Effect of fructose intake during gestation alters gene expression of obesity and inflammatory markers in adipose tissue
Bibliographic record
Abstract
Fructose (FR) consumption is a major contributor to obesity and chronic inflammation but its effects have not been widely studied in pregnancy. Pregnancy is a state of systemic inflammation, and increased expression of inflammatory markers in pregnancy has been associated with pre‐eclampsia and other vascular complications; many of these markers may arise from adipose tissue. The study investigated the effect of FR consumption during gestation and its effect on obesity and inflammatory markers (leptin, mass and obesity‐associated (FTO) gene and nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐kB) gene) Female rats received either 10% FR solution (n=18) or tap water (CNTL) (n=17) during gestation. Pregnancies were terminated at gestation day 20; adipose tissue was frozen and total RNA extracted for real time PCR analysis. mRNA abundance was performed for the genes encoding leptin, FTO gene and NF‐kB with housekeeping gene 18s. mRNA expression of FTO (FR:1.75( 0.3; CNTL: 1( 0.1; p<0.05), NF‐?B (FR:1.67( 0.2; CNTL: 1( 0.2; p<0.05) and leptin (FR:2.1( 0.5; CNTL: 1( 0.1; p<0.05) were all upregulated with the FR group. Maternal weight exhibited no difference between the FR and CNTL groups even though markers of obesity and inflammation showed enhanced expression. Further research on inflammatory markers is required to examine the state of systemic inflammation present. Funded by NSERC Canada
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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 | 0.001 |
| 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".