Nuts and Nut-Based Products: A Meta-Analysis from Intake Health Benefits and Functional Characteristics from Recovered Constituents
Bibliographic record
Abstract
This review provides information on nutritional characteristics, extraction techniques, bioactive compounds, bioavailability and bioaccessibility through in vitro and in vivo assays on nuts and food products obtained from walnuts, such as almonds, walnuts, cashew nuts, pistachios, hazelnuts, walnuts, walnuts, macadamia nuts, Brazil nuts, pine nuts and peanuts. The influence of the consumption of these nuts on human health was carried out through a meta-analysis. Data meta-analysis indicated that nut consumption has a positive effect on total cholesterol, high-density lipoprotein, and low-density lipoprotein levels in the population. Although there are promising studies, more research is needed to determine the beneficial effects of these nuts when applied to products.Abbreviations: ALA: Alpha Linolenic Acid; Ca: Calcium; CVD: Cardiovascular Disease; CI: Confidence Interval; DBP: Diastolic Blood Pressure; EAE: Enzyme Assisted Extraction; GRAS: Generally Recognized as Safe; HDL: high-density lipoprotein; LDL: Low-Density Lipoprotein; Mg: Magnesium; MD: Mean Difference; MAE: Microwave Accelerated Extraction; MUFAS: Monounsaturated Fatty Acids; PUFAS: Polyunsaturated Fatty Acids; K: Potassium; PLE: Pressurized Liquid Extraction; SFAs: Saturated Fatty Acids; SD: Standard Deviation; SFE: Supercritical Fluid Extraction; SBP: Systolic Blood Pressure; UAE: Ultrasound Accelerated Extraction; Zn: Zinc
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.038 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".