Rapid enrichment and SERS differentiation of various bacteria in skin interstitial fluid by 4-MPBA-AuNPs-functionalized hydrogel microneedles
10.1016/j.jpha.2024.101152
- Author:
Ying YANG
1
;
Xingyu WANG
;
Yexin HU
;
Zhongyao LIU
;
Xiao MA
;
Feng FENG
;
Feng ZHENG
;
Xinlin GUO
;
Wenyuan LIU
;
Wenting LIAO
;
Lingfei HAN
Author Information
1. Department of Pharmaceutical Analysis,China Pharmaceutical University,Nanjing,211198,China
- Publication Type:Journal Article
- Keywords:
Hydrogel microneedle;
SERS;
Broad-spectrum bacteria detection;
Skin interstitial fluid;
Machine learning
- From:
Journal of Pharmaceutical Analysis
2025;15(3):564-576
- CountryChina
- Language:English
-
Abstract:
Bacterial infection is a major threat to global public health,and can cause serious diseases such as bacterial skin infection and foodborne diseases.It is essential to develop a new method to rapidly di-agnose clinical multiple bacterial infections and monitor food microbial contamination in production sites in real-time.In this work,we developed a 4-mercaptophenylboronic acid gold nanoparticles(4-MPBA-AuNPs)-functionalized hydrogel microneedle(MPBA-H-MN)for bacteria detection in skin inter-stitial fluid.MPBA-H-MN could conveniently capture and enrich a variety of bacteria within 5 min.Surface enhanced Raman spectroscopy(SERS)detection was then performed and combined with ma-chine learning technology to distinguish and identify a variety of bacteria.Overall,the capture efficiency of this method exceeded 50％.In the concentration range of 1 × 10 7 to 1 × 10 10 colony-forming units/mL(CFU/mL),the corresponding SERS intensity showed a certain linear relationship with the bacterial concentration.Using random forest(RF)-based machine learning,bacteria were effectively distinguished with an accuracy of 97.87％.In addition,the harmless disposal of used MNs by photothermal ablation was convenient,environmentally friendly,and inexpensive.This technique provided a potential method for rapid and real-time diagnosis of multiple clinical bacterial infections and for monitoring microbial contamination of food in production sites.
