Hepatic VLDL-TG Production and MTP Gene Expression are Decreased in Ovariectomized Rats: Effects of Exercise Training
Bibliographic record
Abstract
The present study was designed to investigate the effects of estrogen withdrawal and exercise training on hepatic very low density lipoprotein-triglyceride (VLDL-TG) production and on expression of genes involved in hepatic VLDL synthesis in response to lipid infusion. Female Sprague-Dawley rats underwent ovariectomy (Ovx), sham surgery (Sham), and Ovx with 17β-estradiol supplementation (OvxE2) before being subdivided into sedentary (Sed) and trained (Tr) groups for 8 weeks. Exercise training consisted of continuous running on a rodent treadmill 5 times/wk. At the end of the 8-week period, all rats in the fasted state were intravenously infused with a 20% solution of Intralipid for 3-h followed by an injection of Triton WR-1339 to block lipoprotein lipase activity. Plasma TG accumulation was subsequently measured during 90 min to estimate VLDL-TG production. An additional control group consisting of Sham-Sed rats was infused with saline (0.9% NaCl). Estrogen withdrawal resulted in higher (p<0.01) liver fat accumulation concomitantly with lower (p<0.01) VLDL-TG production and lower mRNA and protein content of hepatic microsomal triglyceride transfer protein (MTP). All of these effects in Ovx rats were corrected with estrogen supplementation. Training in Ovx rats reduced (p<0.01) liver fat accumulation and further reduced (p<0.01) hepatic VLDL-TG production along with gene expression of MTP and diacylglycerol acyltransferase-2 (DGAT-2). It is concluded that VLDL-TG synthesis and/or secretion is decreased in Ovx rats probably via MTP regulation and that this decrease may constitute one of the factors involved in hepatic fat accumulation. The training effect on reducing VLDL production was independent of the estrogenic status.
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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 | 0.000 |
| 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".