Stimulating innate immunity in feedlot cattle: strategies to induce tracheal antimicrobial peptide gene expression
Bibliographic record
Abstract
Bovine respiratory disease (BRD) is an economically devastating complex of bacterial and viral infections that greatly affects the feedlot industry across North America. Studies have shown that viral infections such as BVDV and glucocorticoid production in stressed calves may inhibit the induction of tracheal antimicrobial peptide (TAP), a cationic β‐defensin that has direct microbicidal effects on invading microbes in the respiratory tract. Certain surface receptors of the innate immune system called the Toll‐Like‐Receptors (TLRs) recognize pathogen associated molecular patterns (PAMPs) to initiate intracellular signal transduction pathways that potentially induce TAP gene expression. PCR analyses of unstimulated tracheal epithelial cells show mRNA expression for TLRs 1,2,3,4, and 6. Therefore, stimulation of tracheal epithelial cells with agonists for these TLRs may induce TAP expression. Previous studies have shown that a TLR 4 agonist called Lipopolysaccharide (LPS) significantly induces TAP expression in epithelial cells after 16 hours of stimulation. This suggests that LPS induces TAP expression via the TLR 4 pathway. The present study examines TLR 2 agonists to determine their effect on TAP induction. Quantitative Real‐Time PCR analysis of epithelial cells stimulated by the TLR 2 agonist Lipoteichoic Acid has shown significant induction of TAP. However, the amount of induction is much less than LPS and occurs after the same amount of stimulation time. Future experiments will determine if other TLR 2 agonists can induce gene expression of TAP in a robust and rapid manner so that TAP production can be increased to ultimately reduce the incidence of BRD in feedlot calves.
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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 | 0.001 | 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".