Is methemoglobin an inert bystander, biomarker or a mediator of oxidative stress—The example of anemia?
Bibliographic record
Abstract
Acute anemia increases the risk for perioperative morbidity and mortality in critically ill patients who experience blood loss and fluid resuscitation (hemodilution). Animal models of acute anemia suggest that neuronal nitric oxide synthase (nNOS)-derived nitric oxide (NO) is adaptive and protects against anemia-induced mortality. During acute anemia, we have observed a small but consistent increase in methemoglobin (MetHb) levels that is inversely proportional to the acute reduction in Hb observed during hemodilution in animals and humans. We hypothesize that this increase in MetHb may be a biomarker of anemia-induced tissue hypoxia. The increase in MetHb may occur by at least two mechanisms: (1) direct hemoglobin oxidation by increased nNOS-derived NO within the perivascular tissue and (2) by increased deoxyhemoglobin (DeoxyHb) nitrite reductase activity within the vascular compartment. Both mechanisms reflect a potential increase in NO signaling from the tissue and vascular compartments during anemia. These responses are thought to be adaptive; as deletion of nNOS results in increased mortality in a model of acute anemia. Finally, it is possible that prolonged activation of these mechanisms may lead to maladaptive changes in redox signaling. We hypothesize, increased MetHb in the vascular compartment during acute anemia may reflect activation of adaptive mechanisms which augment NO signaling. Understanding the link between anemia, MetHb and its treatments (transfusion of stored blood) may help us to develop novel treatment strategies to reduce the risk of anemia-induced morbidity and mortality.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".