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1.
Sci Adv ; 9(24): eadg6670, 2023 06 16.
Article in English | MEDLINE | ID: mdl-37327328

ABSTRACT

Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We report a paper-like battery-free in situ AI-enabled multiplexed (PETAL) sensor for holistic wound assessment by leveraging deep learning algorithms. This sensor consists of a wax-printed paper panel with five colorimetric sensors for temperature, pH, trimethylamine, uric acid, and moisture. Sensor images captured by a mobile phone were analyzed by neural network-based machine learning algorithms to determine healing status. For ex situ detection via exudates collected from rat perturbed wounds and burn wounds, the PETAL sensor can classify healing versus nonhealing status with an accuracy as high as 97%. With the sensor patches attached on rat burn wound models, in situ monitoring of wound progression or severity is demonstrated. This PETAL sensor allows early warning of adverse events, which could trigger immediate clinical intervention to facilitate wound care management.


Subject(s)
Burns , Wound Healing , Rats , Animals , Machine Learning , Algorithms
2.
Analyst ; 146(22): 6924-6934, 2021 Nov 08.
Article in English | MEDLINE | ID: mdl-34647550

ABSTRACT

A portable surface-enhanced Raman spectroscopy (SERS) sensor for detecting pyocyanin (PYO) in simulated wound fluid and from bacteria samples was developed. Solution-phase SERS detection protocols are designed to be compatible with two different clinical practices for wound exudate collection, namely negative pressure liquid collection and swabbing. For citrate-coated metal nanoparticles of three different compositions, i.e. gold (AuNPs), alloyed silver/gold (AgAuNPs), and silver (AgNPs), we firstly confirmed their interaction with PYO in the complex wound fluid, using fluorescence quenching experiments, which rationalized the Raman enhancement effects. We then demonstrated the Raman enhancement effects of the metal nanoparticles in the order of AgNPs > AgAuNPs > AuNPs. The limit of detection (LOD) achieved for PYO is 1.1 µM (in a linear range of 0.1-25 µM by the AgNPs), 10.9 µM (in a linear range of 5-100 µM, by the AgAuNPs), and 17.7 µM (in a linear range of 10-100 µM by the AuNPs). The AgNP and AgAuNP sensors together cover the sensitivity and dynamic range requirements for the clinical detection of wound infection, where PYO is present at a concentration of 1-50 µM. In addition, sterilized cotton swabs were used to collect wound fluid and transfer samples into AgNP solution for SERS measurements. This detection protocol was completed within 5 minutes with a LOD of 23.1 µM (in a linear range of 15-100 µM). The SERS sensing protocol was validated by its successful detection of PYO in cultured Pseudomonas aeruginosa bacteria. The findings presented in this work pave the way towards point-of-care diagnostics of wound infections.


Subject(s)
Metal Nanoparticles , Pyocyanine , Gold , Silver , Spectrum Analysis, Raman
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