Raman Spectrum of Follicular Fluid: A Potential Biomarker for Oocyte Developmental Competence in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in reproductive women where abnormal folliculogenesis is considered as a common characteristic. Our aim is to evaluate the potential of follicular fluid (FF) Raman spectra to predict embryo development and pregnancy outcome, so as to prioritize the best promising embryo for implantation, reducing both physiological and economical burdens of PCOS patients. In addition, the altered metabolic profiles will be identified to explore the aetiology and pathobiology of PCOS. In this study, follicular fluid samples obtained from 150 PCOS and 150 non-PCOS women were measured with Raman spectroscopy. Individual Raman spectrum was analyzed to find biologic components contributing to the occurrence of PCOS. More importantly, the Raman spectra of follicular fluid from the 150 PCOS patients were analyzed via machine-learning algorithms to evaluate their predictive value for oocyte development potential and clinical pregnancy. Mean-centered Raman spectra and principal component analysis (PCA) showed global differences in the footprints of follicular fluid between PCOS and non-PCOS women. Two Raman zones (993–1,165 cm−1 and 1,439–1,678 cm−1) were identified for describing the largest variances between the two groups, with the former higher and the latter lower in PCOS FF. The tentative assignments of corresponding Raman bands included phenylalanine and β -carotene. Moreover, it was found that FF, in which oocytes would develop into high-quality blastocysts and obtain high clinical pregnancy rate, were detected with lower quantification of the integration at 993–1,165 cm−1 and higher quantification of the integration at 1,439–1,678 cm−1 in PCOS. In addition, based on Raman spectra of PCOS FF, the machine-learning algorithms via the fully connected artificial neural network (ANN) achieved the overall accuracies of 90 and 74% in correctly assigning oocyte developmental potential and clinical pregnancy, respectively. The study suggests that the PCOS displays unique metabolic profiles in follicular fluid which could be detected by Raman spectroscopy. Specific bands in Raman spectra have the biomarker potential to predict the embryo development and pregnancy outcome for PCOS patients. Importantly, these data may provide some valuable biochemical information and metabolic signatures that will help us to understand the abnormal follicular development in PCOS.

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Raman Spectrum of Follicular Fluid: a Potential Biomarker for Oocyte Developmental Competence in Polycystic Ovary Syndrome

10.21203/rs.3.rs-885748/v1 ◽

2021 ◽

Author(s):

Xin Huang ◽

Ling Hong ◽

Yuanyuan Wu ◽

Miaoxin Chen ◽

Pengcheng Kong ◽

...

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Raman Spectra ◽

Follicular Fluid ◽

Polycystic Ovary ◽

Clinical Pregnancy ◽

Machine Learning Algorithms ◽

Oocyte Development ◽

Metabolic Profiles ◽

Ovary Syndrome

Abstract Background: Polycystic ovary syndrome (PCOS) is a common endocrine and metabolic disorder in reproductive women where abnormal folliculogenesis is considered as a common characteristic. Our aim is to evaluate the potential of follicular fluid (FF) Raman spectra to predict oocyte development and pregnancy outcome, so as to prioritize the best promising oocyte for implantation, reducing both physiological and economical burdens of PCOS patients. In addition, the altered metabolic profiles will be identified to explore the aetiology and pathobiology of PCOS. Methods: In this study, follicular fluid samples obtained from 150 PCOS and 150 non-PCOS women were measured with Raman spectroscopy. Individual Raman spectrum was analyzed to find biologic components contributing to the occurrence of PCOS. More importantly, the Raman spectra of follicular fluid from the 150 PCOS patients were analyzed via machine-learning algorithms to evaluate their predictive value for oocyte development potential and clinical pregnancy. Results: Mean-centered Raman spectra and principal component analysis showed global differences in the footprints of follicular fluid between PCOS and non-PCOS women. Two Raman zones (993-1,165 cm-1 and 1,439-1,678cm-1) were identified for describing the largest variances between the two groups, with the former higher and the latter lower in PCOS FF. The tentative assignments of corresponding Raman bands included phenylalanine and β -carotene. Moreover, it was found that FF, in which oocytes would develop into high-quality blastocysts and obtain high clinical pregnancy rate, were detected with lower quantification of the integration at 993-1,165 cm-1 and higher quantification of the integration at 1,439-1,678 cm-1 in PCOS. In addition, based on Raman spectra of PCOS FF, the machine-learning algorithms via the fully connected artificial neural network (ANN) achieved the overall accuracies of 90% and 74% in correctly assigning oocyte developmental potential and clinical pregnancy, respectively. Conclusions: The study suggests that the PCOS displays unique metabolic profiles in follicular fluid which could be detected by Raman spectroscopy. Specific bands in Raman spectra have the biomarker potential to predict the oocyte development and pregnancy outcome for PCOS patients. Importantly, these data may provide some valuable biochemical information and metabolic signatures that will help us to understand the abnormal follicular development in PCOS.

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Raman spectroscopy of follicular fluid and plasma with machine-learning algorithms for polycystic ovary syndrome screening

Molecular and Cellular Endocrinology ◽

10.1016/j.mce.2020.111139 ◽

2021 ◽

Vol 523 ◽

pp. 111139

Author(s):

Xinyi Zhang ◽

Bo Liang ◽

Jun Zhang ◽

Xinyao Hao ◽

Xiaoyan Xu ◽

...

