Source Tracing of Kidney Injury via the Multispectral Fingerprint Identified by Machine Learning-Driven Surface-Enhanced Raman Spectroscopic Analysis.

Early diagnosis of drug-induced kidney injury (DIKI) is essential for clinical treatment and intervention. However, developing a reliable method to trace kidney injury origins through retrospective studies remains a challenge. In this study, we designed ordered fried-bun-shaped Au nanocone arrays (FBS NCAs) to create microarray chips as a surface-enhanced Raman scattering (SERS) analysis platform. Subsequently, the principal component analysis (PCA)-two-layer nearest neighbor (TLNN) model was constructed to identify and analyze the SERS spectra of exosomes from renal injury induced by cisplatin and gentamycin. The established PCA-TLNN model successfully differentiated the SERS spectra of exosomes from renal injury at different stages and causes, capturing the most significant spectral features for distinguishing these variations. For the SERS spectra of exosomes from renal injury at different induction times, the accuracy of PCA-TLNN reached 97.8% (cisplatin) and 93.3% (gentamicin). For the SERS spectra of exosomes from renal injury caused by different agents, the accuracy of PCA-TLNN reached 100% (7 days) and 96.7% (14 days). This study demonstrates that the combination of label-free exosome SERS and machine learning could serve as an innovative strategy for medical diagnosis and therapeutic intervention.

Phenylboronic acid modification-based novel dumbbell-shaped Au-Ag nanorod SERS substrates for ultrasensitive detection of SO42.

Based on the coordination principle of Lewis acids, a 4-mercaptophenylboronic acid (4-MPBA)-modified novel dumbbell-shaped Au-Ag nanorod (4-MPBA@DS Au-AgNR) substrate was developed, which could be combined with the surface-enhanced Raman scattering (SERS) technique to detect SO42- with high sensitivity and specificity. DS Au-AgNRs synthesized in this study with a dumbbell-shaped structure were verified by finite-difference time domain (FDTD) simulation to be capable of stimulating strong localized electromagnetic enhancement (EM) at nano-edge and gap, generating a large number of "hot spots" exhibiting excellent SERS performance. The 4-MPBA modified on its surface could specifically recognize SO42-, producing a change in the spectral peak at 1382 cm-1, thus realizing highly sensitive and specific sensing of SO42-. Under optimized conditions, this SERS sensor responded rapidly to SO42- within 2 minutes and demonstrated outstanding specificity. Calculation of the ratio of the characteristic peaks at 1382 and 1070 cm-1 (I1382/I1070) enabled the quantitative detection of SO42- in the range of 1 × 10-8-1 × 10-3 M, and the detection threshold was as low as 1 nM, which was superior to those of similar detection methods. Importantly, the utility and reliability of this SERS substrate for the determination of SO42- in actual samples were evaluated using ion chromatography as the gold standard, and there was no significant difference between the two protocols (P > 0.05), and the RSD was less than 6% with a satisfactory recovery rate (97.6-102.3%). Therefore, the present protocol has the advantages of simplicity and rapidity, high sensitivity, specificity, stability, and practicability in the determination of SO42- in aqueous solution, providing a reliable solution for tracing SO42- in the fields of food safety and environmental testing.