Maternal Hyperglycemia Improves Fetal Cardiac Function During Tachycardia-Induced Heart Failure in Pigs
Bibliographic record
Abstract
BACKGROUND: Fetal tachycardia often leads to cardiac failure, which in experimental settings can be prevented by direct fetal glucose-insulin administration. In this study, we hypothesize that similar effects can be obtained indirectly by inducing maternal hyperglycemia. METHODS AND RESULTS: Systolic and diastolic indices (dP/dt(max) and tau) of left ventricular function were measured by use of high-fidelity catheters during 180 minutes of aggressive atrial pacing ( approximately 300 bpm) in 12 preterm porcine fetuses. In 6 fetuses, maternal hyperglycemia (15 mmol/L) was induced for the last 120 minutes of pacing. The remaining fetuses served as controls. Glucose, insulin, and free fatty acid levels were determined, as was fetal myocardial glycogen content. Maternal glucose infusion led to significant fetal hyperglycemia and hyperinsulinemia but did not change the inherently low fetal levels of free fatty acids. There were no differences between groups with regard to dP/dt(max) (1025+/-226 and 1037+/-207 mm Hg, P=NS) and tau (20.6+/-2.0 and 21.4+/-1.6 ms, P=NS) at baseline (100%). During the 180 minutes of pacing, systolic function (dP/dt(max)) and diastolic function (tau) deteriorated more in the control group than in the hyperglycemic group (P<0.001 for both). At 180 minutes, dP/dt(max) was 62+/-18% of baseline in controls and 85+/-11% in hyperglycemic fetuses (P=0.03), and tau was 117+/-12% and 98+/-4%, respectively (P=0.004). CONCLUSIONS: Induced maternal hyperglycemia improves fetal cardiac function during fetal tachycardia and suggests a possible additional therapeutic option to improve the function of the failing fetal heart before or during antiarrhythmic therapy. The findings may be relevant in fetal heart failure in general.
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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".