Recombinant Soluble CD14 Reduces Severity of Intramammary Infection by<i>Escherichia coli</i>
Bibliographic record
Abstract
The interaction among gram-negative bacteria, the innate immune system, and soluble CD14 (sCD14) has not been well documented. The effect of recombinant bovine sCD14 (rbosCD14) on milk somatic cell count (SCC), bacterial clearance, and cytokine production was investigated by using a bovine intramammary Escherichia coli infection model. We first determined whether rbosCD14 would increase the SCC during a lipopolysaccharide (LPS) challenge. Three quarters of each of six healthy lactating cows were injected with either 0.3 microg of LPS, 0.3 microg of LPS plus 100 micro g of rbosCD14, or saline. In comparison with quarters injected with LPS alone, the SCC was twofold higher (P < 0.05) in quarters injected with LPS plus rbosCD14 after the challenge. We therefore hypothesized that when E. coli bacteria invade the mammary gland, sCD14 in milk would interact with LPS and rapidly recruit neutrophils from the blood to eliminate the bacteria before establishment of infection. To test this hypothesis, two quarters of each of nine healthy cows were challenged with either 50 CFU of E. coli plus saline or 50 CFU of E. coli plus 100 microg of rbosCD14. Quarters challenged with E. coli plus rbosCD14 had a more rapid recruitment of neutrophils, which was accompanied by a faster clearance of bacteria, lower concentrations of tumor necrosis factor alpha and interleukin-8 in milk, and milder clinical symptoms, than challenged quarters injected with saline. Results indicate that increasing the concentration of sCD14 in milk may be a potential strategy with which to prevent or reduce the severity of infection by coliform bacteria.
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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".