Bibliographic record
Abstract
Abstract Iron is essential for oxidation–reduction catalysis and bioenergetics, but unless appropriately shielded, iron plays a key role in the formation of toxic oxygen radicals that can attack all biological molecules. Hence, specialized molecules for the acquisition, transport, and storage (ferritin) of iron in a soluble nontoxic form have evolved. The delivery of iron to most cells occurs after the binding of transferrin to transferrin receptors on the cell membrane. The transferrin receptor complexes are then internalized by endocytosis, and iron is released from transferrin by a process involving endosomal acidification. Iron is then transported through the endosomal membrane by the Fe2+ transporter Nramp2/DMT1. Importantly, the identical transporter is involved in the absorption of inorganic iron in the duodenum, a process that is facilitated by the ferric reductase, Dcytb, which provides Fe2+ for Nramp2/DMT1. Organisms and cells have limited ability to excrete excess iron and only some specialized cells evolved active mechanisms to export iron. Iron release from these “donor cells” (primarily enterocytes and macrophages that recycle hemoglobin iron) is mediated by ferroportin 1. The ferroxidase activity of copper‐containing proteins, hephaestin and ceruloplasmin, facilitates the movement of iron across the membranes of enterocytes and macrophages, respectively. Cells are also equipped with a regulatory system that controls iron levels in the labile pool. Levels of iron modulate the capacity of iron regulatory proteins to bind to the iron responsive elements present in the untranslated regions of mRNAs for several proteins involved in iron metabolism (e.g., ferritin, transferrin receptor, Nramp2); these associations, or lack of them, in turn control the expression of these proteins. Despite these homeostatic mechanisms, organisms often face the threat of either iron deficiency or iron overload. J. Trace Elem. Exp. Med. 16:201–217, 2003. © 2003 Wiley‐Liss, Inc.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".