Differential HIF and NOS responses to acute anemia: defining organ-specific hemoglobin thresholds for tissue hypoxia
Bibliographic record
Abstract
Tissue hypoxia likely contributes to anemia-induced organ injury and mortality. Severe anemia activates hypoxia-inducible factor (HIF) signaling by hypoxic- and neuronal nitric oxide (NO) synthase- (nNOS) dependent mechanisms. However, organ-specific hemoglobin (Hb) thresholds for increased HIF expression have not been defined. To assess organ-specific Hb thresholds for tissue hypoxia, HIF-α (oxygen-dependent degradation domain, ODD) luciferase mice were hemodiluted to mild, moderate, or severe anemia corresponding to Hb levels of 90, 70, and 50 g/l, respectively. HIF luciferase reporter activity, HIF protein, and HIF-dependent RNA levels were assessed. In the brain, HIF-1α was paradoxically decreased at mild anemia, returned to baseline at moderate anemia, and then increased at severe anemia. Brain HIF-2α remained unchanged at all Hb levels. Both kidney HIF-1α and HIF-2α increased earlier (Hb ∼70-90 g/l) in response to anemia. Liver also exhibited an early HIF-α response. Carotid blood flow was increased early (Hb ∼70, g/l), but renal blood flow remained relatively constant, only increased at Hb of 50 g/l. Anemia increased nNOS (brain and kidney) and endothelia NOS (eNOS) (kidney) levels. Whereas anemia-induced increases in brain HIFα were nNOS-dependent, our current data demonstrate that increased renal HIFα was nNOS independent. HIF-dependent RNA levels increased linearly (∼10-fold) in the brain. However, renal HIF-RNA responses (MCT4, EPO) increased exponentially (∼100-fold). Plasma EPO levels increased near Hb threshold of 90 g/l, suggesting that the EPO response is sensitive. Collectively, these observations suggest that each organ expresses a different threshold for cellular HIF/NOS hypoxia responses. This knowledge may help define the mechanism(s) by which the brain and kidney maintain oxygen homeostasis during anemia.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".