Circulating miRNA-16 in inflammatory bowel disease and some clinical correlations - a cohort study in Bulgarian patients.
Bibliographic record
Abstract
OBJECTIVE: The pathogenesis of the inflammatory bowel disease (IBD) includes chronic inflammation and altered immune reactions. There are several publications, reporting that micro ribonucleic acids (miRNAs) may serve as a diagnostic biomarker with a potential to assess inflammation severity and treatment response1,2 in IBD patients. The objective of the study is to assess and correlate the serum expression of circulating miR-16 in IBD patients with some clinical parameters, such as extent, activity and severity of the disease. PATIENTS AND METHODS: 70 IBD patients [35 with ulcerative colitis (UC) and 35 with Crohn's disease (CD)] were included in the study. Serum miR-16 expression in both IBD diseases was assessed using reverse transcription quantitative real time PCR (RT-qPCR). Circulating miR-16 levels were also correlated with disease extent, activity and severity indices [Crohn's Disease Activity Index (CDAI), Montreal classification, Partial Mayo score]. Serum expression of miR-16 in the 70 patients was also compared to miR-16 serum levels in 30 healthy control subjects. RESULTS: The patients' group showed mean serum miR-16 expression of 3.07 for CD, 1.97 for UC and 1.61 for the control group of healthy subjects with a significant difference in the expression between groups. There is a significant correlation between increased serum expression of miR-16 and disease activity, extent and severity. CONCLUSIONS: The increased miR-16 serum expression correlates with disease activity, intestinal localization of CD, stenotic and penetrating phenotype. MiR-16 could serve as a potential biomarker to assess inflammation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".