Bibliographic record
Abstract
MicroRNAs are small noncoding RNAs responsible for regulating 40% to 60% of gene expression at the posttranscriptional level. The discovery of circulating microRNAs in several biological fluids opened the path for their study as biomarkers and long-range cell-to-cell communication mediators. Their transfer between individuals in the case of blood transfusion, for example, and their high enrichment in milk have sparked the interest for microRNA transfer through diet, especially from mothers to infants during breastfeeding. The extension of such paradigm led to the study of milk microRNAs in the case of cow or goat milk consumption in adults. Here we provide a comprehensive critical review of the key findings surrounding milk microRNAs in human, cow, and goat milk among other species. We discuss the data on their biological properties, their use as disease biomarkers, their transfer between individuals or species, and their putative or verified functions in health and disease of infants and adult consumers. This work is based on all the literature available and integrates all the results, theories, debates, and validation studies available so far on milk microRNAs and related areas of investigations. We critically discuss the limitations and outline future aspects and avenues to explore in this rapidly growing field of research that could impact public health through infant milk formulations or new therapies. We hope that this comprehensive review of the literature will provide insight for all teams investigating milk RNAs' biological activities and help ensure the quality of future reports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".