Diving beetle–like miniaturized plungers with reversible, rapid biofluid capturing for machine learning–based care of skin disease
Bibliographic record
Abstract
Recent advances in bioinspired nano/microstructures have received attention as promising approaches with which to implement smart skin-interfacial devices for personalized health care. In situ skin diagnosis requires adaptable skin adherence and rapid capture of clinical biofluids. Here, we report a simple, all-in-one device consisting of microplungers and hydrogels that can rapidly capture biofluids and conformally attach to skin for stable, real-time monitoring of health. Inspired by the male diving beetle, the microplungers achieve repeatable, enhanced, and multidirectional adhesion to human skin in dry/wet environments, revealing the role of the cavities in these architectures. The hydrogels within the microplungers instantaneously absorb liquids from the epidermis for enhanced adhesiveness and reversibly change color for visual indication of skin pH levels. To realize advanced biomedical technologies for the diagnosis and treatment of skin, our suction-mediated device is integrated with a machine learning framework for accurate and automated colorimetric analysis of pH levels.
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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) | 0.001 | 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".