Efficacy of consumption of whole and fractionated pulses on plasma lipids in diet‐induced dyslipidemic Syrian hamsters (829.19)
Bibliographic record
Abstract
The objective was to examine effects of whole and fractions of pinto beans and peas on diet induced dyslipidemia. Hamsters were randomly assigned to consume high fat diet (n=12 each) including control (CON); bean starch (BS); bean fibre (BF); whole bean (WB); cooked whole bean (CWB); inner pea fibre (IPF); pea hull fibre (PHF); pea protein isolate (PPI); pea protein concentrate (PPC); or whole pea (WP) for 4 wk. Reduced feed intake (FI) and body weight gain (BWG) were observed with WB (FI: p=0.030; BWG: p<0.001), BS (FI: p=0.041; BWG: p<0.001) and BF (FI: p=0.037; BWG: p<0.001) compared to CON. However, both FI and BWG did not differ between CWB and CON. Plasma total, HDL and non‐HDL cholesterol, triglyceride and glucose concentrations were lower (p<0.05) in CWB, WB, BS and BF compared to CON. Furthermore, no changes in plasma lipids were observed with BF and BS compared to WB. No changes in FI, BWG and plasma glucose and lipid profile were observed with any of the peas supplemented groups compared to CON. A positive hemagglutination was observed with raw WB, BS and BF, but not CWB, against rabbit erythrocytes. Data suggest that, while whole and fractionated peas failed to alter plasma lipids and glucose levels, fractions and whole beans possess lipid lowering and hypoglycemic effects in hamsters, but must be cooked prior to intake. Results indicate that bean consumption can improve blood lipids and reduce cardiovascular disease risk Grant Funding Source : Supported by Pulse Canada; Agriculture and Agri‐Food 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) | 0.001 | 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 | 0.001 | 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".