A Naked Eye‐Invisible Ratiometric Fluorescent Microneedle Tattoo for Real‐Time Monitoring of Inflammatory Skin Conditions
Bibliographic record
Abstract
The field of portable healthcare monitoring devices has an urgent need for the development of real-time, noninvasive sensing and detection methods for various physiological analytes. Currently, transdermal sensing techniques are severely limited in scope (i.e., measurement of heart rate or sweat composition), or else tend to be invasive, often needing to be performed in a clinical setting. This study proposes a minimally invasive alternative strategy, consisting of using dissolving polymeric microneedles to deliver naked eye-invisible functional fluorescent ratiometric microneedle tattoos directly to the skin for real-time monitoring and quantification of physiological and pathological parameters. Reactive oxygen species are overexpressed in the skin in association with various pathological conditions. Here, one demonstrates for the first time the microneedle-based delivery to the skin of active fluorescent sensors in the form of an invisible, ratiometric microneedle tattoo capable of sensing reactive oxygen species in a reconstructed human-based skin disease model, as well as an in vivo model of UV-induced dermal inflammation. One also elaborates a universal ratiometric quantification concept coupled with a custom-built, multiwavelength portable fluorescence detection system. Fully realized, this approach presents an opportunity for the minimally invasive monitoring of a broad range of physiological parameters through the skin.
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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".