Bibliographic record
Abstract
PURPOSE OF REVIEW: Heme biosynthesis requires a series of enzymatic reactions that take place in the cytosol and the mitochondria as well as the proper intercellular and intracellular trafficking of iron. Heme can also be acquired by intestinal absorption and intercellular transport. The purpose of this review is to highlight recent work on heme and iron transport with an emphasis on their relevance in erythropoiesis. RECENT FINDINGS: Whereas the enzymes responsible for heme biosynthesis have been identified, transport mechanisms for iron, heme, or heme synthesis intermediates are only emerging. Recent studies have shed light on how these molecules are transported among various cellular compartments, as well as tissues. Much of this progress can be attributed to the use of model organisms such as S. cerevisiae, C. elegans, D. rerio, and M. musculus. Genetic studies in these models have led to the identification of several new genes involved in heme metabolism. Although our understanding has greatly improved, it is highly likely that other regulators exist and additional work is required to characterize the pathways by which heme and iron are transported within the erythron. SUMMARY: The identification of heme and iron transport mechanisms will improve our understanding of blood development and provide new insight into human blood disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.005 | 0.005 |
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".