Gene regulation by dietary microRNAs
Bibliographic record
Abstract
MicroRNAs (miRNAs) silence genes through destabilizing mRNA or preventing translation of mRNA, thereby playing an essential role in gene silencing. Traditionally, miRNAs have been considered endogenous regulators of genes, i.e., miRNAs synthesized by an organism regulate the genes in that organism. Recently, that dogma has been challenged in studies suggesting that food-borne miRNAs are bioavailable and affect gene expression in mice and humans. While the evidence in support of this theory may be considered weak for miRNAs that originate in plants, there is compelling evidence to suggest that humans use bovine miRNAs in cow's milk and avian miRNAs in chicken eggs for gene regulation. Importantly, evidence also suggests that mice fed a miRNA-depleted diet cannot compensate for dietary depletion by increased endogenous synthesis. Bioinformatics predictions implicate bovine miRNAs in the regulation of genes that play roles in human health and development. Current challenges in this area of research include that some miRNAs are unable to establish a cause-and-effect between miRNA depletion and disease in miRNA knockout mice, and sequence similarities and identities for bovine and human miRNAs render it difficult to distinguish between exogenous and endogenous miRNAs. Based on what is currently known about dietary miRNAs, the body of evidence appears to be sufficient to consider milk miRNA bioactive compounds in foods, and to increase research activities in this field.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.002 |
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".