An accurate and interpretable model for antimicrobial resistance in pathogenic Escherichia coli from livestock and companion animal species

Fecal carriage of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli in South American camelids and biosecurity practices among farms in northern Italy

South American camelids (SACs), particularly llamas (Lama glama) and alpacas (Vicugna pacos) are gaining popularity in Europe. Initially valued for their fiber and land management capabilities, these animals are now also kept for animal therapy, outdoor activities, and as companion animals. Despite their close interactions with humans and other animals, there is limited research on the transmission of microbes or antimicrobial resistance genes from SACs. This study aimed to survey the fecal carriage of extended-spectrum beta-lactamase ESBL-producing Escherichia coli (ESBL- E. coli) in SACs. A questionnaire was administered on-site to the farmers to survey management and biosecurity measures. Twelve farms from northern Italy (Lombardy, Piedmont, Veneto, and Emilia-Romagna) participated in the study. Fecal samples were analyzed to identify ESBL- E. coli and subjected to bacteriological culture on CHROMagar™ ESBL plates. Isolate identification was accomplished by MALDI-TOF MS, then subjected to the double-disk synergy test (DDST) and examined for 3 ESBL-encoding genes (blaCTX-M, blaTEM, blaSHV) via PCR analysis. A total of 125 SACs (19 llamas and 106 alpacas) were included. Four (3.2%) of these animals were positive for ESBL- E. coli. Two isolates carried the blaCTX-M gene; one had both blaCTX-M and blaTEM, and one was negative for all ESBL-encoding genes. Furthermore, none of the ESBL- E. coli isolates tested positive for the blaSHV gene. Several data from our questionnaire revealed a lack of biosecurity protocols, which aligns with other studies. The prevalence of ESBL-producing bacteria identified in this study was lower than in different other studies. Despite the low biosecurity levels observed on the SAC farms, our findings showed a low occurrence of ESBL- E. coli and a low carriage rate of multidrug-resistant (MDR) ESBL- E. coli.

Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects

The issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. These advanced computational methods are capable of processing and analyzing large-scale biomedical data, thereby uncovering complex patterns and mechanisms behind the development of resistance. AI technologies are increasingly applied to predict the resistance of pathogens to various antibiotics based on gene content and genomic composition. This article reviews the latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms. We begin with an overview of the biological foundations of microbial resistance and its epidemiological research. Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. Furthermore, we explore the major challenges in the field, such as data availability, model interpretability, and cross-species resistance prediction. Finally, we discuss new perspectives and solutions for research into microbial resistance through algorithm optimization, dataset expansion, and interdisciplinary collaboration. With the continuous advancement of AI technology, we will have the most powerful weapon in the fight against pathogenic microbial resistance in the future.

Phenotypic and Genotypic Characterizations of Antimicrobial-Resistant Escherichia coli Isolates from Diverse Retail Meat Samples in North Carolina During 2018-2019

Surveillance of antimicrobial-resistant pathogens in U.S. retail meats is conducted to identify potential risks of foodborne illness. In this study, we conducted a phenotypic and genotypic analysis of Escherichia coli recovered from a diverse range of retail meat types during 2018-2019 in North Carolina. The investigation was conducted as part of the National Antimicrobial Resistance Monitoring System (NARMS). Retail meat sampling and E. coli isolation were performed in accordance with NARMS retail meat isolation protocols. We used the Sensititre™ broth microdilution system to determine phenotypic resistance to 14 antimicrobial agents and the Illumina next-generation sequencing platform for genotypic resistance profiling. The highest prevalence of E. coli isolates was found in ground turkey (n = 57, 42.9%) and chicken (n = 27, 20.3%), followed by ground beef (n = 25, 18.9%) and pork (n = 24, 18%). The isolates were divided into seven different phylogroups using the Clermont typing tool, with B1 (n = 59, 44.4%) and A (n = 39, 29.3%) being the most dominant, followed by B2 (n = 14, 10.5%), D (n = 7, 5.3%), F (n = 6, 4.5%), E (n = 3, 2.3%), and C (n = 2, 1.5%). Using multilocus sequence typing (MLST), 128 Sequence types (STs) were identified indicating high diversity. Phenotypic and genotypic resistance was observed toward aminoglycosides, sulfonamides, beta-lactams, macrolides, tetracyclines, phenicols, and fluoroquinolones. Ground turkey samples were more resistant to the panel of tested antimicrobials than chicken, beef, or pork (p < 0.05). All isolates were found to be susceptible to meropenem. A high percentage of turkey isolates (n = 16, 28%) were multidrug-resistant (MDR) compared with 18.5% of chicken (n = 5), 8.4% of pork (n = 2), and 8% of beef isolates (n = 2). This study highlights the benefit of surveillance to identify MDR E. coli for epidemiologic tracking and is a comprehensive report of the phenotypic and genotypic characterization of E. coli isolated from retail meats in North Carolina.