Erythropoietin improves skeletal muscle microcirculation in mice with sepsis through activation of eNOS
Bibliographic record
Abstract
This study is to explore the potential mechanisms involved in the improvement of microcirculation by the EPO in septic mice. Methods Sepsis was induced in mice by intraperitoneal injection of a fecal suspension (12.5 mg/mouse). The septic mice were given rHuEPO (400 U/kg) 18 hrs after sepsis. Capillary perfusion and nicotinamide adenine dinucleotide (NADH) fluorescence were observed using an intravital microscopy. In addition, endothelial cells derived from the skeletal muscle were treated with rHuEPO (5 U/ml) and eNOS activation and activity were assessed. Results Septic mice demonstrated microcirculatory dysfunction with a decreased capillary perfusion and an increased tissue NADH fluorescence indicating impaired tissue bioenergetics. In contrast, septic mice treated with rHuEPO resulted in an improvement in the perfused capillary density and decreased muscle NADH fluorescence. However, this beneficial effect of rHuEPO did not occur in septic mice treated with L‐NAME (20 mg/kg) or mice deficient in eNOS. Treatment of endothelial cells with rHuEPO resulted in an increase in eNOS phosphorylation and NO production. In conclusion Our results suggest that eNOS plays an important role in mediating the beneficial effect of rHuEPO on microcirculation in this model of sepsis. (Supported by funding from Department of National Defense, Canadian Forces Medical Group).
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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 | 0.001 |
| 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".