Efficient sweat induction and pilocarpine delivery on the forearm but not the palm using coated microneedles in humans
Bibliographic record
Abstract
• Pilocarpine-coated microneedles were developed for sweat induction in humans. • Efficacy was validated against iontophoresis on the forearm. • High test-retest reliability was confirmed on the forearm. • Effectiveness and reliability were reduced in thick skin regions like the palm. • A promising method to induce sweat for evaluating sweating function. The modulation of sweating via pharmacologic manipulation is widely employed in research, diagnostics, and therapeutics of the sweating response. However, its application requires a user-friendly drug delivery system that can be applied to various skin regions with varying permeability. In this study, we evaluated the effectiveness of a new pilocarpine-coated microneedle array for sweat induction on the forearm (non-glabrous, thin skin) and palm (glabrous, thick skin) in healthy young adults. The pilocarpine-coated microneedle arrays induced sweating on the forearm, which was highly consistent with responses observed using the iontophoretic application of pilocarpine. Further, it offered superior controlled delivery capabilities along with excellent reproducibility. However, its effectiveness was reduced on the palm, highlighting the possible roles of mechanical, anatomical, and physiological properties in microneedle-mediated drug delivery. Altogether, we show that pilocarpine-coated microneedle arrays is an effective method to induce sweating in thin, non-glabrous 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 | 0.000 | 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".