Interactions of lactoferrin with cells involved in immune functionThis 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
The antimicrobial activities of lactoferrin (Lf) depend on its capacity to bind iron and on its direct interaction with the surface of microorganisms. Its protective effect also extends to the regulation of the host response to infections. Depending on the immune status of an individual, Lf can have anti-inflammatory properties that downregulate the immune response and prevent septic shock and damage to tissues. It also acts as a promoter of the activation, differentiation, and (or) proliferation of immune cells. Although most of the anti-inflammatory activities are correlated with the neutralization of proinflammatory molecules by Lf, the promoting activity seems to be related to a direct effect of Lf on immune cells. Although the mechanisms that govern these activities are not clearly defined, and probably differ from cell to cell, several cellular targets and possible mechanisms of action are highlighted. The majority of the molecular targets at the surface of cells are multiligand receptors but, interestingly, most of them have been reported as signaling, endocytosis, and nuclear-targeting molecules. This review focuses on the known and putative mechanisms that allow the immunoregulating effect of Lf in its interactions with immune cells.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".