Phosphatidylcholine functional foods and nutraceuticals: A potential approach to prevent non‐alcoholic fatty liver disease
Bibliographic record
Abstract
Abstract Non‐alcoholic fatty liver disease (NAFLD) is the most common liver condition in the developed world and may progress to more severe forms of disease such as cirrhosis or liver cancer. Thus, steps taken to reduce its prevalence through preventative nutritional approaches such as functional foods and nutraceuticals (FFN) should be pursued. It is well known that certain lipotropic nutrients such as choline and metabolites betaine and phosphatidylcholine (PC; lecithin) could prevent or alleviate fatty liver through a variety of mechanisms, including increased hepatic VLDL secretion. Animal and human studies have demonstrated clear protective effects of choline, betaine and PC for NAFLD as well as possible roles in CVD prevention from epidemiological data. Currently, choline consumption is below dietary recommendations due in large part to a general lack of understanding regarding the importance of this nutrient for human health. As lecithin is commonly added (in small amounts) in many processed foods due to its functional capabilities, it is projected that increasing its abundance in the food supply and increasing consumer education and acceptance of lecithin‐based functional foods and nutraceuticals may represent a progressive step towards preventing liver and whole‐body metabolic pathologies in many areas of the world.
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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) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".