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Enregistrement W184209616

Special Issue — Antimicrobial Resistance

2008· article· en· W184209616 sur OpenAlexaboutno aff
Scott A. McEwen, Patrick Boerlin, Andrijana Rajić, Richard J. Reid‐Smith

Notice bibliographique

RevuePubMed Central · 2008
Typearticle
Langueen
DomaineEnvironmental Science
ThématiquePharmaceutical and Antibiotic Environmental Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAntibiotic resistanceCampylobacterAntimicrobialDrug resistancePublic healthOne HealthSalmonellaEnvironmental healthMedicineBiologyMicrobiologyAntibioticsBacteria
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Antimicrobial resistance (AMR) threatens our ability to treat bacterial, parasitic, and viral infections in humans and animals. The threat is especially apparent for certain bacterial infections of humans, such as methicillin-resistant Staphylococcus aureus, multi-drug resistant Mycobacterium tuberculosis, and Streptococcus pneumoniae, but there are also concerns about resistance among foodborne zoonotic pathogens, such as Salmonella and Campylobacter. Over the last decade the scientific community has witnessed much discussion and debate about the possible role of antimicrobial use (AMU) in animals in selection of resistance in bacteria of human health concern, and what, if anything, should be done about it. Regrettably, not as much attention has been given to the impacts of resistance on animal health and welfare. A common theme that has emerged from numerous expert and stakeholder reports on AMR published in the last decade is the urgent need for coordinated research and for the establishment or enhancement of monitoring systems for AMR and AMU in different populations that would hopefully result in better understanding of the extent and impacts of resistance problems in animals, humans, and the environment. Other recommendations included better insights into the role of AMU and other factors in the emergence and dissemination of resistance determinants, and ways to better protect the efficacy of existing antimicrobials, including improved infectious disease control methods. Many animal and public health organizations in both government and industry have responded to these recommendations. Since 2003, the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS) has provided important monitoring data on antimicrobial susceptibility patterns in enteric bacteria (for example, Salmonella) from humans, animals, and food in Canada, as well as AMU data in humans, and hopefully soon, in animals. Data from CIPARS and similar programs in the United States and other countries are being used to inform and evaluate AMU policy and practices within individual countries and in the international arena. The research community has also responded to the need for new insights into AMR. In Canada, extensive research has been conducted in this area over the past 5 years, including multiple collaborative projects between Canadian veterinary colleges, and federal and provincial agri-food or health departments. The Special Issue of the Canadian Journal of Veterinary Research shares some information on what has been accomplished in these projects, illustrates the existing gaps in knowledge, and suggests the research needs that might be considered in future. Researchers were solicited from Canadian veterinary colleges and other veterinary research institutions to provide manuscripts describing observational research that investigated antimicrobial resistance (AMR) and/or antimicrobial use (AMU) in bacteria from food and companion animals in Canada, with emphasis on research at the farm/household level as well as post-harvest level (slaughter, retail). The articles in this issue reflect the very diverse nature of AMR. The research ranges from development and evaluation of on-farm methods for AMU data collection, through susceptibility test performance, resistance prevalence in indicator and/or pathogenic bacteria, to associations between AMU and AMR. The research contexts include on-farm studies in cattle, chickens, and swine, AMU use for therapy, prophylaxis, and growth promotion, and AMR in both pathogens and commensals. It is clear that AMR research is being extensively pursued in the Canadian veterinary research community. Much progress has been made in understanding the prevalence of AMR in fecal commensals, particularly Escherichia coli in swine and cattle raised in commercial conditions, and how populations of these bacteria are responding, and in some cases apparently not responding, to the selection pressure of antimicrobials. Considerable progress has also been made towards development of antimicrobial data collection systems, which are badly needed in North America due to a lack of centralized antimicrobial distribution systems and universal prescription databases. There are many areas in which more research is needed. In this issue, resistance to antibacterial agents is profiled, but resistance to antiviral and antiparasitic agents are also problems in need of investigation. While study of the phenotypic expression of resistance is helpful, a much better understanding of the movement of resistance genes within and among populations of bacteria from animals, humans, and the environment, and the role of various selection pressures and risk factors on spread of resistance is required. There is a pressing need for better methods for assessing human and animal health risks and benefits from AMU, incorporation of research into antimicrobial use policy development and evaluation, and into alternatives for antimicrobial therapy. Several studies of AMR in companion animals are underway in Canada and other counties, but more work in this field is necessary. Clearly, much has been accomplished in understanding the intricacies of AMR in animals, but much more remains to be done. A bright spot on the horizon is the increasing depth of AMR expertise in Canada. The articles in this issue are all drawn from the theses of graduate students.

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)0,004
score de la tête « metaresearch » (Gemma)0,019
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,159
Score d'incertitude au seuil0,533

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,019
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0040,002
Études des sciences et des technologies0,0020,001
Communication savante0,0070,005
Science ouverte0,0040,004
Intégrité de la recherche0,0090,005
Charge utile insuffisante (le modèle a refusé de juger)0,1590,093

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 Opus0,020
Tête enseignante GPT0,227
Écart entre enseignants0,208 · 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'étudeSans objet
Domainenon disponible
GenreÉditorial

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

En bref

Citations5
Publié2008
Routes d'admission1
Résumé présentoui

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