Gut-on-a-Chip-Based Real-Time miRNA-21 Monitoring and Anti-Inflammatory Drug Evaluation
Bibliographic record
Abstract
MiRNA-21 is a crucial biomarker involved in inflammatory pathways and is linked to gastrointestinal diseases like inflammatory bowel disease (IBD). Its dynamic expression reflects disease progression and treatment response, making it an attractive target for diagnostic and therapeutic applications. However, current in vitro models often lack the physiological relevance needed for effective biomarker monitoring, limiting their utility in drug screening and therapeutic evaluation. In this study, we developed an advanced gut-on-a-chip (GOC) platform integrated with an electrochemical biosensor to achieve high-sensitivity detection of miRNA-21. The chip replicates key aspects of the intestinal microenvironment, including dynamic medium perfusion and mechanical stretching, which support the formation of a functional intestinal barrier using Caco-2 cells. The integrated biosensor demonstrated excellent performance, with a wide linear range from 1 × 10 –15 to 1 × 10 –10 M, enabling precise monitoring of miRNA-21 expression. To demonstrate its utility, we established an in vitro inflammation model by introducing pro-inflammatory stimuli and monitored miRNA-21 levels dynamically. The platform successfully captured the correlation between miRNA-21 expression and inflammatory progression. Furthermore, we used the system to evaluate the effects of anti-inflammatory drugs, providing proof-of-concept for its application in drug screening.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".