkA and p75NTR using immunohistochemistry staining. NGF was present in equine synovial fluid and articular cartilage. Compared to synovial fluid from healthy joints, NGF concentration was higher in synovial fluid from joints with structural osteoarthritic changes (P = 0.032) or acute septic inflammation (P = 0.006). In articular cartilage with severe osteoarthritic changes, there was more abundant positive immunohistochemistry staining for NGF and its receptors than in normal articular cartilage. Further studies should focus on identifying precursor forms of NGF, and on receptor expression and downstream signalling of TrkA and P75NTR in health and disease.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".