Deferoxamine Reduces Tissue Damage During Endotoxin-Induced Mastitis in Dairy Cows
Bibliographic record
Abstract
The protective effects of 3 antioxidants on polymorphonuclear neutrophil-induced damage to mammary cells were evaluated in vivo using an endotoxin-induced mastitis model. Fifteen healthy, midlactation cows with no history of clinical Escherichia coli mastitis were randomly assigned to 1 of the 3 treatment groups corresponding to each modulator to be evaluated, that is, deferoxamine, catechin, and glutathione ethyl ester. Each cow had 1 quarter infused with saline and 1 quarter infused with the selected modulator; a third quarter was infused with lipopolysaccharides (LPS), whereas the fourth quarter received a combination of LPS and the modulator. Infusion of LPS caused acute mastitis as determined by visual observations and by large increases in milk somatic cell count, BSA, and proteolytic activity. These parameters were not affected by antioxidant administration. The extent of cell damage was evaluated by measuring milk levels of lactate dehydrogenase and N-acetyl-beta-D-glucosaminidase activity. Levels of these parameters were several times higher after LPS administration. Intramammary infusions of catechin or glutathione ethyl ester did not exert any protective effect, whereas infusion of deferoxamine, a chelator of iron, decreased milk lactate dehydrogenase and NA-Gase activity, suggesting a protective effect against neutrophil-induced damage. The protective effect of deferoxamine was also evidenced by a lower milk level of haptoglobin. The proteolytic activity of mastitic milk was not influenced by the presence of deferoxamine. Overall, our results suggest that local infusion of deferoxamine may be an effective tool to protect mammary tissue against neutrophil-induced oxidative stress during bovine mastitis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".