Bibliographic record
Abstract
PURPOSE OF REVIEW: MicroRNAs (miRNAs) regulate gene expression by binding to target mRNAs and control a wide range of biological functions. Recent reports have identified specific miRNAs as major regulators of fatty acid and cholesterol homeostasis. This review examines the biological function of various miRNAs and the emerging evidence linking specific miRNAs to critical pathways in lipid metabolism. RECENT FINDINGS: Disruption of lipid balance can lead to metabolic disturbances and thus tight regulation is required to maintain lipid homeostasis. Recent studies have shown key roles for miR-33 and miR-122 in regulation of lipid metabolism, and further evidence implicates miR-370 in regulation of miR-122. In addition, miRNAs involved in adipogenesis (miR-378/378* and miR-27) as well as newly discovered miRNAs such as miR-613, miR-302a, and miR-168 have now been implicated in regulation of lipid metabolism. SUMMARY: Growing evidence support key roles for miRNAs in regulating both cholesterol and fatty acid metabolism, leading to considerable interest in miRNAs as potential drug targets to modulate lipid and lipoprotein metabolism. MiRNA-based therapeutics hold considerable promise in the fight to curtail the growing epidemic of obesity and type 2 diabetes and the associated risk of atherosclerosis.
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) | 0.032 | 0.039 |
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".