Barley protein supplementation and oxidative damage
Bibliographic record
Abstract
Objective To assess the effect of vegetable protein as high protein barley flour, baked into bread, on risk factors for heart disease (i.e. serum lipids and markers of oxidative damage). Method In a randomized controlled crossover design, 23 hypercholesterolemic adults (16F, 7M; 57±7y; LDL‐C, 3.90±0.21mmol/L) completed two 4‐week treatments of bread supplementation containing either 30g/d (per 2000 kcal diet) of barley protein (treatment) or dairy protein (calcium caseinate placebo). Outcomes included serum lipids and markers of oxidative damage, measured as serum protein thiols, and MDA and conjugated dienes in the LDL fraction. Results At week 4, supplementation with barley protein did not provide evidence of improved LDL‐C and TC:HDL‐C, expressed as the difference from baseline (‐0.05±0.12 mmol/L, P=0.685 and 0.19±0.15 mmol/L, P=0.216, respectively) compared to control (‐0.02±0.12 mmol/L, P=0.858 and 0.19±0.11 mmol/L, P=0.094, respectively). No effect was seen on serum protein thiols, as an overall marker of oxidative status. In addition, neither MDA nor conjugated dienes in the LDL fraction, as markers of oxidized LDL, were affected differently by barley compared to dairy protein. Conclusion Barley protein appears similar to low fat dairy protein in its effect on blood lipids and markers of oxidative damage. Research Support: NSERC, Loblaw Companies Limited Grant Funding Source Canadian Institutes of Health Research
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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) | 0.000 | 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 | 0.000 |
| 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".