Placenta nutrient transport-related gene expression: the impact of maternal obesity and excessive gestational weight gain
Bibliographic record
Abstract
OBJECTIVE: Maternal obesity and excess gestational weight gain (GWG) increase the risk of delivering large infants. This study examined the associations between maternal obesity and GWG on the expression of genes involved in fatty acid, amino acid and glucose transport, and the mechanistic target of rapamycin (mTOR) and insulin signaling axes in the placenta. METHODS: Placenta samples were obtained from lean (n = 11) and obese (n = 10) women. Gene expression in the placenta was measured using polymerase chain reaction. RESULTS: There were no differences in placenta gene expression between the lean and obese women, with the exception of lower expression of mTOR in the women with obesity who delivered male offspring (obese n = 6; lean n = 7). GWG in excess of the upper limit of the body mass index (BMI) specific guidelines was correlated with increased expression of SNAT1 and decreased expression of FABP3, mTOR, IRS1 and IGF1R. CONCLUSIONS: Variations in GWG may alter the expression of genes involved in regulating placental nutrient transport. Future research on placental nutrient transport should account for the sex of the offspring and the percentage of GWG that is gained above the upper limit for the pre-pregnancy BMI.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".