Uncovering the Placental Mechanisms that Contribute to Preterm Birth and Risk for Adverse Offspring Development in Pregnancies Complicated by Suboptimal Maternal Body Mass Index
Bibliographic record
Abstract
The objectives of this thesis were to understand how maternal underweight, obesity, and preterm birth alter placental development and function.Placental pathology data were obtained from an archived dataset, and qPCR and immunohistochemistry were used in a current cohort, to explore the effects of maternal body mass index (BMI) and/or preterm birth on placental development and function.Increased maternal BMI associated with increased placental inflammation, maternal vascular malperfusion, and decreased placental efficiency.Preterm placentae had increased expression of multidrug resistance transporters, and altered expression of antimicrobial peptides.These findings revealed maternal underweight and obesity are not inert conditions for the developing placenta.Upregulated placental efflux transport earlier in gestation may regulate fetal exposure to increased inflammation/infection at preterm, while altered placental defences may impair placental-mediated fetal protection.Understanding placental adaptations in these common conditions helps to uncover the mechanisms linking suboptimal maternal BMI and inflammatory states with adverse pregnancy outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".