The hypocholesterolemic effects of guar gum consumption are mediated by increased nuclear form of sterol regulatory element binding protein 2 (SREBP2) in the pig model
Bibliographic record
Abstract
To investigate the molecular basis for the hypocholesterolemic effects of guar gum (GG) consumption, fourteen grower boars were fed an atherogenic basal (control) diet supplemented with 10% GG for 30 days according to a completely randomized block design. GG consumption lowered ( P < 0.05) plasma total cholesterol (78.45 ± 6.31 vs. 110.02 ± 5.56 mg/dl) and LDL cholesterol (41.03 ± 6.22 vs. 64.97 ± 5.83 mg/dl) concentrations in comparison to the control diet. Real time RT‐PCR and immunoblot analyses were used to quantify the hepatic mRNA and protein abundance of SREBP2 and its target genes, 3‐hydroxy‐ 3methylglutaryl coenzyme A reductase (HMG‐CoAr) and low‐density lipoprotein receptor (LDLr). SREBP2 mRNA expression was similar ( P = 0.89) between the control and the GG‐supplemented groups. GG consumption increased the mRNA expression of HMG‐CoAr (8‐fold, P = 0.04) and LDLr (1.5 fold, P = 0.20) in comparison to the control groups. GG consumption increased (1.5 fold, P = 0.02) the nuclear form of SREBP2 in comparison to the control group. While no difference ( P = 0.88) in HMG‐CoAr protein abundance was detected, LDLr protein abundance was increased in the GG group (1.8 fold, P = 0.04) in comparison to the control group. These results suggest that the hypocholesterolemic effects of GG are partially mediated by increased amount of the nuclear, active form of hepatic SREBP2.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".