Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

Li, Xian Chang; Chen, Hongjun; Zhang, Shiding; Yang, Haijun; Gao, Shanshan; Xu, Huiping; Wang, Lidong; Xu, Ruiping; Zhou, Fuyou; Hu, Jiming; Zhao, Jianhua; Zeng, Haishan

doi:10.1002/jbio.202100010

Cited by 7 publications

(1 citation statement)

References 39 publications

(37 reference statements)

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“…It is widely used in the field of biomedicine and disease diagnosis 15 – 17 . It has been investigated that body fluids contain biomarkers for a variety of clinical diseases and can be used as a reference for disease diagnosis 18 – 21 , and Raman spectroscopy based on serum and urine has been used with good results in the diagnosis of a variety of diseases such as breast cancer 22 , cervical cancer 23 , lung cancer 24 and esophageal cancer 25 . In recent years, significant progress has been made in the study of Raman spectroscopy applied to the diagnosis of kidney diseases.…”

Section: Introductionmentioning

confidence: 99%

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

Zhang

Xue

et al. 2023

Sci Rep

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Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy.

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Section: Introductionmentioning

confidence: 99%

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

Zhang

Xue

et al. 2023

Sci Rep

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Raman spectroscopic analysis of human blood serum of glaucoma patients supplemented with macular pigment carotenoids

Udensi,

Loskutova,

Loughman

et al. 2024

Journal of Biophotonics

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As all major dietary carotenoids are contained in blood, it is a suitable substrate to evaluate their content, in vivo. Following 18‐month supplementation of open‐angle glaucoma patients with macula‐pigment carotenoids (Lutein, Zeaxanthin and Meso‐Zeaxanthin) in the European Nutrition in Glaucoma Management trial, Raman spectroscopic analysis of the carotenoid content of pre‐ and post‐supplementation participant blood serum was carried out, to investigate the systemic impact of the supplementation regimen and explore a more direct way of quantifying this impact using routine blood tests. Using a 532 nm laser source for optimal response, a consistent increase in serum carotenoid concentration was observed in the supplemented serum, highest in patients with initial high baseline carotenoid content. A shift in the 1519 cm−1 carotenoid peak also revealed differences in the carotenoid structural profile of the two groups. The findings highlight the potential of Raman spectroscopy toquantify and differentiate carotenoids directly in blood serum.

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Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study

Fang,

Xu,

et al. 2024

Anal Bioanal Chem

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Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

Cited by 7 publications

References 39 publications

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods

Raman spectroscopic analysis of human blood serum of glaucoma patients supplemented with macular pigment carotenoids

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study

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