ArticleEnvironment & health (Washington, D.C.)2026
Clinical Human-Derived Pathogen Signatures Captured by SERS and Deep Learning for Environmental Exposure Risk Assessment.
Article in Environment & health (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Quantitative Surface-Enhanced Raman Spectroscopy: Challenges, Strategies, and Prospects.Molecules (Basel, Switzerland) · 2026Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial contamination and antibiotic-resistant bacteria (ARB) pose significant threats to environmental ecosystems, particularly in regions lacking adequate sanitation. Urinary pathogens of human origin, frequently detected in contaminated water and surfaces, represent early indicators of ARB-related exposure. However, current exposure assessment approaches rely heavily on standard strains, limiting their effectiveness in real-world scenarios. In this study, a label-free platform was developed by integrating surface-enhanced Raman spectroscopy (SERS) with a convolutional neural network (CNN) for the ARB exposure assessment. A comprehensive spectral database consisting of 368 clinical urinary isolates was established. The CNN achieved the highest classification accuracy (97.6%), surpassing random forest (93.1%) and PCA-SVM (91.2%). Robust performance was further confirmed in wastewater samples (92.2%) and independent urine specimens (90.3%). Importantly, SHAP-based interpretation revealed key discriminatory features in the 724-738 cm
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