Maternal tadalafil treatment does not increase uterine artery blood flow or oxygen delivery in the pregnant ewe
Bibliographic record
Abstract
Abstract Increasing placental perfusion (PP) could improve outcomes of growth‐restricted fetuses. One way of increasing PP may be by using phosphodiesterase (PDE)‐5 inhibitors, which induce vasodilatation of vascular beds. We used a combination of clinically relevant magnetic resonance imaging (MRI) techniques to characterize the impact that tadalafil infusion has on maternal, placental and fetal circulations. At 116–117 days’ gestational age (dGA; term, 150 days), pregnant ewes (n = 6) underwent fetal catheterization surgery. At 120–123 dGA ewes were anaesthetized and MRI scans were performed during three acquisition windows: a basal state and then ∼15–75 min (TAD 1) and ∼75–135 min (TAD 2) post maternal administration (24 mg; intravenous bolus) of tadalafil. Phase contrast MRI and T2 oximetry were used to measure blood flow and oxygen delivery. Placental diffusion and PP were assessed using the Diffusion‐Relaxation Combined Imaging for Detailed Placental Evaluation—‘DECIDE’ technique. Uterine artery (UtA) blood flow when normalized to maternal left ventricular cardiac output (LVCO) was reduced in both TAD periods. DECIDE imaging found no impact of tadalafil on placental diffusivity or fetoplacental blood volume fraction. Maternal‐placental blood volume fraction was increased in the TAD 2 period. Fetal and were not affected by maternal tadalafil administration. Maternal tadalafil administration did not increase UtA blood flow and thus may not be an effective vasodilator at the level of the UtAs. The increased maternal–placental blood volume fraction may indicate local vasodilatation of the maternal intervillous space, which may have compensated for the reduced proportion of UtA .
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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".