Use of a biopolymer delivery system to investigate the influence of interleukin-4 on recruitment of neutrophils in equids
Bibliographic record
Abstract
OBJECTIVE: To use a biopolymer delivery system to investigate the ability of interleukin (IL)-4 to recruit neutrophils into subcutaneous tissues of equids. ANIMALS: 16 horses and 2 ponies. PROCEDURES: Animals were assigned to 3 experiments (6/experiment). Effects of recombinant equine (Req) IL-4 (100, 250, or 500 ng/site) versus a positive control (ReqIL-8; 100 ng, 250 ng, or 1 μg/site) and a negative control (Dulbecco PBSS or culture medium) on neutrophil chemotaxis were assessed after SC injection into the neck with an injectable biopolymer used as the vehicle. Tissue samples including the biopolymer plug were collected by biopsy at various time points from 3 hours to 7 days after injection. Neutrophil infiltration was evaluated by histologic scoring (experiments 1, 2, and 3) or flow cytometry (experiment 3). RESULTS: Histologic neutrophil infiltration scores did not differ significantly among treatments at most evaluated time points. On flow cytometric analysis, log-transformed neutrophil counts in biopsy specimens were significantly greater for the ReqIL-8 treatment (1 μg/site) than the negative control treatment at 3 but not 6 hours after injection; results did not differ between ReqIL-4 and control treatments at either time point. Negative control treatments induced an inflammatory response in most equids in all experiments. CONCLUSIONS AND CLINICAL RELEVANCE: Flow cytometry was a more reliable method to estimate neutrophil migration than histologic score analysis. The ReqIL-4 treatment did not induce a detectable neutrophil response, compared with the negative control treatment in this study. Evidence of inflammation in negative control samples suggested the biopolymer is not a suitable vehicle for use in equids.
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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 | 0.000 | 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 | 0.000 |
| 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".