Antisense agents against antibiotic-resistant bacteria

The dramatically increasing levels of antibiotic resistance are being seen worldwide, and is a significant threat to public health. Antibiotic and drug resistance is seen in various bacterial species. Antibiotic resistance is associated with increased morbidity and mortality and increased treatment costs. Antisense-relevant technologies include the utilization of oligonucleotide molecules to interfere with gene expression, as a new technique for the treatment of antibiotic-resistant bacteria has been proposed antisense agents or nucleic acids analogs with antibacterial properties, which are commonly very short and their size almost 10-20 bases and can be hinted to peptide nucleic acids (PNAs), phosphorodiamidate morpholino oligomers (PPMOs) and locked nucleic acids (LNAs). This review highlights that PNAs, PPMOs, and LNAs target the genes that cause destroy the gene and inhibit the growth of bacteria. These results open a new perspective for therapeutic intervention. In future studies, it is necessary to examine different aspects of antisense agents, for example, safety, toxicity, and pharmacokinetic properties of antisense agents to be employed in clinical treatment.

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health. Graphical abstract