English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the zaia usage as premium feature

ZAIA

Presents the pdf analysis as premium feature

PDF Analysis

Insights

Presents the summarisation as premium feature

Summarisation

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the access of premium content as premium feature

Premium Content

Zendy Tools

Zendy Open

Summary   Klebsiella pneumoniae  has become the number one bacterial pathogen that causes high mortality in clinical settings worldwide. Clinical  K .  pneumoniae  strains with carbapenem resistance and/or hypervirulent phenotypes cause higher mortality comparing with classical  K .  pneumoniae  strains. Rapid differentiation of clinical  K .  pneumoniae  with high resistance/hypervirulence from classical  K. pneumoniae  would allow us to develop rational and timely treatment plans. In this study, we developed a convolution neural network (CNN) as a prediction method using Raman spectra raw data for rapid identification of ARGs, hypervirulence‐encoding factors and resistance phenotypes from  K .  pneumoniae  strains. A total of 71  K .  pneumoniae  strains were included in this study. The minimum inhibitory concentrations (MICs) of 15 commonly used antimicrobial agents on  K .  pneumoniae  strains were determined. Seven thousand four hundred fifty‐five spectra were obtained using the InVia Reflex confocal Raman microscope and used for deep learning‐based and machine learning (ML) algorithms analyses. The quality of predictors was estimated in an independent data set. The results of antibiotic resistance and virulence‐encoding factors identification showed that the CNN model not only simplified the classification system for Raman spectroscopy but also provided significantly higher accuracy to identify  K .  pneumoniae  with high resistance and virulence when compared with the support vector machine (SVM) and logistic regression (LR) models. By back‐testing the Raman‐CNN platform on 71  K .  pneumoniae  strains, we found that Raman spectroscopy allows for highly accurate and rationally designed treatment plans against bacterial infections within hours. More importantly, this method could reduce healthcare costs and antibiotics misuse, limiting the development of antimicrobial resistance and improving patient outcomes.

Identification of antibiotic resistance and virulence‐encoding factors in Klebsiella pneumoniae by Raman spectroscopy and deep learning