MicroRNAs in the pathogenesis of type 2 diabetes: new research progress and future direction
Bibliographic record
Abstract
miRNA is a short non-coding RNA that can influence mRNA processing at the post-transcriptional level. A large number of miRNAs have been found in virtually all species so far, and these small molecules play an important role in many different physiological processes and various pathologic conditions, such as cell metabolism, cancer, autoimmune disease, and diabetes mellitus. T2D arises from a dysregulated response to the elevated glucose level in the circulation. The prevalence of T2D has increased dramatically in all age groups, and T2D in older adults is associated with more T2D complications and higher mortality. Despite the existing findings describing the pathological mechanism, T2D pathology is more complex and the pathophysiology of the disease is still not fully elucidated. In this review, we summarize the current understanding of miRNA-mediated modulation of gene expression in T2D pathogenesis, as well as related signaling pathways, and insight into the important role of miRNA in various T2D complications. Furthermore, the potential therapeutic value of miRNA for T2D patients is also discussed in detail.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".