Human urinary mutagenicity after wood smoke exposure during traditional temazcal use
Bibliographic record
Abstract
In Central America, the traditional temazcales or wood-fired steam baths, commonly used by many Native American populations, are often heated by wood fires with little ventilation, and this use results in high wood smoke exposure. Urinary mutagenicity has been previously employed as a non-invasive biomarker of human exposure to combustion emissions. This study examined the urinary mutagenicity in 19 indigenous Mayan families from the highlands of Guatemala who regularly use temazcales (N = 32), as well as control (unexposed) individuals from the same population (N = 9). Urine samples collected before and after temazcal exposure were enzymatically deconjugated and extracted using solid-phase extraction. The creatinine-adjusted mutagenic potency of urine extracts was assessed using the plate-incorporation version of the Salmonella mutagenicity assay with strain YG1041 in the presence of exogenous metabolic activation. The post-exposure mutagenic potency of urine extracts were, on average, 1.7-fold higher than pre-exposure samples (P < 0.005) and also significantly more mutagenic than the control samples (P < 0.05). Exhaled carbon monoxide (CO) was ~10 times higher following temazcal use (P < 0.0001), and both CO level and time spent in temazcal were positively associated with urinary mutagenic potency (i.e. P < 0.0001 and P = 0.01, respectively). Thus, the wood smoke exposure associated with temazcal use contributes to increased excretion of conjugated mutagenic metabolites. Moreover, urinary mutagenic potency is correlated with other metrics of exposure (i.e. exhaled CO, duration of exposure). Since urinary mutagenicity is a biomarker associated with genetic damage, temazcal use may therefore be expected to contribute to an increased risk of DNA damage and mutation, effects associated with the initiation of cancer.
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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 | 0.001 | 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".