Maternal obesity reduces placental autophagy marker expression in uncomplicated pregnancies
Bibliographic record
Abstract
AIM: Obesity has been associated with changes in autophagy and its increasing prevalence among pregnant women is implicated in higher rates of placental-mediated complications of pregnancy such as pre-eclampsia and intrauterine growth restriction. Autophagy is involved in normal placentation, thus changes in autophagy may lead to impaired placental function and development. The aim of this study was to investigate the connection between obesity and autophagy in the placenta in otherwise uncomplicated pregnancies. METHODS: ) pregnant women with singleton pregnancies undergoing planned Caesarean delivery without labor at term. Samples were analyzed for autophagic markers LC3B and p62 in the peripheral, middle and central regions of the placenta and in omental adipocytes, milky spots and vasculature. RESULTS: As pre-pregnancy BMI increased, there was an increase in both placental and fetal weight as well as decreased levels of LC3B in the central region of the placenta (P = 0.0046). Within the obese patient group, LC3B levels were significantly decreased in the placentas of male fetuses compared to females (P < 0.0001). Adipocytes, compared to milky spots and vasculature, had lower levels of p62 (P = 0.0127) and LC3B (P = 0.003) in obese omenta and lower levels of LC3B in control omenta (P = 0.0071). CONCLUSION: Obesity leads to reduced placental autophagy in uncomplicated pregnancies; thus, changes in autophagy may be involved in the underlying mechanisms of obesity-related placental diseases of 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 | 0.000 | 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) | 0.002 | 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".