Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis
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...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2025-01-01
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| Series: | Journal of Innovative Optical Health Sciences |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S1793545825500038 |
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| author | Jiarui Wang Shiyan Jiang Jiaxin Shi Jing Wang Shengrong Du Zufang Huang |
| author_facet | Jiarui Wang Shiyan Jiang Jiaxin Shi Jing Wang Shengrong Du Zufang Huang |
| author_sort | Jiarui Wang |
| collection | DOAJ |
| description | 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 ([Formula: see text]) and azoospermic patients ([Formula: see text]) were collected, and their exosomal SERS spectra were obtained. Machine learning algorithms were employed to distinguish between the SERS profiles of healthy and azoospermic samples, achieving an impressive sensitivity of 99.61% and a specificity of 99.58%, thereby highlighting significant 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. |
| format | Article |
| id | doaj-art-e9b7cf121ba14ed8b80f7dda6cf3b0f3 |
| institution | Kabale University |
| issn | 1793-5458 1793-7205 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj-art-e9b7cf121ba14ed8b80f7dda6cf3b0f32025-01-27T05:49:52ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052025-01-01180110.1142/S1793545825500038Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysisJiarui Wang0Shiyan Jiang1Jiaxin Shi2Jing Wang3Shengrong Du4Zufang Huang5Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, P. R. ChinaKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, P. R. ChinaKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, P. R. ChinaKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, P. R. ChinaCenter of Reproductive Medicine, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou 350001, P. R. ChinaKey Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, P. R. ChinaMale 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 ([Formula: see text]) and azoospermic patients ([Formula: see text]) were collected, and their exosomal SERS spectra were obtained. Machine learning algorithms were employed to distinguish between the SERS profiles of healthy and azoospermic samples, achieving an impressive sensitivity of 99.61% and a specificity of 99.58%, thereby highlighting significant 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.https://www.worldscientific.com/doi/10.1142/S1793545825500038AzoospermiaRaman spectroscopySERSmachine learningseminal plasma exosomes |
| spellingShingle | Jiarui Wang Shiyan Jiang Jiaxin Shi Jing Wang Shengrong Du Zufang Huang Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis Journal of Innovative Optical Health Sciences Azoospermia Raman spectroscopy SERS machine learning seminal plasma exosomes |
| title | Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis |
| title_full | Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis |
| title_fullStr | Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis |
| title_full_unstemmed | Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis |
| title_short | Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis |
| title_sort | machine learning enhanced sers for accurate azoospermia diagnosis via seminal plasma exosome analysis |
| topic | Azoospermia Raman spectroscopy SERS machine learning seminal plasma exosomes |
| url | https://www.worldscientific.com/doi/10.1142/S1793545825500038 |
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