Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy

Autores de IDIVAL

• 

  María Gabriela Fernández Manteca

  Autor

• 

  Alain Antonio Ocampo Sosa

  Autor

• Domingo Fernández Vecilla

  Autor

• 

  Fidel Madrazo Toca

  Autor

• 

  Jorge Rodríguez Grande

  Autor

• 

  Jorge Calvo Montes

  Autor

• 

  Luis Rodríguez Cobo

  Autor

• José Miguel López Higuera

  Autor

• 

  María Del Carmen Fariñas Álvarez

  Autor

• 

  Adolfo Cobo García

  Autor

Autores ajenos al IDIVAL

• Ruiz MS
• Roiz MP

Unidades

• 

  ENFERMEDADES INFECCIOSAS Y MICROBIOLOGIA CLÍNICA

• 

  INGENIERÍA FOTÓNICA

Abstract

Antimicrobial resistance poses a significant challenge in modern medicine, affecting public health. Klebsiella pneumoniae infections compound this issue due to their broad range of infections and the emergence of multiple antibiotic resistance mechanisms. Efficient detection of its capsular serotypes is crucial for immediate patient treatment, epidemiological tracking and outbreak containment. Current methods have limitations that can delay interventions and increase the risk of morbidity and mortality. Raman spectroscopy is a promising alternative to identify capsular serotypes in hypermucoviscous K. pneumoniae isolates. It provides rapid and in situ measurements with minimal sample preparation. Moreover, its combination with machine learning tools demonstrates high accuracy and reproducibility. This study analyzed the viability of combining Raman spectroscopy with onedimensional convolutional neural networks (1-D CNN) to classify four capsular serotypes of hypermucoviscous K. pneumoniae : K1, K2, K54 and K57. Our approach involved identifying the most relevant Raman features for classification to prevent overfitting in the training models. Simplifying the dataset to essential information maintains accuracy and reduces computational costs and training time. Capsular serotypes were classified with 96 % accuracy using less than 30 Raman features out of 2400 contained in each spectrum. To validate our methodology, we expanded the dataset to include both hypermucoviscous and non-mucoid isolates and distinguished between them. This resulted in an accuracy rate of 94 %. The results obtained have significant potential for practical healthcare applications, especially for enabling the prompt prescription of the appropriate antibiotic treatment against infections.

Keywords

• Raman spectroscopy; Hypermucoviscous/hypervirulent Klebsiella pneumoniae; Capsular serotype; Machine learning; Convolutional neural network