Dairy nutrients and their effect on inflammatory profile in molecular studies
Bibliographic record
Abstract
Dairy products contain milk fat, proteins, minerals, vitamin D, and other bioactive nutrients that have the potential to contribute to the association observed between increased dairy intake and a decreased risk of inflammation. The objective of this paper is to review the role of dairy bioactive molecules including dairy fat, proteins, micronutrients, and vitamins on inflammation markers in adipose, macrophage, and vascular tissues, which play a key role in the regulation of inflammation. A review was conducted to identify current scientific literature on dairy nutrients and inflammation in cell studies published until November 2014. The majority of saturated fatty acids (FAs) activate proinflammatory markers. Therefore, other dairy FAs or components may offset these harmful effects. Protein and amino acid composition of dairy products may have anti-inflammatory action. Magnesium may have beneficial effects on inflammatory profile; on the contrary, studies on vitamin D demonstrate conflicting results. In conclusion, numerous studies assessed the effects of individual or mixtures of FAs on inflammatory markers; yet, there is far less research on the effects of other dairy bioactive nutrients. The exact bioactive molecule or combination of these molecules in dairy products, which underlies the inverse association between dairy intake and inflammation remains to be elucidated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".