ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
An Interpretable SERS-AI Platform for Rapid and Quantitative Diagnosis of Polymicrobial UTIs: Powered by Positively Charged Plasmonic Nanoparticles and Attention-Based Deep Learning.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Rapid single-cell identification of foodborne pathogens with limited data: A peak-aware Raman attention deep learning model.Current research in food science · 2026Article
- Explainable AI-SERS approach for highly accurate discrimination ofCurrent research in microbial sciences · 2026Article
- An Interpretable SERS-AI Platform for Rapid and Quantitative Diagnosis of Polymicrobial UTIs: Powered by Positively Charged Plasmonic Nanoparticles and Attention-Based Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
Polymicrobial urinary tract infections (UTIs) present diagnostic challenges due to overlapping symptoms and limitations of conventional methods. Although SERS and AI have shown potential for microbial diagnostics, existing approaches lack reproducibility, quantification capability, and interpretability-especially in complex clinical samples. Here, a label-free, interpretable SERS-AI platform for rapid identification and quantification of mixed urinary tract pathogens is proposed. A plasmonic substrate is engineered by combining Au@Ag core-shell nanoparticles with a positively charged bPEI surface, enabling electrostatic bacterial capture and stable SERS signal generation across diverse microbial mixtures. A convolutional neural network (CNN) enhanced with a convolutional block attention module (CBAM) to enable both accurate classification (95.8%, AUC = 0.9774) and reliable bacterial proportion prediction (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.