Remote analysis and management of sweat biomarkers using a wearable microfluidic sticker in adult cystic fibrosis patients
Bibliographic record
Abstract
Sweat parameters such as volume and chloride concentration may offer invaluable clinical insights for people with CF (PwCF). Pilocarpine-induced sweat collection for chloridometry measurement is the gold standard for a CF diagnosis, but this technique is cumbersome and not suitable for remote settings or repeat measurements. We have previously reported the utility of a skin-interfaced microfluidic device (CF Patch) in conjunction with a smartphone image processing platform that enables real-time measurement of sweating rates and sodium chloride loss in laboratory and remote settings. Here, we conducted clinical studies assessing the accuracy of the CF Patch compared to chloridometry when using pilocarpine to induce sweat. We also tested the feasibility and accuracy of exercise-induced sweat chloride measurements in PwCF and healthy volunteers (HV). In the laboratory, using either pilocarpine or exercise to induce sweat, the CF Patch demonstrated strong correlations with sweat chloride measured by pilocarpine-induced chloridometry. In remote settings, exercise-induced sweat chlorides measured using the CF patch were strongly correlated with in-laboratory exercise-induced CF patch sweat chlorides in HV but had a weaker correlation in PwCF. For PwCF on CFTR modulators, there was greater day-to-day variability in sweat chloride compared to HV, which highlights the limitations of assessing CFTR modulator efficacy and pharmacodynamics based on a single in-laboratory chloridometry measurement. Moreover, these findings demonstrate that the CF Patch is suitable as a remote management device capable of measuring serial sweat chloride concentrations and offers the potential of monitoring the efficacy of CF medication regimens but should not replace pilocarpine-based chloridometry for making a CF diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".