Sildenafil Citrate Increases Fetal Weight in a Mouse Model of Fetal Growth Restriction with a Normal Vascular Phenotype
Bibliographic record
Abstract
Fetal growth restriction (FGR) is defined as the inability of a fetus to achieve its genetic growth potential and is associated with a significantly increased risk of morbidity and mortality. Clinically, FGR is diagnosed as a fetus falling below the 5(th) centile of customised growth charts. Sildenafil citrate (SC, Viagra™), a potent and selective phosphodiesterase-5 inhibitor, corrects ex vivo placental vascular dysfunction in FGR, demonstrating potential as a therapy for this condition. However, many FGR cases present without an abnormal vascular phenotype, as assessed by Doppler measures of uterine/umbilical artery blood flow velocity. Thus, we hypothesized that SC would not increase fetal growth in a mouse model of FGR, the placental-specific Igf2 knockout mouse, which has altered placental exchange capacity but normal placental blood flow. Fetal weights were increased (by 8%) in P0 mice following maternal SC treatment (0.4 mg/ml) via drinking water. There was also a trend towards increased placental weight in treated P0 mice (P = 0.056). Additionally, 75% of the P0 fetal weights were below the 5(th) centile, the criterion used to define human FGR, of the non-treated WT fetal weights; this was reduced to 51% when dams were treated with SC. Umbilical artery and vein blood flow velocity measures confirmed the lack of an abnormal vascular phenotype in the P0 mouse; and were unaffected by SC treatment. (14)C-methylaminoisobutyric acid transfer (measured to assess effects on placental nutrient transporter activity) per g placenta was unaffected by SC, versus untreated, though total transfer was increased, commensurate with the trend towards larger placentas in this group. These data suggest that SC may improve fetal growth even in the absence of an abnormal placental blood flow, potentially affording use in multiple sub-populations of individuals presenting with FGR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".