A maternal high fat diet has long‐lasting effects on skeletal muscle lipid and PLIN protein content in rat offspring at young adulthood (1162.7)
Bibliographic record
Abstract
High fat diets (HFD) can have adverse effects on skeletal muscle development. Skeletal muscle PLIN proteins (PLIN2, 3, & 5) are thought to play critical roles in lipid metabolism, however effects of HFD on PLINs as well as lipases (HSL, ATGL, CGI‐58) have yet to be investigated. The objective of this study was to determine whether HFD would influence skeletal muscle lipase and PLIN protein content in dams as well as offspring at weaning (19d) and young adulthood (3mo). Female rats (28d old, n=9/group) were fed control (CON, AIN93G, 7% soybean oil) or HFD (AIN93G, 20% lard) for 10wks prior to mating, throughout pregnancy and lactation. Offspring were weaned onto CON (n=18/group, one female & one male pup per litter was studied at 19d & 3mo of age). Because there was no effect of sex for outcomes measured, male and female data was combined. HFD resulted in increased lipid content in plantaris of dams and pups, both at weaning and at 3mo (p=0.07). HFD resulted in increased PLIN3 content in plantaris of dams (P=0.016) and increased PLIN5 content in pups at weaning and 3mo (p=0.05). PLIN2 and PLIN5 content increased at 3mo vs. weaning (P<0.001). Diet had no effect on ATGL, CGI‐58, or HSL content. The data suggest that exposure to a maternal HFD results in increased skeletal muscle lipid and PLIN5 content in offspring through to young adulthood. Funded by 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".