Quantification of fetal organ volume and fat deposition following in utero exposure to maternal Western Diet using MRI
Bibliographic record
Abstract
PURPOSE: To examine the feasibility of using MRI to identify differences in liver size and fat deposition in fetal guinea pigs exposed to an in utero environment influenced by maternal consumption of a Western diet. MATERIALS AND METHODS: Female guinea pigs fed either an energy-dense Western Diet (WD), comprised of increased saturated fats and simple sugars, or a Control Diet (CD) from weaning through pregnancy, underwent MR scanning near term (~ 60 days; term ~ 69 days). Maternal weights were collected at mating and at MR scanning. T1-weighted, T2-weighted, and IDEAL water-fat images were acquired at 3 Tesla. The images were used to segment maternal adipose tissue, fetal liver, fetal brain, fetal adipose tissue, and total fetal volumes and to measure maternal and fetal hepatic fat fractions. RESULTS: Weights of WD sows were lower prior to pregnancy (P = .04), however their weight gain over pregnancy did not differ from the CD group (P = .98). The WD sows had less total adipose tissue (TAT) at MR scanning (P = .04), while hepatic fat content was significantly elevated (P = .04). When controlling for litter size, WD fetuses had larger livers (P = .02), smaller brains (P = .01), and increased total adipose tissue volume (P = .01) when normalized by fetal volume. The WD fetuses also had increased hepatic fat fractions compared to CD fetal livers (P < .001). CONCLUSION: Maternal Western Diet consumption prior to and during pregnancy induces differences in maternal liver fat content, fetal liver volume and liver fat storage, as well as changes in fetal adipose tissue deposition that can be measured in utero using MRI.
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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 | 0.001 | 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".