Antimicrobial resistance and machine learning: past, present, and future

Comparative genomic and antimicrobial resistance profiles of Salmonella strains isolated from pork and human sources in Sichuan, China

Introduction

Salmonella detection in retail pork is increasing, yet studies on its antimicrobial resistance (AMR) profiles and genomic characteristics remain limited. Moreover, it is still unclear whether certain Salmonella sequence types (STs) are consistently or rarely associated with pork as a transmission source. Sichuan province, the largest pork-production region in China, provides a critical setting to investigate these dynamics.

Methods

In this study, 213 Salmonella strains isolated from pork and human sources (2019-2021) underwent phenotypic AMR testing and whole-genome sequencing (WGS).

Results

Resistance profiling revealed a higher prevalence of AMR in the pork-derived strains, particularly in veterinary-associated antibiotics. We identified STs not observed in pork in this study, such as ST23 (S. Oranienburg) and the poultry-commonly associated ST32 (S. Infantis), suggesting potential non-pork transmission routes for these Salmonella STs. To quantify sequence type diversity within each sample source, we introduced the sequencing type index (ST index = number of different STs/ total isolates). The ST index was 32% (49/153) for human-derived isolates and 20% (12/60) for pork-derived isolates. PERMANOVA analysis revealed significant differences in the structural composition of sequence types between human- and pork-derived isolates (p = 0.001), indicating that pork may harbor specific Salmonella STs more frequently.

Discussion

These findings highlight the role of pork as a reservoir for certain Salmonella STs, while also implying potential non-pork transmission pathways. The ST index represents a novel metric for assessing Salmonella diversity across different sample sources, offering a better understanding of genetic variation and transmission dynamics.

Comparative genome analysis reveals putative and novel antimicrobial resistance genes common to the nosocomial infection pathogens

The 21st century has witnessed several clinical outcomes regarding AMR. One health concept has been foreseen as a standard global public health initiative in ensuring human, animal and environmental health. The present study explores critical Gram-negative ESKAPE pathogens encompassing Acinetobacter baumannii (ACB), Klebsiella pneumoniae (KPX) and Pseudomonas aeruginosa (PAE). A comparative genomic analysis approach was utilized for identifying novel and putative genes coercing global health consequences stressing the significance of the above iatrogenic and nosocomial pathogens. O findings reveal that Pseudomonas aeruginosaPAO1 (PAE) possesses the largest genome, measuring 62,64,404 base pairs, containing 14,342 protein-coding genes and an elevated count of ORFs, surpassing other organisms. Notably, P. aeruginosa PAO1 exhibits a comprehensive metabolic landscape with 355 pathways and 1659 metabolic reactions, encompassing 200 biosynthesis and 132 degradation pathways. Transferases are the predominant enzyme category across all three genomes, followed by oxidoreductases and hydrolases. The pivotal role of beta-lactamase in conferring resistance against antibiotics is also evident in all three microbes. This investigation underscores the PAE genome harbours genes and enzymes associated with heightened virulence in antibiotic resistance. The holistic review combined with comparative genomics underlines the significance of delving into the genomes of these antimicrobial-resistant organisms. In silico methodologies are increasingly stressed in aiding the successful accomplishment of the United Nations Sustainable Development Goal -3: Good Health and Well-being. The prominent findings establish Carbapenem resistance and evolutionary lineages of the MCR-1 gene conferring AMR landscapes for future research.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

The scholarly footprint of ChatGPT: a bibliometric analysis of the early outbreak phase

This paper presents a comprehensive analysis of the scholarly footprint of ChatGPT, an AI language model, using bibliometric and scientometric methods. The study zooms in on the early outbreak phase from when ChatGPT was launched in November 2022 to early June 2023. It aims to understand the evolution of research output, citation patterns, collaborative networks, application domains, and future research directions related to ChatGPT. By retrieving data from the Scopus database, 533 relevant articles were identified for analysis. The findings reveal the prominent publication venues, influential authors, and countries contributing to ChatGPT research. Collaborative networks among researchers and institutions are visualized, highlighting patterns of co-authorship. The application domains of ChatGPT, such as customer support and content generation, are examined. Moreover, the study identifies emerging keywords and potential research areas for future exploration. The methodology employed includes data extraction, bibliometric analysis using various indicators, and visualization techniques such as Sankey diagrams. The analysis provides valuable insights into ChatGPT's early footprint in academia and offers researchers guidance for further advancements. This study stimulates discussions, collaborations, and innovations to enhance ChatGPT's capabilities and impact across domains.

Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis

BACKGROUND

The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments.

OBJECTIVE

To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation.

METHODS

Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis.

RESULTS

The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42-0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79-0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9-39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34-0.61), the diagnostic odds ratio was 23 (95% CI: 7-81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74-0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies.

CONCLUSION

This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.