Application and Progress of Raman Spectroscopy in Male Reproductive System

Zhang, Feng; Tan, Yiling; Ding, Jinli; Cao, Dishuang; Gong, Yibo; Zhang, Yan; Yang, Jing; Yin, Tailang

doi:10.3389/fcell.2021.823546

Cited by 4 publications

(2 citation statements)

References 74 publications

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“…[47] Moreover, another study also showed that using Raman spectroscopy, increased markers of OSI in the seminal plasma metabolome of men with unexplained infertility were detected (Zhang et al, 2015). [48] However, this method had also limitations. Especially the resolution of obtained spectra causes the bands often overlap.…”

Section: Machine Learning Models Based On Raman Spectra Classifymentioning

confidence: 99%

Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods

Depciuch

Paja

Pancerz

et al. 2023

J Raman Spectroscopy

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Oocytes are surrounded by a fluid called follicular fluid, which provides an essential microenvironment for developing oocytes in human fertility. Various molecules exist in antral follicles, including proteins, steroid hormones, polysaccharides, metabolites, reactive oxygen species, and antioxidants. Oxidative stress is involved in the etiology of defective oocyte development or poor oocyte and embryo quality. Raman spectroscopy, a noninvasive method, can be used for biological diagnostics and direct chemical identification of follicular fluid. Therefore, we measured the oxidative index of follicular fluids and then attempted Raman spectroscopy on the follicular fluids combined with machine learning techniques to identify, detect, and quantify follicular fluid of unexplained infertility-diagnosed women as a safe and effective tool to use as adjacent for clinical studies. This was a retrospective study set in an academic hospital where the patients were selected from an unexplained infertilitydiagnosed population in the in vitro fertilization (IVF) center. Raman spectra of 128 follicular fluid samples (n = 63 control; and 65 unexplained infertility) were obtained. To profile Raman-based results of follicular fluid, oxidative load measurements, multivariate analysis, correlation tests, and six machine learning methods were used. Raman bands associated with oxidative load and amide III and lipids differed significantly. Classification using stacks of Raman signals was applied by random forest, C5.0 decision tree algorithm, k-nearest neighbors, deep neural networks, support vector machine, and XGBoost trees Joanna Depciuch and Zozan Guleken are equal senior authors.

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Section: Machine Learning Models Based On Raman Spectra Classifymentioning

confidence: 99%

Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods

Depciuch

Paja

Pancerz

et al. 2023

J Raman Spectroscopy

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“…Raman spectroscopy, or Raman scattering, is based on the interaction between a laser beam and a sample and enables the measurement of the vibrational energy of the tested sample. Thus, the set of Raman shifts that correspond to the vibrational energies are recorded, and they are called the Raman spectrum, which is considered a "biochemical fingerprint" of the tested sample [10][11][12][13]. The combination of Raman spectroscopy and machine learning has become a powerful diagnostic tool that has shown great potential in the medical field in recent years [14].…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning

Sharaha,

Hania,

Lapidot

et al. 2023

Cells

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Cancer is the most common and fatal disease around the globe, with an estimated 19 million newly diagnosed patients and approximately 10 million deaths annually. Patients with cancer struggle daily due to difficult treatments, pain, and financial and social difficulties. Detecting the disease in its early stages is critical in increasing the likelihood of recovery and reducing the financial burden on the patient and society. Currently used methods for the diagnosis of cancer are time-consuming, producing discomfort and anxiety for patients and significant medical waste. The main goal of this study is to evaluate the potential of Raman spectroscopy-based machine learning for the identification and characterization of precancerous and cancerous cells. As a representative model, normal mouse primary fibroblast cells (NFC) as healthy cells; a mouse fibroblast cell line (NIH/3T3), as precancerous cells; and fully malignant mouse fibroblasts (MBM-T) as cancerous cells were used. Raman spectra were measured from three different sites of each of the 457 investigated cells and analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA). Our results showed that it was possible to distinguish between the normal and abnormal (precancerous and cancerous) cells with a success rate of 93.1%; this value was 93.7% when distinguishing between normal and precancerous cells and 80.2% between precancerous and cancerous cells. Moreover, there was no influence of the measurement site on the differentiation between the different examined biological systems.

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Emerging microfluidic technologies for sperm sorting

Huang

Chen

et al. 2023

Engineered Regeneration

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Application and Progress of Raman Spectroscopy in Male Reproductive System

Cited by 4 publications

References 74 publications

Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods

Analysis of follicular fluid and serum markers of oxidative stress in women with unexplained infertility by Raman and machine learning methods

Early Detection of Pre-Cancerous and Cancerous Cells Using Raman Spectroscopy-Based Machine Learning

Emerging microfluidic technologies for sperm sorting

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