Physiological focus on the erythropoietin–hepcidin–ferroportin axis
Bibliographic record
Abstract
To analyze the interconnection between erythropoiesis and iron metabolism, one of the issues raised in this study was to know iron bioavailability under physiopathological conditions. Our aim was to understand the functional axis response composed of erythropoietin (Epo)-hepcidin-ferroportin (FPN), when 2 dysfunctional states coexist, using an animal model of iron overload followed by hypoxia. FPN and prohepcidin were assessed by immunohistochemistry using rabbit anti-mouse FPN polyclonal and prohepcidin monoclonal antibodies. Goat-labeled polymer - horseradish peroxidase anti-rabbit EnVision + System (DAB) was used as the secondary antibody. Epo levels were measured by ELISA. Tissue iron was studied by Prussian blue iron staining. Erythropoietic response was assessed using conventional hematological tests. Iron overload increased prohepcidin that remained high in hypoxia, coexisting with high levels of Epo in hypoxia, with or without iron overload. In hypoxia, FPN was clearly evident in reticuloendothelial macrophages, more than in hypoxia with iron overload. Interestingly, duodenal FPN was clearly identified on the basolateral membrane in hypoxia, with or without iron overload. Our data indicate that 2 signals could induce the cell-specific response as follows: (i) iron signal, induced prohepcidin, which reduced reticuloendothelial FPN and reduced iron availability; and (ii) hypoxia signal, stimulated Epo, which affected iron absorption by stabilizing duodenal FPN and allowed iron supply to erythropoiesis independently of store size.
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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".