Profiling of circulating microRNAs in children with recent onset of type 1 diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) is an autoimmune disease that is clinically silent until the majority of β cells are destroyed. There is an unmet need for reliable and cost-effective biomarkers to predict and diagnose diabetes at an early stage. A number of stable microRNAs (miRNAs) have been reported in serum and plasma and are now being investigated as biomarkers of different diseases. We measured the levels of 745 miRNAs in sera of children with recent-onset T1D and age-matched controls using locked nucleic acid-enhanced (LNA-enhanced) quantitative PCR profiling. Thirty-five miRNAs were significantly different between the groups, and 27 miRNAs were elevated in T1D. Good discriminating power was obtained for 6 miRNAs (miR-454-3p, miR-222-3p, miR-144-5p, miR-345-5p, miR-24-3p, and miR-140-5p), which were not elevated at later stages of diabetes. In silico pathway analysis, based on inferred miRNA target genes, associated glycosaminoglycan biosynthesis as well as PI3K/Akt, MAPK, and Wnt signaling pathways with early stages of T1D. Among the 27 upregulated miRNAs in T1D, 2 miRNAs significantly correlated with hemoglobin A1c (HbA1c), as did 5 of 8 downregulated miRNAs. A total of 134 miRNAs significantly correlated with HbA1c when stratifying hyperglycemia-induced miRNAs from T1D-specific miRNAs. In conclusion, we have identified a serum miRNA pattern of recent-onset T1D and signaling pathways that may be involved in its pathogenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".