MiRNAs in Poultry Health and Production: Progress and Challenges
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small, non-coding RNA molecules that play crucial roles in the regulation of gene expression. In poultry, miRNAs have emerged as significant regulators of various biological processes, such as growth, development, immune response, and disease resistance in humans, plants, and animals. Recent studies have identified numerous miRNAs in different poultry species (chickens, geese, turkeys, quails, and ducks) and have elucidated their roles in various economically important traits in these species. This review aims to provide a comprehensive overview of the progress made in understanding the functions and mechanisms of miRNAs in poultry as well as the challenges faced in this field. Firstly, an overview of progress in the identification and validation of miRNAs in each species is provided. Then, specific roles of miRNAs in different classes of phenotypic traits are presented. Finally, the challenges and impacts of key technologies and methods, including artificial intelligence, genome editing, and single-cell sequencing, are discussed. From state-of-the-art research on miRNA functions across different trait classes to up-to-date technology trends, this review provides a comprehensive picture of miRNAs in poultry, which might open up new research and application possibilities.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".