Rapid miRNA detection in skin interstitial fluid using a hydrogel microneedle patch integrated with DNA probes and graphene oxide
Bibliographic record
Abstract
MicroRNA (miRNA) is a type of short, non-coding nucleic acid molecule that plays essential roles in diagnosing and prognosing various types of cancer. MiRNA is abundantly present in skin interstitial fluid (ISF), providing real-time and localized physiological information. Hydrogel microneedle (HMN) patches enable miRNA collection in a fast, pain-free, minimally invasive, and user-friendly manner. In this study, we introduced a fluorescence-based HMN assay, namely the HMN-miR sensor, composed of methacrylated hyaluronic acid (MeHA) and a graphene oxide-probe DNA (GO.pDNA) conjugate for miR21 and miR210 detection. The HMN-miR sensor demonstrates excellent skin penetration efficiency, rapid ISF collection capability, and sufficient miRNA detection and sequence identification specificity. The HMN-miR sensor facilitates a new assay that, with further optimization, could be applied in future clinical settings. Its simple fabrication process and excellent biocompatibility give it significant potential for various clinical uses, such as personalized cancer treatment and monitoring the healing progress of burn wounds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".