259 Investigating welding fume exposure in professional welders using biomonitoring and metabolomics approaches
Bibliographic record
Abstract
Abstract We aimed to investigate the health effects attributed to welding fume exposure in professional welders using biomonitoring and metabolomics approaches. The cohort included 38 welders as the case group and 36 electricians as the control group, recruited from various facilities across the province of Alberta, Canada. Air sampling was performed throughout the work shift. Fasting urine samples were collected from the welders and controls. The identification of metals and metabolites in the samples was performed using Inductively Coupled Plasma Mass Spectrometry (ICP/MS) and Liquid Chromatography with Tandem Mass Spectrometry (LC-MS-MS), respectively. Data analyses were conducted using STATA version 19.0 and MetaboAnalyst version 5.0. Significantly higher levels of As, Cd, Cr, Fe, Mn, and Ni were observed in the urine of welders compared to controls (p < 0.05). Regarding the metabolomics profile, we identified 92 metabolites in the urine samples of the studied groups. Multivariate receiver operating characteristic (ROC) curve analysis identified metabolites, such as beta-hydroxybutyric acid, asparagine, ornithine, choline, propionic acid, and histidine, as biomarkers for welding fume exposure (AUC > 0.7). These metabolites exhibited significantly higher levels in welders than in controls. In addition, our findings suggest that smoking, welding experience, respirator usage, and age significantly affect the concentrations of certain urinary metabolites in professional welders. Our study highlights the potential benefits of using biomonitoring and metabolomics approaches to investigate the adverse health effects of welding fume exposure. However, we recommend further investigations to explore the underlying mechanisms of these associations and the effects of other factors. Keywords: Biomonitoring, Exposure assessment, Welding fumes, Metabolomics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".