Lactoferrin efficacy in treating hyperferritinemia in patients suffering from pathologies unrelated to hereditary hemochromatosis
Bibliographic record
Abstract
Ferritin (Ftn), a globular protein, sequesters 4500 atoms of iron per molecule. Elevated serum Ftn levels (hyperferritinemia) is an indicator of iron homeostasis disorders. We present the results of an observational study involving 17 patients with hyperferritinemia unrelated to hereditary hemochromatosis (HH). All participants received treatment with 200 mg of bovine lactoferrin (bLf) once ( n = 14) or twice ( n = 3) a day before meals. The patients, treated with 200 mg/day of bLf, exhibited a significant increase in red blood cells (+10%, p < 0.001), hemoglobin (+4%, p < 0.001), and hematocrit (+15%, p = 0.004), accompanied by a significant reduction in serum Ftn levels (−52%, p < 0.001), C-reactive protein (CRP) (−85.0%, p < 0.001), and D-dimers (−19%, p < 0.001). Among the three patients treated with 400 mg/day of bLf, two had effects similar to those of patients bLf-treated with 200 mg/day and one experienced a strong reduction of Ftn, CRP, and erythrocyte sedimentation rate (from −97% to −75%). The decrease in serum Ftn levels due to bLf treatment was largely independent of gender ( p = 0.78), age ( p = 0.66), baseline symptoms ( p = 0.20), and concomitant acute ( p = 0.34) and chronic ( p = 0.53) infections. Although this observational pilot study yields positive effects in patients with hyperferritinemia unrelated to HH treated with bLf, a larger sample size is needed for conclusive results.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".