Editorial: Antimicrobial use, antimicrobial resistance, and the microbiome in animals, volume II
Bibliographic record
Abstract
As demonstrated by the COVID-19 pandemic, endemic diseases or epidemic outbreaks represent a significant financial risk to the society. In veterinary medicine, these include also loss of animals, reduction of productivity and market access. Antimicrobials contribute to the treatment and prevention of infectious diseases in both animals and humans while improving farm animals' productivity and welfare. However, antimicrobial resistance (AMR) is becoming an important and growing economic and social problem inducing annual costs estimated at US$1 trillion to US$3.4 trillion worldwide (Ahmad et al., 2019;WHO, 2017), and is regarded as the silent pandemic. Even though AMR, a global threat to humans, animals, and the environment, is complex, wide use of antibiotics has been linked to the emergence and spread of AMR in all ecosystems (One Health). This Research Topic presents 12 studies on antimicrobial use (AMU) and AMR as well as on antimicrobial impacts on the microbiota and epidemiology, dissemination-transmission and the surveillance of AMR.It is well known that AMR is selected mainly by antibiotic/antimicrobial use (AMU). In conventional production, antibiotics (although "antibiotics" and "antimicrobials" are sometimes used interchangeably, antibiotics are actually a subset of antimicrobials) have been used to prevent infectious diseases. However, this practice is discontinued in more and more countries due to restrictions of antibiotic use. Antibiotics as feed additives to promote growth and eventually prevent diseases in healthy production animals has been banned by the European Union in 2006. In 2018, the European Parliament approved new restrictions on the use of antimicrobials in healthy livestock. The Government of Canada (and also many other countries) has developed a Federal Framework and This is a provisional file, not the final typeset article Optimization of therapeutic doses by a better knowledge of the pharmacokinetic/pharmacodynamic (PK/PD) could reduced the burden on AMR of therapeutic use of antimicrobials. . This concept has been presented for danofloxacin in pigs by Zhou et al.The microbiota play critical roles in the gut and establish general health in the animal by maintaining/improving organ integrity and functions, provision and absorptions of nutrients, and protecting against pathogens including promoting immunity. A well-established microbiota, plays particularly in youth age an important role in the animal. Feed additives including alternative to antibiotics received attention since the ban or restriction of in-feed antibiotics as growth promoters.Few studies have investigated the effects of antimicrobials on animal's metabolism, physiology and Editorial: Antimicrobial Use, Antimicrobial Resistance, and the Microbiome in Animals Volume II This is a provisional file, not the final typeset article immunity. In the contrast, several studies reported their effects on microbiota and microbiome.However, many other factors such as genetic (line), physiological status, sex (male, female), health (clinical and sub-clinical) and housing/husbandry influence the microbiota. Microbes respond to antimicrobials by developing and acquiring resistance mechanisms, change gene expression pater which alter their metabolism, nutrients uptake and transport (Brown et al., 2017). The elimination and reduction of multiplication bacteria by the antibiotics result in changes of the bacterial community structure and diversity.In this Research Topic, the analysis of the fecal microbiota in healthy, diarrheal and treated weaned piglets showed differences between these three animal groups (Kong et al. It is important to intensify research to understand circumstances leading to the emergence of pathogenic bacteria and of antibiotic resistance. In animal production industries, better practices in respect to food and environmental safety as well as public and animal health and welfare still need to be developed. Microorganisms living in changing environmental conditions adapt and evolve.Bacterial resistance determinants can be spread through horizontal gene transfer (HGT) which could This is a provisional file, not the final typeset article be in function of the temperature (Burnham 2021). Therefore, climate change and AMR are interlinked, and both should be addressed to protect humans, animals and the environment."One Health" approaches, using "omics" and well structured surveillance under government control, Antimicrobial resistance is a "One Health" issue because AMR genes can be spread across humans, animals and the environment. Surveillance, whole genome sequencing, microbiota/microbiome and antibiotic stewardship research are needed to determine important ARM drivers. Identification of hot spots and the ability to predict phenotype and transmission pathways along with adoption of best AMU practices will contribute to mitigate AMR. New knowledge contributing to the improvement of animal health and production as well as studies providing science-based evidences on AMR transmission Editorial: Antimicrobial Use, Antimicrobial Resistance, and the Microbiome in Animals Volume II This is a provisional file, not the final typeset article through the food chain and the environment are needed. Due to the high load of ARGs in animal manure and their potential spread to the environment when manures are used as soil fertilizers, the effects of different treatments of raw manures, such as composting (thermophilic composting and vermicomposting) and anaerobic digestion should be investigated.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.020 |
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".