Genetic Variation in Bovine Mononuclear Leukocyte Responses to Dexamethasone
Bibliographic record
Abstract
The objective of this study was to determine whether bovine mononuclear leukocytes exhibit genetic variability prior to and after a glucocorticoid hormone challenge in vivo. Test animals included 60 pedigreed Holstein bulls treated on 3 consecutive days with dexamethasone and 5 untreated control bulls. Eight indicator traits of leukocyte responsiveness to dexamethasone included the percentages of circulating B cells, T cells (CD4, CD8, and workshop cluster 1 molecule expressed by bovine gammadelta T cell), major histocompatibility complex (MHC) I and II expressing cells, and mean expressions of surface MHC I and MHC II on circulating cells. Blood for this work was collected from each test bull 10 times before, during, and after dexamethasone administration, with corresponding samples taken for control bulls. Random regression models with treatment-specific serial correlation were applied to the leukocyte data sets to estimate genetic and nongenetic sources of variation in baseline and recovery aspects of the traits. All traits responded predictably to glucocorticoid challenge. Genetic variation was observed in baseline measurements of all traits, with heritability estimates ranging from 0.21 +/- 0.03 to 0.60 +/- 0.06. Genetic variation in linear recovery from nadir values following dexamethasone administration was significant only for percentage CD4, percentage CD8, and for surface expression of MHC II. The genetic covariance between basal and linear recovery was positive and significant for percentage CD4, percentage CD8, and MHC II expression. The bovine lymphocyte antigen DRB3.2 locus accounted for significant proportions of total variation in percentage MHC II cells and MHC I expression. These results suggest that genetic variability exists for important basal and glucocorticoid-modified phenotypes of bovine mononuclear leukocytes, implying that immunocompetence traits impacted by this stress hormone may be enhanced by genetic selection.
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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 | 0.001 | 0.001 |
| 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".