Researchers from Ulsan University School of Medicine and Seoul Asan Medical Center, including Professor Kim Jun-ki and Professor Shin Sung, have developed a diagnostic method that integrates surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI).

Traditionally, diagnosing transplant rejection involves invasive tissue biopsies, which can be risky and uncomfortable.

This new approach employs SERS, enhanced by gold-zinc oxide (Au-ZnO) nanoparticles, to detect low concentrations of specific biomarkers in blood samples. AI algorithms then analyze Raman spectra to identify rejection with high precision.

The study, published in Biosensors & Bioelectronics, demonstrated significant results. Researchers were able to categorize patient samples into three groups—no rejection, antibody-mediated rejection, and T cell-mediated rejection—with remarkable accuracy.

The accuracy rates were 93.53% using linear discriminant analysis (PC-LDA) and 98.82% with partial least squares discriminant analysis (PC-PLS-DA), achieved through advanced dimensionality reduction techniques.

This innovative method offers a promising alternative to traditional biopsy procedures, potentially allowing for earlier and more accurate detection of transplant rejection with a simple blood test.

Further research and validation are anticipated to establish this technique as a standard practice for monitoring kidney transplant patients.

The research received support from the Ministry of Health and Welfare's Health and Medical Technology Research and Development Project and the Ministry of Science and ICT.

The study’s authors include Professor Kim Jun-ki and Dr. Lee Sang-hwa from the Department of Convergence Medicine, and Professor Shin Sung and Dr. Kim Jin-myeong from the Department of Renal and Pancreatic Transplant Surgery.

 

Source: seoul.kr

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