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Enregistrement W3148038931 · doi:10.14264/3c9591f

Assessing early-life human exposure to selected pesticides using urinary biomarkers

2021· dissertation· en· W3148038931 sur OpenAlexaboutno aff
Yan Li

Notice bibliographique

RevueThe University of Queensland · 2021
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiquePesticide Exposure and Toxicity
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiomonitoringPesticideUrineAtrazineMetaboliteIngestionToxicologyPopulationEnvironmental chemistryReference doseChemistryEnvironmental healthMedicineRisk assessmentBiologyAgronomyBiochemistry

Résumé

récupéré en direct d'OpenAlex

Pesticides are an important and high use group of chemicals as they increase crop yield in agricultural activities and help control disease spread in public areas. Globally, over 3 billion kilograms of active pesticide ingredients are used per year. The general population may be exposed to these pesticides through dietary ingestion, inhalation, ingestion of dust and soil, and dermal absorption. Pesticide exposure has been linked to numerous adverse health effects, particularly in children who may have additional exposure pathways and metabolise toxicants more slowly and thus are more sensitive to chemical toxicity. Assessing their pesticide exposure and relevant health impact is therefore particularly important. Human biomonitoring using human body fluids is seen as the “gold standard” approach to assess such exposure and risk. Urine is an ideal matrix to investigate recent exposures for currently used pesticides as the majority of them have short half-lives ( 60%) detected in urine samples from Australian and New Zealand children: dialkylphosphate metabolites (DAPs, except for diethyl dithiophosphate (DEDTP)), 3,5,6-trichloro-2-pyridinol (TCPY), para-nitrophenol (PNP), 3-phenoxybenzoic acid, and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid. Urine samples from Australian children also saw a high detection frequency of atrazine mercapturate (ATZ), metabolite of a chlorotriazine herbicide, atrazine. In contrast, detection of ATZ in urine samples from New Zealand children was rare (≤ 2%) but a phenoxyacetic herbicide, 2,4-dichlorophenoxyacetic acid (2,4-D), had a detection frequency of 86%, compared to the Australian study (< 45%). Although comparison between results based on pooled (the Australian study in Chapter 3) and individual (the New Zealand study in Chapter 5) samples should proceed with caution, the above results showed that Australian and New Zealand children share a common exposure of OPs and PYRs but may differ in certain herbicide exposure.Concentrations of DAPs were generally comparable between Australian (0-5 y) and New Zealand children (5-14 y), and were at the lower end compared to other countries. The exception was diethyl thiophosphate (DETP) for Australian pre-schoolers, who had 3 times higher concentration (geometric mean (GM) of 1.8 ng/mL) in urine compared to Japanese peers (~0.6 ng/mL). The other exception was dimethyl phosphate (DMP), for which New Zealand children had a median value of 11 μg/g creatinine, comparable with an existing Australian study on children of the same age but higher than results from other countries such as the US. The profile of DAPs, namely the concentration of ∑DMAP (sum of DMP, dimethyl thiophosphate (DMTP) and dimethyl dithiophosphate (DMDTP)) compared to the one for ∑DEAP (sum of diethyl phosphate (DEP), DETP and DEDTP), were similar between Australia and New Zealand and several other countries such as Canada and Israel. It was however different from some other countries such as Chile and Malaysia, which may reflect the difference in OP use patterns in different countries. The highest level was measured for TCPY, a metabolite for chlorpyrifos, chlorpyrifos-methyl and triclopyr, in both Australian (GM of 9.7 ng/mL urine) and New Zealand (GM of 13 μg/g creatinine) studies. They were generally higher than results from other countries, indicating a prevalent use of and exposure to pesticide products containing the above pesticides in Australia and New Zealand. Although overall exposure risk is low for these pesticides as assessed by calculated daily intake or against Biomonitoring Equivalents, compared to peers in the US, Australian and New Zealand children may have higher exposures to chlorpyrifos/triclopyr and pyrethroids. Biomarker association analysis suggested an important role of permethrin and cypermethrin as sources for pyrethroid exposure for both populations.Age is identified as an important predictor for pesticide exposure but from different directions for children in different ages. Exposure seems to increase following weaning or as a result of increased dietary intake and mobility/activity (0-5 y), and since then, starts decreasing when children are getting older (5-14 y). Girls appear to have elevated concentrations of PNP in urine compared to boys in the 5-14 y group, which may be related to the use of nitrobenzene or PNP itself associated with production of dyes and flavouring agents in cosmetics such as nail polish that girls generally tend to access more than boys.For lifestyle predictors, fruit and vegetable consumptions were found associated with pesticide exposure for children under 3 years but not in the school-age (5-14) group. However, for the school-aged children in New Zealand, urine samples from low spray season had significantly higher concentrations of several biomarkers, which might be associated with consumption of imported fruits and vegetables during this season. Washing fruits and vegetables and hands appear to decrease the exposure to certain pesticides (but not all). Living in rural areas is associated with increased exposure to pyrethroids via pest control practices, whereas children living in urban areas tend to have higher exposure to nitrobenzene or PNP. Having a dog in the home was associated with higher exposure risk to pyrethroids. The exposure risk is further increased for children with a low socioeconomic status.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut 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: Empirique
Score de désaccord entre enseignants0,939
Score d'incertitude au seuil0,666

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,031
Tête enseignante GPT0,252
Écart entre enseignants0,220 · 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 tête enseignante, 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
Publié2021
Routes d'admission1
Résumé présentoui

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Même revueThe University of QueenslandMême sujetPesticide Exposure and ToxicityTravaux en français237 207