Medical Swab Analysis Using Desorption Electrospray Ionization Mass Spectrometry: A Noninvasive Approach for Mucosal Diagnostics
Bibliographic record
Abstract
Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab-inlet geometry, tip-sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".