Downregulation of <i>miR-25-3p</i> and Its Impact on <i>PTAFR</i> and <i>IGF2BP3</i> Expression in Type 2 Diabetes Mellitus: Implications for Biomarker Discovery and Disease Pathogenesis
Bibliographic record
Abstract
Background: This study is designed to investigate the differential microRNA (miRNA) expression profiles in individuals with and without type 2 diabetes mellitus (T2DM). The focus is on miRNAs that play a crucial role in the onset and progression of T2DM, particularly in glucose metabolism, inflammation, platelet reactivity, and endothelial dysfunction. Methods: Twenty samples were categorized into groups of T2DM and non-T2DM, and miRNA profiling was conducted using microarray analysis. The expression levels of the candidate miR-25-3p, as well as its target genes platelet-activating factor receptor (PTAFR) and insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3), were validated using quantitative polymerase chain reaction (qPCR). Results: The present study revealed a significant reduction in the level of miR-25-3p in the T2DM group compared to the non-T2DM group. This suggests higher levels of PTAFR and IGF2BP3 in individuals with T2DM, indicating a potential biomarker for the condition. Conclusions: The downregulation of miR-25-3p, which is associated with increased PTAFR levels, may contribute to heightened platelet reactivity and inflammation, worsening endothelial dysfunction, and potentially influencing vascular complications in diabetes. Additionally, the upregulation of IGF2BP3 is correlated with insulin resistance and β-cell dysfunction, which may contribute to elevated hyperglycemia and hyperinsulinemia, further aggravating the progression of diabetes. These findings highlight the potential of miR-25-3p and IGF2BP3 as biomarkers for T2DM and suggest their possible relevance for improving diagnosis and treatment strategies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".