Rapid enrichment and SERS differentiation of various bacteria in skin interstitial fluid by 4-MPBA-AuNPs-functionalized hydrogel microneedles.

Ying YANG; Xingyu WANG; Yexin HU; Zhongyao LIU; Xiao MA; Feng FENG; Feng ZHENG; Xinlin GUO; Wenyuan LIU; Wenting LIAO; Lingfei HAN

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Rapid enrichment and SERS differentiation of various bacteria in skin interstitial fluid by 4-MPBA-AuNPs-functionalized hydrogel microneedles.

Author: Ying YANG ¹ ; Xingyu WANG ¹ ; Yexin HU ¹ ; Zhongyao LIU ¹ ; Xiao MA ² ; Feng FENG ³ ; Feng ZHENG ¹ ; Xinlin GUO ⁴ ; Wenyuan LIU ¹ ; Wenting LIAO ¹ ; Lingfei HAN ¹
Author Information

1. Department of Pharmaceutical Analysis, China Pharmaceutical University, Nanjing, 211198, China.
2. Gansu Institute for Drug Control, Lanzhou, Gansu, 730000, China.
3. School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
4. School of Chemistry, University of Manchester, Manchester, M13 9PL, UK.
Publication Type:Journal Article
Keywords: Broad-spectrum bacteria detection; Hydrogel microneedle; Machine learning; SERS; Skin interstitial fluid
From: Journal of Pharmaceutical Analysis 2025;15(3):101152-101152
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 diagnose 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 interstitial 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 machine 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 × 10⁷ to 1 × 10¹⁰ 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.