Temporal kinetics of serum amyloid A (SAA) concentration and identification of SAA isoforms in blood and synovial fluid of horses with experimentally induced septic arthritis, non-septic synovitis, and systemic inflammation
Bibliographic record
Abstract
Prompt diagnosis of equine septic arthritis is crucial for successful treatment. Serum amyloid A (SAA) has been suggested as a reliable biomarker. However, we previously found that synovial fluid SAA increases in nonaffected joints of horses with septic arthritis. We hypothesized that systemic SAA may leak into the nonaffected joints. If this is the case, we also hypothesized that locally produced joint SAA isoforms may be better candidates for septic arthritis biomarkers. Thus, our objectives were 1) to evaluate the temporal kinetics of systemic and synovial fluid SAA in horses with septic arthritis ( n = 5), non-septic synovitis ( n = 5), and systemic inflammation ( n = 5), examining both affected and contralateral joints; and 2) investigate putative locally produced joint SAA isoforms and detect amino-acid differences between them. We confirmed that SAA increases significantly in synovial fluid in nonaffected joints of horses with systemic inflammation (≤352 mg/L), as well as in contralateral nonaffected joins in horses with septic arthritis (≤1,830 mg/L) compared to baseline at time 0 (<0.2 mg/L). We also identified a putative locally produced joint SAA peptide in synovial fluid (FGDSGHGAADSR) that differed in 1 amino acid from 2 systemic peptides found both in plasma and synovial fluid. The putative joint SAA isoform was present in joints of horses with both septic arthritis and systemic inflammation (ion intensities 10 4 –10 6 ). Thus, the increase of synovial fluid SAA may be both due to the leakage of SAA from serum into joints and local production of joint SAA isoforms.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".