Bibliographic record
Abstract
In chronic hemodialysis patients, a disruption in iron metabolism ranging from absolute to functional deficiency, with compartmentalization of this metal into macrophages, is often observed. Chronic inflammation indeed often causes an upregulation of the iron hormone hepcidin, thereby reducing iron absorption and availability to the erythron. We systematically reviewed the literature on the role of genetic risk factors on iron metabolism in hemodialysis. In this setting, mutations in the HFE gene of hereditary hemochromatosis may confer an adaptive benefit by decreasing hepcidin release, thus improving iron availability to erythropoiesis, anemia control, and the response to erythropoiesis stimulating agents and iron itself, and reducing the side effects of these therapies. The HFE protein together with Transferrin receptor-2 may also have a direct role on erythroid differentiation and iron uptake in erythroid cells. In addition, other genetic determinants of iron status, such as variants in Matriptase-2 (TMPRSS6), have been shown to influence iron metabolism in chronic hemodialysis patients, most likely acting through hepcidin regulation. Although data must be confirmed in larger prospective studies, this favorable shift in iron metabolism balance possibly results in reduced mortality, in particular because of cardiovascular and infective diseases. Further genetic studies may offer a valuable tool to test these hypotheses and guide personalized clinical management and the research of new therapies.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".