Lactoferrin and hematoma detoxification after intracerebral hemorrhage
Bibliographic record
Abstract
In this minireview we discuss the role of lactoferrin (LTF) in detoxifying hematoma after intracerebral hemorrhage (ICH). Subsequent to ICH, neutrophils enter the ICH-affected brain, where they release various granule contents, including LTF. LTF is an iron-binding glycoprotein that binds Fe3+ with high affinity. Unlike other iron-binding proteins, LTF can retain Fe3+ at the low pH associated with inflamed tissue. LTF’s ability to sequester Fe3+ is of particular importance to ICH pathogenesis, because large quantities of free iron, which is pro-oxidative and pro-inflammatory, are generated in the ICH-affected brain owing to blood hemolysis. LTF delivered to ICH-affected brain, either as a therapeutic agent or through infiltrated polymorphonuclear neutrophils (cells containing high levels of LTF), could limit the pathogenesis of ICH. LTF is a protein with a high isoelectric point (8.7), a property that enables it to bind to negatively-charged apoptotic cells or proteins. Here, LTF could act as a bridging molecule that couples the apoptotic cells to LTF receptors on the cellular membranes of microglia/macrophages to facilitate the efferocytosis/erythrophagocytosis of apoptotic cells and damaged red blood cells. Thus, by virtue of sequestrating iron and facilitating efferocytosis, LTF may contribute to hematoma detoxification and hematoma/inflammation resolution after ICH.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".