ERYTHROPOIETIN NEUROPROTECTIVE PROPERTY AS NEW AS THERAPEUTIC TOOL FOR APNEA OF PREMATURITY
Bibliographic record
Abstract
We tested the hypothesis that Epo (Erythropoietin) protects newborns against the consequences of IH induced by apnea of prematurity (AoP). As caffeine (despite ineffective in about 50% of cases) is the treatment of choice of AoP, the effect of Epo and caffeine was compared. Male and female newborn rats exposed to IH during postnatal days (P) 3–10 were used in this work. During this time, animals were daily gavage with vehicle, Epo, caffeine, and Epo+caffeine (10–12 pups/group). At P10 the frequency of apneas and mean duration of apnea at rest were measured (as index of respiratory dysfunction induced by IH) plethysmography. Next, the hippocampus, cortex, and brainstem were harvested, and the activity of Superoxide‐Dismutase (SOD – major anti‐oxidant), Glutathion peroxydase (GPx) and NADPH oxidase (NOX – major pro‐oxidant) enzymes were evaluated. Our results showed that IH increased the frequency of apnea and mean duration of apnea in male and female, reduced SOD anf GPx activity, but increased NOX activity. Interestingly, Epo and caffeine significantly reduced apnea frequency, but only Epo efficiently restored the oxidant activity to normal levels. Moreover, the administration of Epo and caffeine together provided cumulative beneficial effects in reducing the apneic episodes in male and female pups. We conclude that Epo and caffeine activate different, but complementary mechanisms to decrease apneas, suggesting that combined Epo+caffeine treatment could be clinically relevant against AoP. Support or Funding Information Founded by CIHR, Région Rhône alpes and consulat de France à Québec. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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) | 0.001 | 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".