Repeated Oronasal Exposure to Lipopolysaccharide Induced Mucosal IgA Responses in Periparturient Dairy Cows
Bibliographic record
Abstract
This study investigated the effects of repeated oronasal treatment with lipopolysaccharide (LPS) on the humoral immune responses in saliva, vaginal mucus, and the plasma markers of the acute phase response in periparturient dairy cows. One hundred pregnant Holstein cows were administered either 3 increasing doses of LPS (n = 50) as follows: 1) 0.01 µg/kg body weight (BW) on d -28, 2) 0.05 µg/kg BW on d -25, and -21, and 3) 0.1 µg/kg BW on d -18, and -14, or sterile saline solution (controls; n = 50) oronasally for 3 consecutive wk starting at 28 d before parturition. Intensive sampling was conducted on thirty cows (n = 15/group). Multiple saliva, vaginal mucus and blood samples were collected around parturition and analyzed for total immunoglobulin-(Ig)A, plasma serum amyloid A (SAA), lipopolysaccharide-binding protein (LBP), anti-LPS IgA, IgG, IgM, tumour necrosis factor(TNF)-α, and interleukin(IL)-1. Results regarding total secretory IgA (sIgA) antibodies showed greater concentrations in the saliva and an overall tendency for higher total sIgA in the vaginal mucus of the LPS-treated cows. Treatment had no effect on plasma sIgA, IgG, IgM anti-LPS antibodies, haptoglobin, SAA, LBP, TNF-α, and IL-1. Treatments by time interactions were observed for SAA and IL-1 with lowered concentrations of both variables in the plasma of LPS-treated cows after parturition. Overall, repeated oronasal LPS treatment clearly enhanced total sIgA antibodies in the saliva, stimulated their production in vaginal mucus shortly before calving, and lowered plasma IL-1 around parturition, but showed limited effects on markers of the acute phase response in the plasma in dairy cows around parturition.
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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) | 0.001 | 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 | 0.001 |
| 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".