Antibacterial, anti-invasive, and anti-inflammatory activity of bovine lactoferrin extracted from milk or colostrum versus whole colostrum
Bibliographic record
Abstract
Lactoferrin (Lf), a multifunctional cationic glycoprotein extracted from milk or colostrum, is able to chelate two ferric ions per molecule, inhibit the formation of reactive oxygen species, interact with the anionic components of bacteria or host cells, and enter inside host cell nucleus, thereby exerting antibacterial, anti-invasive, and anti-inflammatory activities. By virtue of Lf presence, bovine colostrum is expected to perform analogous functions to pure Lf, along with additional activities attributable to other bioactive constituents. The present research aims to compare the antibacterial, anti-invasive, and anti-inflammatory activities of bovine Lf purified from milk (mbLf) and colostrum (cbLf) in comparison to those exhibited by whole bovine colostrum (wbc). The results demonstrated a major efficacy of mbLf in inhibiting pathogenic bacteria and in exerting anti-invasive and anti-survival activities with respect to cbLf and wbc. Furthermore, mbLf lowered IL-6 levels to those of uninfected cells, while a less evident decrease was observed upon cbLf treatment. Conversely, wbc managed to slightly lower IL-6 levels compared to those synthesized by infected cells. These data demonstrate that, to obtain maximum effectiveness in such activities, Lf should be formulated/used without addition of other substances and should be sourced from bovine milk rather than colostrum.
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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".