Red blood cell antibody-induced anemia causes differential degrees of tissue hypoxia in kidney and brain
Bibliographic record
Abstract
Moderate anemia is associated with increased mortality and morbidity, including acute kidney injury (AKI), in surgical patients. A red blood cell (RBC)-specific antibody model was utilized to determine whether moderate subacute anemia could result in tissue hypoxia as a potential mechanism of injury. Cardiovascular and hypoxic cellular responses were measured in transgenic mice capable of expressing hypoxia-inducible factor-1α (HIF-1α)/luciferase activity in vivo. Antibody-mediated anemia was associated with mild intravascular hemolysis (6 h) and splenic RBC sequestration ( day 4), resulting in a nadir hemoglobin concentration of 89 ± 13 g/l on day 4. At this time point, renal tissue oxygen tension (P t O 2 ) was decreased in anemic mice relative to controls (13.1 ± 4.3 vs. 20.8 ± 3.7 mmHg, P < 0.001). Renal tissue hypoxia was associated with an increase in HIF/luciferase expression in vivo ( P = 0.04) and a 20-fold relative increase in renal erythropoietin mRNA transcription ( P < 0.001) but no increase in renal blood flow ( P = 0.67). By contrast, brain P t O 2 was maintained in anemic mice relative to controls (22.7 ± 5.2 vs. 23.4 ± 9.8 mmHg, P = 0.59) in part because of an increase in internal carotid artery blood flow (80%, P < 0.001) and preserved cerebrovascular reactivity. Despite these adaptive changes, an increase in brain HIF-dependent mRNA levels was observed (erythropoietin: P < 0.001; heme oxygenase-1: P = 0.01), providing evidence for subtle cerebral tissue hypoxia in anemic mice. These data demonstrate that moderate subacute anemia causes significant renal tissue hypoxia, whereas adaptive cerebrovascular responses limit the degree of cerebral tissue hypoxia. Further studies are required to assess whether hypoxia is a mechanism for acute kidney injury associated with anemia.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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 teacher head, 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".