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Antibiotic Resistance Genes in Bioaerosols from Saskatchewan Livestock Operations

2025· article· en· W6979966993 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2025
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueIndoor Air Quality and Microbial Exposure
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIndoor bioaerosolAntibiotic resistanceLivestockBioaerosolAntibioticsResistomeBacteria
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Antibiotic resistance is increasing, and infections with resistant microorganisms that are typically related to hospital settings are now being seen away from hospital settings. Therefore, there is a pressing need to understand the factors that contribute to the rise of antibiotic resistance. It is known that the use of antibiotics, the presence of residuals of antibiotics, high microbial density, and human activity are potential contributing sources of antibiotic resistant microorganisms. Animal operations, such as swine and poultry, are a source of antibiotic resistant bacteria. Bacteria can acquire resistance through transformation, which is the ability of the bacteria to assimilate DNA, including Antibiotic Resistance Genes, from the environment under specific conditions. Although the conditions are to be determined, Antibiotic Resistance Genes (ARG) are a piece of the puzzle to understand acquired resistance. In the environment, bioaerosols act as carriers of ARG and can be collected at the fan exhaust of animal operations. Despite ARG being detected in animal livestock operations, it is unclear the role of Saskatchewan operations in the emission and spread of bioaerosols with ARG and its overall contribution to the antimicrobial resistance emergency. We aimed to determine the role of Saskatchewan livestock operations in antibiotic resistant genes from bioaerosol emissions and spreading. The specific objectives were I) identify ARG in bioaerosols emitted from Saskatchewan swine and poultry operations; II) evaluate the abundance of ARG in bioaerosols emitted from Saskatchewan swine and poultry operations; and III) evaluate the dispersion of ARG in bioaerosols emitted from livestock operations. Two commercial swine and poultry producer barns were visited. Both swine producers managed three housing systems: finishing, gestation in stalls, and gestation in groups. Bioaerosols were collected at the fan exhaust (n=18) and 10 meters (n=15), 100 meters (n=15), and 1 kilometer (n=15) in front of the fan exhaust of swine finishing and poultry facilities, and at the fan exhaust of swine gestation in stalls (n=19) and swine gestation in groups (n=24), using a high-volume sampler with an electret filter attached to it. DNA was extracted and assessed for bacterial DNA and ARG relative abundance. In parallel to the collection of bioaerosols, the concentration of particulate matter and its fractions were measured in real-time. Overall, poultry facilities emitted more inhalable particulate matter compared with swine facilities. Regarding 16S relative abundance, significant differences were seen between poultry and both swine gestation housing types. Swine finishing and poultry operations had the highest 16S relative abundance. ARG related to quinolone, tetracycline, macrolide, iii sulfonamide, beta-lactam, vancomycin, and mobile genetic elements (MGE) were found at the exhaust of all samples at different relative abundances. Particularly, in swine finishing operations, quinolone resistance represented 34.7% of genes, whereas in swine gestation using group housing 43.25% of the genes were associated with tetracycline resistance. In swine gestation with stall housing, both tetracycline (25.38%) and beta-lactam (28.01%) resistance genes were predominant. Macrolide resistance genes accounted for 42.50% of the genes while 30.57% represented quinolone resistance in poultry operations. Many of the genes linked to tetracycline resistance were present in all facilities, and their abundance varied depending on the housing operation. Notably, tetracycline resistance genes were more diverse and abundant in swine operations where oxytetracycline was used to fight infections. Quinolone and macrolide resistance genes were detected but none of the facilities visited used quinolones or macrolides as treatment for infections. Vancomycin resistance genes were detected in some barns and in low concentrations. In poultry facilities, Bacitracin Methylene Disalicylate (BDM ®) was the only antibiotic used, and it was administered through feed and as a prophylactic measure. At swine finishing and poultry operations, a dilution pattern was observed in the abundance of 16S and ARG as the distance in front of the fan exhaust increased. However, the abundance of macrolide and beta-lactam resistance genes increased when the distance in front of the fan exhaust increased. The number of genes detected after 10 meters decreased compared to the number of genes at the fan exhaust. Mobile genetic elements were not detected beyond the fan exhaust. Livestock operations contribute to the deposition of ARG in the environment; however, it is not the only contributor. Also, after 10 meters in front of the fan exhaust the concentration and diversity of genes are low. Bioaerosols contain essential information about the microbial dynamics in the environment and can be used as tool for controlling and monitoring of prevalence of ARG. ARG related to antibiotics that have not been used in animal facilities can be found in bioaerosols at the fan exhaust. The abundance of ARG was higher in swine operations than in poultry operations.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
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: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,593
Score d'incertitude au seuil0,809

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,006
Tête enseignante GPT0,170
Écart entre enseignants0,164 · 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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