Protective effects of lactoferrin chimera and bovine lactoferrin in a mouse model of enterohaemorrhagic <i>Escherichia coli</i> O157:H7 infection<sup>1</sup>This article is part of a Special Issue entitled Lactoferrin and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
Mice orally infected with enterohaemorrhagic Escherichia coli (EHEC) O157:H7 were used to evaluate the activity of bovine lactoferrin (bLF) and the synthetic peptide LFchimera. Groups of BALB/c mice inoculated intragastrically with EHEC O157:H7 showed chronic intestinal infection with the pathogen that persisted over 6 days and resulted in a high mortality rate (90%). LFchimera and kanamycin significantly decreased (40%) this mortality rate (P = 0.028). On the other hand, although mice administered with bLF showed an important reduction in mortality (50%), this was not statistically significant (P = 0.070). In infected and untreated mice, severe tubular necrosis, glomerular lesions, and moderate intratubular hyaline casts were found in the kidney. However, in the bLF and LFchimera groups we found a reduction in the damage and a substantial decrease in the bacterial concentration excreted in feces 48 h after infection. Furthermore, sepsis caused by EHEC was reduced by the treatments, evidenced by the fact that bacteria were not detected in the kidney or liver 72 h after infection. The results suggest the bLF and LFchimera could have potential as therapeutics in EHEC infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".