Resting and activated bovine neutrophils and eosinophils differ in their responses to adrenergic agonists
Bibliographic record
Abstract
Polymorphonuclear cells (PMN) provide a rapid response to infection and tissue damage and stress can modify these critical innate immune defences. The study of adrenergic receptor (AR) expression and function in bovine PMNs is limited but both neutrophils and eosinophils express numerous AR genes but differ significantly in their expression of individual AR genes. A flow cytometric technique was developed to differentiate between bovine neutrophils and eosinophils so both neutrophil and eosinophil responses to adrenergic agonists could be analysed. Neutrophils and eosinophils displayed significantly different changes in CD11b, L-selectin, and CD44 expression when activated by bovine serum opsonized zymosan and recombinant bovine interferon gamma. The responses of activated and resting neutrophils and eosinophils were then compared following stimulation with endogenous adrenergic agonists, epinephrine (E) norepinephrine (NE), and synthetic agonists targeting α1-, α2-, or β-ARs. Both resting and activated neutrophils and eosinophils displayed differences in iROS, CD44, and L-selectin expression following stimulation with E and NE. Resting neutrophils displayed pro-inflammatory responses to both E and NE, while resting eosinophils displayed a pro-inflammatory response to only NE. No single synthetic adrenergic agonist fully recapitulated responses observed with either E or NE and responses to adrenergic agonists were dose-dependent. In conclusion, bovine eosinophils and neutrophils responded to multiple adrenergic agonists by altering expression of proteins involved in immune surveillance and pro-inflammatory responses. Significant differences in neutrophil and eosinophil responses to adrenergic agonists are consistent with their differences in AR gene expression. This highlights the importance of analysing separately these two PMN subpopulations when investigating the effects of either endogenous or synthetic AR agonists.
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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".