Erythropoietin Improves Skeletal Muscle Microcirculation Through the Activation of eNOS in a Mouse Sepsis Model
Bibliographic record
Abstract
BACKGROUND: Sepsis and septic shock remain the major causes of morbidity and mortality in intensive care units. One mechanism that leads to organ failure is microcirculatory dysfunction. Erythropoietin (EPO) is a glycoprotein produced by the kidney that primarily regulates erythropoiesis, but it also can exert hemodynamic, anti-inflammatory, and tissue protective effects. We previously reported that administration of EPO to septic mice improves mouse skeletal muscle capillary perfusion and tissue bioenergetics. The objective of this study was to explore the potential mechanism(s) involved. METHODS: Sepsis was induced by intraperitoneal (i.p.) injection of a fecal suspension (12.5 g in 0.5 saline/mouse) in mice. At 18 hours after sepsis induction, a single dose of rHuEPO (400 U/kg) was given to the mice. Mouse capillary perfusion density and nicotinamide adenine dinucleotide (NADH) fluorescence in skeletal muscle were observed using intravital microscopy. Endothelial cells derived from the skeletal muscle were treated with rHuEPO (5 U/mL) and endothelial nitric oxide synthase (eNOS) activation and activity were assessed. RESULTS: Septic mice had decreased capillary perfusion density and increased tissue NADH fluorescence indicating impaired tissue bioenergetics, whereas animals treated with rHuEPO demonstrated an improvement in capillary perfusion density and decreased skeletal muscle NADH fluorescence. The beneficial effect of rHuEPO did not occur in septic mice treated with l-NAME (an NOS inhibitor, 20 mg/kg) or mice genetically deficient in eNOS. Treatment of endothelial cells with rHuEPO resulted in activation of eNOS as indicated by increased eNOS phosphorylation and NO production. CONCLUSIONS: Our results suggest that eNOS plays an important role in mediating the beneficial effect of rHuEPO on microcirculation in this septic mouse model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".