Academic Journal
Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy
| العنوان: | Identification of hypermucoviscous Klebsiella pneumoniae K1, K2, K54 and K57 capsular serotypes by Raman spectroscopy |
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| المؤلفون: | Fernández Manteca, María Gabriela, Ocampo Sosa, Alain Antonio, Fernández Vecilla, Domingo, Siller Ruiz, María, Pía Roiz, María, Madrazo, Fidel, Rodríguez Grande, Jorge, Calvo Montes, Jorge, Rodríguez Cobo, Luis, López Higuera, José Miguel, Fariñas Álvarez, María del Carmen, Cobo García, Adolfo |
| المساهمون: | Universidad de Cantabria |
| المصدر: | Spectrochimica acta - part A: molecular and biomolecular spectroscopy, 2024, 319, 124533 |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2024 |
| المجموعة: | Universidad de Cantabria: UCrea |
| مصطلحات موضوعية: | Raman spectroscopy, |
| الوصف: | 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 one-dimensional 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. ; This work was supported by the R+D projects PREVAL23/05, INNVAL19/17, INNVAL23/10, funded by Instituto de Investigación Valdecilla (IDIVAL); TED2021-130378B-C21 funded by MCIN/AEI/ 10.13039/501100011033/ European Union NextGenerationEU/PRTR; ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1386-1425 |
| Relation: | https://doi.org/10.1016/j.saa.2024.124533; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137269OB-C22/ES/SENSORES FOTONICOS PARA CIUDADES INTELIGENTES Y SOSTENIBLES II/"; PID2022-137269OB-C22; TED2021-130378B-C21; https://hdl.handle.net/10902/33228 |
| DOI: | 10.1016/j.saa.2024.124533 |
| الاتاحة: | https://hdl.handle.net/10902/33228 https://doi.org/10.1016/j.saa.2024.124533 |
| Rights: | Attribution 4.0 International ; http://creativecommons.org/licenses/by/4.0/ ; openAccess |
| رقم الانضمام: | edsbas.7C403F81 |
| قاعدة البيانات: | BASE |
| تدمد: | 13861425 |
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| DOI: | 10.1016/j.saa.2024.124533 |