Analysis of Multidrug Resistance in Staphylococcus aureus with a Machine Learning-Generated Antibiogram

Machine learning to predict antimicrobial resistance: future applications in clinical practice?

INTRODUCTION

Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction.

METHODS

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023.

RESULTS

Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93.

CONCLUSION

ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.

Comparative De Novo and Pan-Genome Analysis of MDR Nosocomial Bacteria Isolated from Hospitals in Jeddah, Saudi Arabia

Multidrug-resistant (MDR) bacteria are one of the most serious threats to public health, and one of the most important types of MDR bacteria are those that are acquired in a hospital, known as nosocomial. This study aimed to isolate and identify MDR bacteria from selected hospitals in Jeddah and analyze their antibiotic-resistant genes. Bacteria were collected from different sources and wards of hospitals in Jeddah City. Phoenix BD was used to identify the strains and perform susceptibility testing. Identification of selected isolates showing MDR to more than three classes on antibiotics was based on 16S rRNA gene and whole genome sequencing. Genes conferring resistance were characterized using de novo and pan-genome analyses. In total, we isolated 108 bacterial strains, of which 75 (69.44%) were found to be MDR. Taxonomic identification revealed that 24 (32%) isolates were identified as Escherichia coli, 19 (25.3%) corresponded to Klebsiella pneumoniae, and 17 (22.67%) were methicillin-resistant Staphylococcus aureus (MRSA). Among the Gram-negative bacteria, K. pneumoniae isolates showed the highest resistance levels to most antibiotics. Of the Gram-positive bacteria, S. aureus (MRSA) strains were noticed to exhibit the uppermost degree of resistance to the tested antibiotics, which is higher than that observed for K. pneumoniae isolates. Taken together, our results illustrated that MDR Gram-negative bacteria are the most common cause of nosocomial infections, while MDR Gram-positive bacteria are characterized by a wider antibiotic resistance spectrum. Whole genome sequencing found the appearance of antibiotic resistance genes, including SHV, OXA, CTX-M, TEM-1, NDM-1, VIM-1, ere(A), ermA, ermB, ermC, msrA, qacA, qacB, and qacC.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.

Synthesis and characterization of ionic liquid microneedle patches with different carbon chain lengths for antibacterial application

The prevention of bacterial infection is becoming more and more important in clinical medicine. Ionic liquids (ILs) can change the structure in an almost infinite way to actively antagonize pathogenic microorganism strains. The current biological materials of skin dressings inevitably have the shortcomings of single drug delivery form and low drug loading, which limit the practical application of skin dressings. Therefore, it is particularly important to develop drug delivery forms that can meet different conditions. The addition of ILs into crosslinked microneedle (MN) patches is a novel design scheme of MNs. The broad-spectrum antibacterial properties of imidazolium salt ILs ensure that the wound skin is sterile after the use of MN patches on the skin to open channels for drug delivery. In this study, imidazole IL monomers with different carbon chain lengths and the corresponding IL-MN patches were designed and synthesized. By comparing the antibacterial properties of four imidazolium salt IL monomers with different carbon chain lengths and the corresponding ionic liquid microneedle patches, we found that the antibacterial properties of IL monomers and IL-MN patches increased with the increase of substituent carbon chain lengths. Imidazole IL monomers have excellent antibacterial properties, which may be caused by the electrostatic interaction between the cations in the IL monomers and the anions in the bacterial membrane and the hydrophilic and hydrophobic interactions between the IL monomers.