Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a...Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a novel,noninvasive,and accurate diagnostic method using surface-enhanced Raman spectroscopy(SERS)combined with machine learning to analyze seminal plasma exosomes.Semen samples from healthy controls(n=32)and azoospermic patients(n=22)were collected,and their exosomal SERS spectra were obtained.Machine learning algorithms were employed to distinguish between the SERS pro files of healthy and azoospermic samples,achieving an impressive sensitivity of 99.61%and a speci ficity of 99.58%,thereby highlighting signi ficant spectral differences.This integrated SERS and machine learning approach offers a sensitive,label-free,and objective diagnostic tool for early detection and monitoring of azoospermia,potentially enhancing clinical outcomes and patient management.展开更多
基金support from the National Natural Science Foundation of China(No.62275049)the Natural Science Foundation of Fujian Province,China(No.2022J02024)the Fujian Province Joint Fund Project for Scientific and Technological Innovation(2023Y9383).
文摘Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a novel,noninvasive,and accurate diagnostic method using surface-enhanced Raman spectroscopy(SERS)combined with machine learning to analyze seminal plasma exosomes.Semen samples from healthy controls(n=32)and azoospermic patients(n=22)were collected,and their exosomal SERS spectra were obtained.Machine learning algorithms were employed to distinguish between the SERS pro files of healthy and azoospermic samples,achieving an impressive sensitivity of 99.61%and a speci ficity of 99.58%,thereby highlighting signi ficant spectral differences.This integrated SERS and machine learning approach offers a sensitive,label-free,and objective diagnostic tool for early detection and monitoring of azoospermia,potentially enhancing clinical outcomes and patient management.
