Hydrogel Micropatches for Sampling and Profiling Skin Metabolites
Bibliographic record
Abstract
Metabolites excreted by skin have a huge potential as disease biomarkers. However, due to the shortage of convenient sampling/analysis methods, the analysis of sweat has not become very popular in the clinical setting (pilocarpine iontophoresis being a prominent exception). In this report, a facile method for sampling and rapid chemical profiling of skin metabolites excreted with sweat is proposed. Metabolites released by skin (primarily the constituents of sweat) are collected into hydrogel (agarose) micropatches. Subsequently, they are extracted in an online analytical setup incorporating nanospray desorption electrospray ionization and an ion trap mass spectrometer. In a series of reference measurements, using bulk sampling and electrospray ionization mass spectrometry, various low-molecular-weight metabolites are detected in the micropatches exposed to skin. The sampling time is as short as 10 min, while the desorption time is 2 min. Technical precision of micropatch analysis varies within the range of 3-42%, depending on the sample and the method of data treatment; the best technical precision (≤10%) has been achieved while using an isotopically labeled internal standard. The limits of detection range from 7 to 278 pmol. Differences in the quantities of extracted metabolites are observed for the samples obtained from healthy individuals (intersubject variabilities: 30-89%; n = 9), which suggests that this method may have the potential to become a semiquantitative assay in clinical analysis and forensics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".