Researchers have employed machine learning to diagnose and localize sickness in chicken farms. It has helped to reduce the need for antibiotic treatment and the risk of antibiotic resistance spreading to human populations.
Chicken Enclosure Farm
Antimicrobial Resistance in Poultry
Antimicrobial resistance is a global health threat, according to Frontiers in Veterinary Science. One of its significant sources is the use of antibiotics and AMR in the production of animals.
To prevent and treat disease and promote growth, poultry flocks are frequently raised under harsh conditions and with heavy doses of antibiotics. In addition to being a source of resistant bacteria/genes that may pose a risk to human health, antimicrobial-resistant poultry diseases can cause treatment failure and economic losses.
The fast expansion of chicken production in China to fulfill rising demand has led to the widespread and uncontrolled use of antibiotics. As a result, there has been a concerning rise in the number of animal cases of antimicrobial resistance, which might spread to people through direct contact, environmental pollution, and food consumption.
Effective and quick diagnosis of bacterial illnesses in chicken farming can reduce the demand for antibiotics, reducing the spread of epidemics and AMR. According to Phys.org, antibiotic resistance is currently one of the most dangerous threats worldwide.
For this study published in ISME Journal, samples were collected from local slaughterhouses, Chinese farm animals, and people.
New diagnostic biomarkers have been discovered after analysis of this complicated huge data. It will forecast and identify bacterial infections, the rise of AMR, and human transmission. Using this information will enable early detection and treatment, reducing infection and the need for antibiotics.
Identical Antibiotic Resistance Genes on Human and Chicken
Based on the key findings, both human and broiler chicken samples contained a number of identical clinically relevant antibiotic resistance genes (ARGs) and related mobile genetic elements. In particular, samples from various hosts shared eleven different types of clinically significant antibiotic resistance genes with conserved mobile ARG gene architectures.
According to Dr. Dottorini, the similarities would have been overlooked if they had employed a comprehensive comparative analysis. The results of this investigation revealed that resistomes and microbiomes vary between hosts and settings. Overall, this result emphasizes the relevance of using many scales to examine the similarities and differences between resistomes and microbiomes in intricately interrelated settings.
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Metagenomics Data Integration
Other findings demonstrated that the scientists discovered the existence of a core chicken gut resistome connected with the AMR circulating in the farms through machine learning-powered methodology integrating metagenomics data with culture-based methodologies. These findings reinforced the idea that there are relationships between the types of ARGs found in the resistome and the resistance characteristics of specific commensal and pathogenic bacteria.
The scientists also discovered that the AMR-related core resistomes are linked to various environmental conditions, including temperature and humidity, utilizing sensor technology and machine learning.
According to Dottorini, the food manufacturing business is a big consumer of antibiotics. Yet, the dangers associated with AMR in these settings are still not fully recognized. Therefore, it is crucial to lay out research and better techniques tailored to these settings where people and animals may be close. AMR hazards in farming contexts can be better understood and quantified with the help of approaches that can be developed due to precision farming, affordable DNA sequencing, and the growing adoption of machine learning technology.
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