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Polycystic Ovary Syndrome ◽

Follicular Fluid ◽

Polycystic Ovary ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Ovary Syndrome

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Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

10.36227/techrxiv.17004058.v1 ◽

2021 ◽

Author(s):

Mohammadrahim Kazemzadeh ◽

Colin Hisey ◽

Kamran Zargar ◽

Peter Xu ◽

Neil Broderick

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Raman Spectra ◽

Biomedical Applications ◽

Data Augmentation ◽

Surface Enhanced Raman Spectroscopy ◽

Machine Learning Algorithms ◽

Cancer Tissue ◽

Deep Convolutional Neural Networks ◽

Surface Enhanced Raman

<div>Machine learning has shown great potential for classifying diverse samples in biomedical applications based on their Raman spectra. However, the acquired spectra typically require several preprocessing steps before standard machine learning algorithms can accurately and reliably classify them. To simplify this workflow and enable future growth of this technology, we present a unified solution for classifying biological Raman spectra without any need of prepossessing, including denoising and baseline establishment. This method is developed based on a custom version of a convolutional neural network (CNN) elicited from ResNet architecture, combined with our proposed data augmentation technique. The superiority of this method compared to conventional classification techniques is shown by applying it to Raman spectra of different grades of bladder cancer tissue and surface enhanced Raman spectroscopy (SERS) spectra of various strains of E. Coli extracellular vesicles (EVs). These results show that our method is far more robust compared to its conventional counterparts when dealing with the various kinds of spectral baselines produced by different Raman spectrometers.</div>

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Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms

2020 IEEE Region 10 Symposium (TENSYMP) ◽

10.1109/tensymp50017.2020.9230932 ◽

2020 ◽

Author(s):

Subrato Bharati ◽

Prajoy Podder ◽

M. Rubaiyat Hossain Mondal

Keyword(s):

Machine Learning ◽

Polycystic Ovary Syndrome ◽

Polycystic Ovary ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Ovary Syndrome

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Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Frontiers in Microbiology ◽

10.3389/fmicb.2021.696921 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jia-Wei Tang ◽

Qing-Hua Liu ◽

Xiao-Cong Yin ◽

Ya-Cheng Pan ◽

Peng-Bo Wen ◽

...

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Raman Spectra ◽

Surface Enhanced Raman Spectroscopy ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Learning Methods ◽

Machine Learning Methods ◽

Surface Enhanced ◽

Surface Enhanced Raman

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

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Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

10.36227/techrxiv.17004058 ◽

2021 ◽

Author(s):

Mohammadrahim Kazemzadeh ◽

Colin Hisey ◽

Kamran Zargar ◽

Peter Xu ◽

Neil Broderick

Keyword(s):

Machine Learning ◽

Raman Spectroscopy ◽

Raman Spectra ◽

Biomedical Applications ◽

Data Augmentation ◽

Surface Enhanced Raman Spectroscopy ◽

Machine Learning Algorithms ◽

Cancer Tissue ◽

Deep Convolutional Neural Networks ◽

Surface Enhanced Raman

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Correlation between steroid levels in follicular fluid and hormone synthesis related substances in its exosomes and embryo quality in patients with polycystic ovary syndrome

Reproductive Biology and Endocrinology ◽

10.1186/s12958-021-00749-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Li Yu ◽

Miao Liu ◽

Zhenxin Wang ◽

Te Liu ◽

Suying Liu ◽

...

Keyword(s):

Polycystic Ovary Syndrome ◽

Follicular Fluid ◽

Polycystic Ovary ◽

Mrna Levels ◽

Hormonal Changes ◽

Male Factor ◽

Expression Change ◽

Ovary Syndrome ◽

Steroid Synthesis ◽

Pcos Group

Abstract Background Polycystic ovary syndrome (PCOS) is an endocrine and metabolic disorder with various manifestations and complex etiology. Follicular fluid (FF) serves as the complex microenvironment for follicular development. However, the correlation between the concentration of steroid in FF and the pathogenesis of PCOS is still unclear. Methods Twenty steroid levels in FF from ten patients with PCOS and ten women with male-factor infertility undergoing in vitro fertilization were tested by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in order to explore their possibly correlation with PCOS. Meanwhile, the mRNA levels of core enzymes in steroid synthesis pathway from exosomes of FF were also detected by qPCR. Results The estriol (p < 0.01), estradiol (p < 0.05) and prenenolone (p < 0.01) levels in FF of PCOS group were significantly increased, compared to the normal group, and the progesterone levels (p < 0.05) were decreased in PCOS group. Increased mRNA levels of CYP11A, CYP19A and HSD17B2 of exosomes were accompanied by the hormonal changes in FF. Correlation analysis showed that mRNA levels of CYP11A and HSD17B2 were negatively correlated with percent of top-quality embryos and rate of embryos develop to blastocyst. Conclusion Our results suggest that increased levels of estrogen and pregnenolone in follicular fluid may affect follicle development in PCOS patients, and the mechanism is partially related to HSD17B1, CYP19A1 and CYP11A1 expression change in FF exosomes.

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