Sweat gland bioelectrics differ in cystic fibrosis: a new concept for potential diagnosis and assessment of CFTR function in cystic fibrosis
Bibliographic record
Abstract
BACKGROUND: For nearly 50 years the diagnosis of cystic fibrosis (CF) has depended on measurements of sweat chloride concentration. While the validity of this test is universally accepted, increasing diagnostic challenges and the search for adequate biomarker assays to support curative-orientated clinical drug trials have created a new demand for accurate, reliable and more practical CF tests. A novel concept is proposed that may provide a more efficient real-time method for assessing CFTR function in vivo. METHODS: Cholinergic and beta-adrenergic agonists were iontophoresed to stimulate sweating. The bioelectric potential from stimulated sweat glands (SPD) was measured in vivo using a standard ECG electrode applied to the skin surface. SPD and sweat chloride concentrations were compared in cohorts predicted to express a range of CFTR function as presented by healthy controls (HC), heterozygotes (Hz), pancreatic sufficient (CFPS) and pancreatic insufficient patients with CF (CFPI). RESULTS: The median SPD was hyperpolarized in patients with CF compared with control subjects (-47.4 mV vs -14.5 mV, p<0.001). In distinguishing between control and CF subjects, SPD (area under receiver operator curve (AUC) = 0.997) was similar to sweat chloride concentration (AUC = 0.986). Sequential cholinergic/beta-adrenergic sweat stimulation dramatically depolarised the SPD in patients with CF (p<0.001) but had no effect in control subjects (p = 0.6) or on the sweat chloride concentration in either group (p>0.5). Furthermore, the positive SPD response was larger in CFPI than in CFPS subjects (p = 0.04). CONCLUSION: These results support the concept that skin surface voltages arising from stimulated sweat glands can be exploited to assess expressed CFTR function in vivo and may prove to be a useful diagnostic tool.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".