Glucocorticoid Receptor Antagonist Alters Corticosterone and Receptor-sensitive mRNAs in the Hypoxic Neonatal Rat
Bibliographic record
Abstract
Hypoxia, a common stressor with preterm birth, increases morbidity and mortality associated with prematurity. Glucocorticoids (GCs) are administered to the preterm infant to improve oxygenation; prolonged use of GCs remains controversial. We evaluated a selective glucocorticoid receptor (GR) antagonist (CORT113176) in our neonatal rat model of human prematurity to assess how fasting and hypoxia-induced increases in neonatal corticosterone affects endogenous hormones and endocrine pancreas function. Neonatal rat pups at postnatal day (PD) 2, PD8, and PD15 were pretreated with CORT113176 and, after 60 minutes of separation and fasting, exposed to hypoxia (8% O2) or control (normoxia) for 30 or 60 minutes while fasting was continued. Plasma corticosterone, ACTH, glucose, and insulin were measured and fasting Homeostatic Model Assessment of Insulin Resistance was calculated. Glucocorticoid and insulin receptor-sensitive gene mRNAs were analyzed in liver, muscle, and adipose to evaluate target tissue biomarkers. CORT113176 pretreatment augmented baseline and hypoxia-induced increases in corticosterone and attenuated hypoxia-induced increases in insulin resistance at PD2. Normoxic and hypoxic stress increased the hepatic GR-sensitive gene mRNAs, Gilz and Per1; this was eliminated by pretreatment with CORT113176. CORT113176 pretreatment decreased baseline insulin receptor-sensitive gene mRNAs Akt2, Irs1, Pik3r1, and Srebp1c at PD2. We show that CORT113176 variably augments the stress-induced increases in corticosterone concentrations (attenuation of negative feedback) and that GR is critical for hepatic responses to stress in the hypoxic neonate. We also propose that measurement of Gilz and Per1 mRNA expression may be useful to evaluate the effectiveness of GR antagonism.
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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) | 0.002 | 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".