Nitrogen monoxide inhibits haem synthesis in mouse reticulocytes
Bibliographic record
Abstract
AI (anaemia of inflammation) often manifests in patients with chronic immune activation due to cancer, chronic infections, autoimmune disorders, rheumatoid arthritis and other diseases. The pathogenesis of AI is complex and involves cytokine-mediated inhibition of erythropoiesis, insufficient erythropoietin production and diminished sensitivity of erythroid progenitors to this hormone, and retention of iron in haemoglobin-processing macrophages. NO (nitric oxide) is a gaseous molecule produced by activated macrophages that has been identified as having numerous effects on iron metabolism. In the present study, we explore the possibility that NO affects iron metabolism in reticulocytes and our results suggest that NO may also contribute to AI. We treated reticulocytes with the NO donor SNP (sodium nitroprusside). The results indicate that NO inhibits haem synthesis dramatically and rapidly at the level of erythroid-specific 5-aminolaevulinic acid synthase 2, which catalyses the first step of haem synthesis in erythroid cells. We also show that NO leads to the inhibition of iron uptake via the Tf (transferrin)-Tf receptor pathway. In addition, NO also causes an increase in eIF2α (eukaryotic initiation factor 2α) phosphorylation levels and decreases globin translation. The profound impairment of haem synthesis, iron uptake and globin translation in reticulocytes by NO raises the possibility that this gas may also contribute to AI.
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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) | 0.000 | 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 | 0.001 |
| 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".