Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Document type

Article

Version

Published version

Publication date

Publication license

cc by (c) Pazos-Pérez et al., 2016
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/101576

Ultrasensitive multiplex optical quantification of bacteria in large samples of biofluids

Journal Title

Director/Tutor

Journal ISSN

Volume Title

Abstract

Efficient treatments in bacterial infections require the fast and accurate recognition of pathogens, with concentrations as low as one per milliliter in the case of septicemia. Detecting and quantifying bacteria in such low concentrations is challenging and typically demands cultures of large samples of blood (~1 milliliter) extending over 24-72 hours. This delay seriously compromises the health of patients. Here we demonstrate a fast microorganism optical detection system for the exhaustive identification and quantification of pathogens in volumes of biofluids with clinical relevance (~1 milliliter) in minutes. We drive each type of bacteria to accumulate antibody functionalized SERS-labelled silver nanoparticles. Particle aggregation on the bacteria membranes renders dense arrays of inter-particle gaps in which the Raman signal is exponentially amplified by several orders of magnitude relative to the dispersed particles. This enables a multiplex identification of the microorganisms through the molecule-specific spectral fingerprints.

Citation

Citation

PAZOS PÉREZ, Nicolas, et al. Ultrasensitive multiplex optical quantification of bacteria in large samples of biofluids. Scientific Reports. 2016. Vol. 6, pags. 29014. ISSN 2045-2322. [consulted: 8 of August of 2026]. Available at: https://hdl.handle.net/2445/101576

Export metadata

JSON - METS

Share record