Effects of maternal undernutrition during late pregnancy on mitochondrial function of perirenal fat in fetal sheep
Bibliographic record
Abstract
Maternal undernutrition during late gestation impairs fetal development, yet its impact on brown adipose tissue (BAT) thermogenesis remains poorly understood. This study investigated how intrauterine growth restriction induced by maternal undernutrition affects mitochondrial function and thermogenic capacity in fetal ovine perirenal BAT. Eighteen singleton-bearing Mongolian ewes were allocated in their second parity to three dietary groups at gestational day 90: control (control group (CG), 0.67 MJ metabolisable energy (ME)/kg body weight (BW)0.75/day, n = 6), moderate restriction (RG2, 0.33 MJ ME/kg BW0.75/day, n = 6), and severe restriction (RG1, 0.18 MJ ME/kg BW0.75/day, n = 6). At day 140, the fetuses were euthanized and perirenal adipose tissue was dissected, snap-frozen in liquid nitrogen, and stored at −80 °C for analysis. Compared to CG, restricted groups exhibited reduced perirenal fat mass ( P = 0.01), mitochondrial DNA copy number ( P = 0.02), and oxygen consumption ( P = 0.02). Activities of mitochondrial complexes I–III and citrate synthase were significantly decreased in RG1 and moderate restriction ( P < 0.05). These findings indicate that late-gestational undernutrition suppresses BAT thermogenesis by impairing mitochondrial respiratory chain function, potentially compromising neonatal thermoregulation.
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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) | 0.000 | 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) | 0.000 | 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".