Effect of lactoferrin on Enteroaggregative <i>E. coli</i> (EAEC)This paper is one of a selection of papers published in this Special Issue, entitled 7th International Conference on Lactoferrin: Structure, Function, and Applications, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
We previously demonstrated that lactoferrin inhibits adherence of enteropathogenic Escherichia coli to HEp-2 cells and decreases invasiveness of Shigella flexneri in HeLa cells by disruption of the type III secretory system (TTSS) of both enteropathogens. To determine whether these effects were specific to the TTSS, we assessed the activity of bovine lactoferrin on enteroaggregative E. coli (EAEC), enteropathogens whose virulence is not TTSS dependent. Bovine lactoferrin at a concentration of 1.0 and 0.1 mg/mL inhibited EAEC growth. Saturation with iron reversed the bacteriostatic effect. Lactoferrin under nonbacteriostatic conditions decreased EAEC adherence to HEp-2 cells as evaluated by microscopy and CFUs; this effect was not iron dependent. Lactoferrin inhibited EAEC biofilm formation and increased autoagglutination. Lactoferrin blocks EAEC adherence by inducing release and degradation of aggregative adherence fimbria, a key element of EAEC pathogenesis. We hypothesized that lactoferrin binding to lipid A of lipopolysaccharide disrupts the virulence proteins anchored to the bacterial outermembrane. These data suggest that the effect of lactoferrin on surface proteins is not restricted to organisms having a TTSS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".