Hydrogen sulphide in human nasal air quantified using thermal desorption and selected ion flow tube mass spectrometry
Bibliographic record
Abstract
The discovery that hydrogen sulphide (H2S) acts as a gasotransmitter when present at very low concentrations (sub-parts per billion (ppbv)) has resulted in the need to quickly quantify trace amounts of the gas in complex biological samples. Selected ion flow tube mass spectrometry (SIFT-MS) is capable of real-time quantification of H2S but many SIFT-MS instruments lack sufficient sensitivity for this application. In this study we investigate the utility of combining thermal desorption with SIFT-MS for quantifying H2S in the 0.1-1 ppbv concentration range. Human orally or nasally derived breath, and background ambient air, were collected in sampling bags and dried by passing through CaCl2 and H2S pre-concentrated using a sorbent trap optimised for the capture of this gas. The absorbed H2S was then thermally desorbed and quantified by SIFT-MS. H2S concentrations in ambient air, nasal breath and oral breath collected from 10 healthy volunteers were 0.12 ± 0.02 (mean ± SD), 0.40 ± 0.11 and 3.1 ± 2.5 ppbv respectively, and in the oral cavity H2S, quantified by SIFT-MS without pre-concentration, was present at 13.5 ± 8.6 ppbv. The oral cavity H2S correlates well with oral breath H2S but not with nasal breath H2S, suggesting that oral breath H2S derives mainly from the oral cavity but nasal breath is likely pulmonary in origin. The successful quantification of such low concentrations of H2S in nasal air using a rapid analytical procedure paves the way for the straightforward analysis of H2S in breath and may assist in elucidating the role that H2S plays in biological systems.
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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".