Fructose consumption during gestation alters glucose metabolism and placental expression of TNF‐α and SNAT2
Bibliographic record
Abstract
Increased fructose (FR) consumption has been associated with obesity and insulin resistance, which, if present during gestation, may increase the risk of metabolic disease in the offspring. Alterations in placental transport may mediate these effects. This study examined the effect of FR consumption during gestation on placental mRNA expression of nutrient transporters (GLUT3, CD36, SNAT2) and the pro‐inflammatory cytokine TNF‐α. Female rats received either 10% FR solution (n=18) or tap water (CNTL) (n=17) during gestation. Maternal blood was collected before gestation, and on gestational days (GD) 12 and 19; pregnancies were terminated on GD 20. Fetuses and placentas were weighed. Placental mRNA expression was quantified using real‐time PCR. Maternal weight, placental weight, fetal weight and GLUT3 and CD36 expression were not different between FR fed rats and CNTL rats. Maternal glucose (mmol/L) was elevated on GD 19 in FR vs. CNTL rats (6.26 ± 1.2 vs 5.18 ± 0.7; p<0.05). Insulin (ng/ml) was increased at mid‐gestation (FR: 2.74 ± 1.5; CNTL: 1.41± 0.5; p<0.05) and on GD 19 (FR: 2.11 ± 1.3; CNTL: 1.07 ± 0.4; p<0.05). Placental expression of SNAT2 (FR: 1.61 ± 0.1; CNTL: 0.90 ± 0.3; p<0.05) and TNF‐α (FR: 1.38 ± 0.2; CNTL: 0.82 ± 0.2; p<0.05) was increased in FR rats compared to CNTLs. Although overall fetal growth appeared to be unaffected, further studies examining changes in offspring metabolism are warranted. NSERC
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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) | 0.001 | 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".