Investigation of Gender-Specific Exhaled Breath Volatome in Humans by GCxGC-TOF-MS
Bibliographic record
Abstract
Exploring gender-specific metabolic differences in biofluids provides a basic understanding of the physiological and metabolic phenotype of healthy subjects. Many reports have shown gender-specific metabolome profiles in the urine and serum of healthy subjects; however, limited studies focusing on exhaled human breath are available in the literature. In this study, we profiled the exhaled breath (~450 mL) volatile organic compounds (VOCs) of 47 healthy volunteers (age: 19-47; 23 male (M) and 24 female (F)) using a multidimensional gas chromatography and mass spectrometry and employed chemometric analysis to identify gender-specific VOCs. Eleven exhaled breath VOCs were identified from both uni and multivariate analysis from a training set (M = 15, F = 15) that could differentiate the genders within a healthy population. A partial least-squares discriminate analysis (PLS-DA) model built using these putative markers showed high accuracy in predicting (area under the receiver operating characteristic curve >0.9) a hold out/test sample set (n = 17). The outcomes of this report open up new avenues to undertake larger studies to elucidate the association of exhaled breath metabolites with gender-specific disease phenotypes and pharmacokinetics in the future.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".