Fecal miR‐223 is a noninvasive biomarker for estimating Crohn's disease activity
Bibliographic record
Abstract
INTRODUCTION: MicroRNA-223 (miR-223) has emerged as a promising noninvasive biomarker for Crohn's disease (CD). However, it is unclear which tissue derived miRNA-223 can more accurately estimate CD disease activity. MATERIALS AND METHODS: To collect serum, terminal ileal mucosa biopsy and fecal samples from CD patients and healthy controls. The CD Activity Index (CDAI) score, Montreal classification, maintenance medicines, peripheral blood inflammatory markers, fecal calprotectin (FC) and the Simple Endoscopic Score for CD (SES-CD) were recorded. To compare the expression of miR-223 in the serum, intestinal tissue, and feces. RESULTS: MiR-223 expression levels in the serum, intestinal tissue and feces of CD patients were significantly higher than those of controls. The level of miR-223 in the serum, intestinal tissue and feces increased significantly in active CD patients compared with that in inactive CD patients. The levels of serum, intestinal tissue and fecal miR-223 were correlated with the CDAI. Serum miR-223 was also correlated with C-reactive protein (CRP) and IL-6, tissue miR-223 correlated with IL-6 and FC, and fecal miR-223 correlated with FC. In terms of the association with FC, fecal miR-223 had a higher Spearman r value than tissue miR-223. The area under the curve (AUC) values of serum, tissue and fecal miR-223 to diagnose CD were similar to those of CRP and FC (AUC > 0.8). The AUC values of tissue and fecal miR-223 to evaluate CD disease activity were 0.832 and 0.818, respectively, and were higher than serum miR-223, CRP and FC. Fecal miR-223 had a higher specificity of 92.3%. CONCLUSIONS: Fecal miR-223 might be a novel, noninvasive biomarker for estimating the disease activity of CD patients.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".