The effects of expression of different microRNAs on insulin secretion and diabetic nephropathy progression
Bibliographic record
Abstract
MicroRNAs (miRNAs) have recently become well-known efficacious biomarkers for the diagnosis of diabetic nephropathy (DN). MiRNAs, short noncoding RNAs, are posttranscriptional regulators of gene expression, which regulate several biological cell functions, including insulin production and secretion, as well as insulin resistance in tissues. Today, the focus of the medical world is centered on the role of miRNAs as mediators for different diseases, such as DN and end-stage renal diseases (ESRD). MiRNAs are stable and detectable in human biological fluids, so their detection for early diagnosis of different diseases is highly sensitive and specific. Previous reports have shown that the alteration of miRNA profiles significantly correlates with specific stages of DN, kidney fibrosis, and renal dysfunction. This review was aimed at assessing the pathway of different miRNA expressions responsible for insulin secretion disorder and DN progression.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".