In vivo evidence of significant placental growth factor release by normal pregnancy placentas
Bibliographic record
Abstract
Placental growth factor (PlGF) is an angiogenic factor identified in the maternal circulation, and a key biomarker for the diagnosis and management of placental disorders. Furthermore, enhancing the PlGF pathway is regarded as a promising therapy for preeclampsia. The source of PlGF is still controversial with some believing it to be placental in origin while others refute this. To explore the source of PlGF, we undertook a prospective study enrolling normal pregnant women undergoing elective caesarean section. The level of PlGF was estimated in 17 paired serum samples from the uterine vein (ipsilateral or contralateral to the placental insertion) during caesarean section and from a peripheral vein on the same day and second day post-partum. PlGF levels were higher in the uterine than in the peripheral vein with a median difference of 52.2 (IQR 20.1-85.8) pg/mL p = 0.0006. The difference when the sampled uterine vein was ipsilateral to the placenta was 54.8 (IQR 37.1-88.4) pg/mL (n = 11) and 23.7 (IQR -11; 70.5) pg/mL (n = 6) when the sample was contralateral. Moreover, PlGF levels fell by 83% on day 1-2 post-partum. Our findings strongly support the primary source of PlGF to be placental. These findings will be of value in designing target therapies such as PlGF overexpression, to cure placental disorders during pregnancy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".