Assessing welding fume exposure in professional welders: An exploratory study of biomarkers and metabolomic profiles
Bibliographic record
Abstract
Welding fumes exposure is associated with various detrimental health consequences including cardiopulmonary diseases and cancer. We assessed welding fume exposure using biomonitoring, metalomics, and metabolomics. 38 professional welders (exposed) and 36 power line technicians (non-exposed) were recruited from various facilities in Alberta, Canada. Air sampling and urine collection were conducted. Metal levels were quantified using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Metabolites were quantified using Liquid Chromatography tandem Mass Spectrometry (LC-MS-MS). Receiver operating characteristic (ROC) curve analysis and linear mixed models (LMM) were performed using STATA 19.0 and MetaboAnalyst 5.0. Short-term and cumulative doses were calculated using air sampling data in the modified Environmental Protection Agency (EPA) method. Elevated urinary levels of As, Cr, Fe, Mn, and Ni were observed in welders than in the non-exposed group (p˂0.05). Among the metabolites, beta-hydroxybutyric acid, arginine, asparagine, choline, and ornithine were proposed as potential biomarkers for welding fume exposure (AUC>0.7). ROC results identified metabolites associated with welding experience and smoking. LMM identified smoking as the main predictor of urinary Fe, Mn, and V, while short-term Cr and Sb doses predicted their urinary levels; welders' urinary metabolites were mainly influenced by welding experience and smoking. Our study highlights the potential benefits of biomonitoring and metabolomics to assess the health effects of welding fume exposure. However, relatively small sample size and lacking biomarkers exploration by sex limit the generalizability of findings. Further investigation is recommended to explore the underlying mechanisms and the effects of other factors on the metabolomics profile in professional welders. • Welding fume exposure was assessed using biomarkers of exposure and metabolomics. • Ni and Mn proposed as biomarkers of exposure associated with welding experience. • Positive correlations were found between particle cumulative dose and metabolites. • Welding experience and smoking predicted levels of urinary metals and metabolites. • Urinary metabolites may help early detection of welding fume-related health risks.
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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 | 0.001 |
| 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 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".