Effect of Corticosteroids and Neuropeptides on the Expression of Defensins in Bovine Tracheal Epithelial Cells
Bibliographic record
Abstract
Susceptibility to bacterial pneumonia in cattle is enhanced by stressors such as transportation, weaning, and commingling, which trigger a physiologic stress response resulting in elevated levels of endogenous corticosteroids and catecholamines. To determine the effect of neuroendocrine mediators on the expression of innate defense peptides in the lung, bovine tracheal epithelial cells were exposed to dexamethasone, catecholamines, acetylcholine, or substance P, and then beta-defensin expression was quantified using real-time reverse transcription-PCR. Basal expression of tracheal antimicrobial peptide (TAP) mRNA was not affected by any of the mediators tested. However, induction of TAP expression by lipopolysaccharide was significantly inhibited by pretreatment with dexamethasone. Bronchial biopsy specimens from dexamethasone-treated calves had significantly lower expression of TAP and lingual antimicrobial peptide (LAP) mRNA than saline-treated controls following 48 h of treatment. Lipopolysaccharide-elicited neutrophil recruitment was enhanced in the lungs of dexamethasone-treated calves compared to saline-treated controls. These findings indicate that modulation of epithelial antimicrobial peptide expression is one mechanism through which corticosteroids and stress may impair innate pulmonary defenses.
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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".