3D Printed Hydrogel Microneedle Arrays for Interstitial Fluid Biomarker Extraction and Colorimetric Detection
Bibliographic record
Abstract
To treat and manage chronic diseases, it is necessary to continuously monitor relevant biomarkers and modify treatment as the disease state changes. Compared to other bodily fluids, interstitial skin fluid (ISF) is a good choice for identifying biomarkers because it has a molecular composition most similar to blood plasma. Herein, a microneedle array (MNA) is presented to extract ISF painlessly and bloodlessly. The MNA is made of crosslinked poly(ethylene glycol) diacrylate (PEGDA), and an optimal balance of mechanical properties and absorption capability is suggested. Besides, the effect of needles' cross-section shape on skin penetration is studied. The MNA is integrated with a multiplexed sensor that provides a color change in a biomarker concentration-dependent manner based on the relevant reactions for colorimetric detection of pH and glucose biomarkers. The developed device enables diagnosis by visual inspection or quantitative red, green, and blue (RGB) analysis. The outcomes of this study show that MNA can successfully identify biomarkers in interstitial skin fluid in a matter of minutes. The home-based long-term monitoring and management of metabolic diseases will benefit from such practical and self-administrable biomarker detection.
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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 | 0.001 | 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".