Effects of FBCx® on lipid lowering in LDLr‐KO mice
Bibliographic record
Abstract
FBCx®, a new soluble fiber (α‐cyclodextrin), has been shown to bind with fat, thus reducing the absorption of dietary fat. It also reduces blood lipid levels and prevents body weight gain in high‐fat fed rats. In order to investigate the efficacy of FBCx in reducing the blood cholesterol (C) levels and possibly atherogenic lesion development in a common mouse model of atherosclerosis, female LDLr knock‐out (LDLr‐KO) mice, 10 wks old, were divided into the high‐fat “western” diet (CON, 21.2% milkfat w/w, n =10) or the same diet with 2.12% FBCx (FBCx, n =10) groups. After 10 wks ad‐lib feeding, mice were fasted for 6 h and blood samples collected by retro‐orbital bleeding. Food intake and body weight were the same in the 2 groups. Compared to CON mice, mice fed the FBCx diet showed significant reductions in blood total C (−14%, 30.5±3.7 vs. 26.2±3.7 mmol/l, p<.05); phospholipids (−11%, 16.3±1.8 vs. 14.4±1.8 mmol/l, p<.05); free C (−15.9%, 9.0±1.1 vs. 7.6±1.4 mmol/l, p<.05) and C ester (−13.3%, 21.5±2.5 vs. 18.7±2.5 mmol/l, p<.05). Fasting triglyceride levels were not different between the 2 groups. FPLC data showed that the reduction in C levels was mainly in the LDL containing fractions. Therefore, in an animal model of hypercholesterolemia due to the lack of the LDL‐receptor, FBCx demonstrated its efficacy in reducing the blood cholesterol and its components. Oral FBCx may be an effective treatment for hypercholesterolemia.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".