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Enregistrement W7106019093 · doi:10.7939/83324

Assessing Welding Fume Exposure Among Professional Welders: Exploring Biomarkers of Exposure and Markers of Health Effects

2025· dissertation· en· W7106019093 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2025
Typedissertation
Langueen
DomaineMedicine
ThématiqueOccupational exposure and asthma
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésConfoundingOccupational exposureMalondialdehydeWeldingOxidative stressDNA damageExposure assessmentBiomonitoring

Résumé

récupéré en direct d'OpenAlex

Welding is widely used in various industrial settings. Exposure to welding fumes is associated with various health conditions, such as cardiopulmonary diseases and cancer. This dissertation comprises four studies that collectively assess exposure to welding fumes among professional welders using an integrative biomonitoring and metabolomics approach. Study 1 aimed to synthesize the existing evidence on the associations between welding fume exposure and changes in oxidative stress [superoxide dismutase (SOD) and malondialdehyde (MDA)] and DNA damage [8-hydroxy-2′-deoxyguanosine (8-OHdG) and DNA-protein crosslink (DPC)] markers in professional welders. A thorough search was performed in Embase, Web of Science, Scopus, Medline, and CINAHL, supplemented by grey literature. Data were analyzed using narrative synthesis and random-effects meta-analysis. Of the 450 retrieved studies, 14 met the inclusion criteria and were included in the review. Meta-analyses showed significant differences in the 8-OHdG (MD = 9.38; 95% CI, 0.55–18.21) and DPC (MD = 1.07; 95% CI, 0.14–2) levels between welders and controls. However, no significant difference was observed in MDA levels (MD = 0.26; 95% CI, -0.03, 0.55) between the groups, whereas narrative synthesis showed an inconsistent trend in SOD levels. The included studies had a high risk of exclusion and confounding biases. The results suggest an association between welding fume exposure and DNA damage in professional welders, although the evidence is limited. Further studies are warranted to evaluate the potential of other biomarkers for assessing oxidative stress and DNA damage in welders and the underlying mechanisms. Study 2 aimed to evaluate the field effectiveness of respirators against metal particle exposure through a comprehensive systematic review of major bibliographic databases and grey literature sources. Of the 463 references, 70 underwent full-text screening, and eight papers satisfied the inclusion criteria and were included in the review. Studies have reported significant differences in metal particle levels between workers who wore respirators and those who did not (p˂0.05). We also found that N95 respirators provided significantly less protection than elastomeric and powered air-purifying respirators (p˂0.001). The results underscore the need to implement more field studies, including biomonitoring and metabolomics approaches, to better assess the protective role of different types of respirators in protecting welders from the detrimental health effects of exposure to welding fumes. Study 3 aimed to assess exposure to welding fumes in professional welders using biomarkers of exposure and metabolomics. 38 welders and 36 power line technicians as the non-exposed group were recruited from various facilities across the province of Alberta, Canada. Air sampling was performed throughout the shift. Fasting urine samples were collected from the participants the day after the air sampling. Metals and metabolites in air and urine samples were quantified using Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Liquid Chromatography with tandem Mass Spectrometry (LC-MS-MS), respectively. Welders exhibited significantly higher urinary levels of As, Cr, Fe, Mn, and Ni compared to the non-exposed group (p˂0.05). A receiver operating characteristic (ROC) curve analysis showed that Mn and Ni could be potential biomarkers for welding fume exposure (AUC > 0.7). Metabolomics analysis identified urinary beta-hydroxybutyric acid, arginine, asparagine, choline, ornithine higher in welders compared to the non-exposed group (AUC > 0.7). The ROC analysis also identified urinary metabolites associated with welding experience and smoking in welders. The linear mixed models (LMMs) results identified welding experience and smoking as the main predictors of urinary metals and metabolites in welders. The results underscore potential of using biomonitoring and metabolomics for assessing welding fume exposure, suggesting further research into underlying mechanisms. Study 4 aimed to investigate the potential of using metals in the exhaled breath condensate (EBC) as biomarkers of welding fume exposure. The cohort included 33 welders and 30 non-exposed participants. Air sampling was conducted throughout shifts in the welder group. EBC sampling was conducted using R-Tubes. Metals in the collected samples were quantified using ICP-MS. Post-shift levels of Al, Co, Cu, Mn, Fe, Ni, Pb, and Zn in the welders' EBC were significantly higher than those in the non-exposed group (p<0.05). Among welders, V levels were consistently higher post-shift than pre-shift, while Pb levels dropped, especially in smokers. LMMs identified smoking as the main predictor for pre-shift metal levels, while welding experience and exposure to high levels of welding fumes drove post-shift metal levels in EBC. Keywords: Biomarker, Exposure, Welding Fumes, Oxidative Stress, Metabolomics.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,034
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,053

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,034
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0050,010
Bibliométrie0,0080,006
Études des sciences et des technologies0,0000,001
Communication savante0,0030,002
Science ouverte0,0010,001
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,249
Écart entre enseignants0,233 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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