Cardiovascular adaptations to moderate anemia maintain cerebral perfusion but are inadequate to prevent renal and splanchnic tissue hypoxia
Bibliographic record
Abstract
Mild to moderate anemia is associated with increased organ injury and mortality in surgical patients by undefined mechanisms. We hypothesize that anemia‐induced tissue hypoxia is a potential unifying mechanism. A transgenic mouse model, with luciferase fused to hypoxia inducible factor (HIF ODD‐Luciferase) was utilized to assess the impact of acute antibody‐mediated anemia on adaptive cardiovascular responses, tissue PO 2 (PtO 2 ), and HIF expression. A red blood cell (RBC)‐specific antibody (TER119) induced anemia, reducing hemoglobin concentrations from 144±9 to 94±11 g/L (p<0.006) via intravascular hemolysis and RBC sequestration in the spleen and liver. Anemia‐induced cardiovascular adaptations included: 1) increased peripheral arterial oxygen saturation (p=0.018); 2) increased cardiac output (26%, p=0.011); 3) increased internal carotid blood flow (80%, p<0.001); and 4) augmented cerebrovascular reactivity relative to control mice (p<0.001). All of these mechanisms contributed to the maintenance of brain PtO 2 (Control vs. Anemia: 23±10 vs.23±5 mmHg O 2 , p=0.935). By contrast, no increase in renal blood flow occurred (p=0.239), and kidney PtO 2 decreased in anemic mice (21±4 vs. 13±4 mmHg O 2 , p<0.001). Molecular adaptations to tissue hypoxia included increased HIF‐1α expression within the dorsal renal and hepatic region (30%, p=0.006), and ventral gut region (72%, p=0.017). These findings demonstrate anemia‐induced tissue hypoxia is heterogeneous and organ‐specific and support the hypothesis that renal and splanchnic hypoxia may contribute to increased organ injury and mortality in anemic perioperative patients. Support or Funding Information The project described was supported by the St. Michael's Hospital AHSC AFP Innovation Fund Grant Support
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 | 0.000 |
| 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".