Lung neoplasm diagnostics using Raman spectroscopy and autofluorescence analysis

Artemyev, Dmitry N.; Zakharov, Valery P.; Bratchenko, Ivan А.; Myakinin, Oleg O.; Kornilin, Dmitriy V.; Kozlov, Sergey V.; Moryatov, Alexander A.

doi:10.18287/jbpe-2015-1-1-70

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…In the last 30 years, the number of newly discovered lung neoplasm has significantly increased. Early diagnosis of lung neoplasm is helpful for the treatment of tumors [ 72 ]. In our data, 132 miRNAs are associated with the occurrence and development of lung neoplasm.…”

Section: Resultsmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA–disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA–disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease–miRNA association prediction model.

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Section: Resultsmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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“…This approach is free of the restrictions related to the time of the signal acquisition, inherent in the Raman scattering method, and admits the real-time operation without damaging the studied tissue, which allows for fast and economical solution of medical practice problems. The application of AF method was demonstrated in a number of papers for the cancer diagnostics in larynx [9] and lung [10]. The AF spectroscopy was also applied in wound healing monitoring [11] and eye tissue studies [12].…”

Section: Introductionmentioning

confidence: 99%

Method of autofluorescence diagnostics of skin neoplasms in the near infrared region

Khristoforova

Bratchenko

Artemyev

et al. 2015

JBPE

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Abstract. We propose a method for the diagnostics of skin neoplasms, based on the analysis of the changes in the autofluorescence spectra in the near infrared range. The autofluorescence was excited by means of the laser radiation with the wavelength 785 nm for ex vivo and in vivo studies with subsequent exponential approximation of its spectrum. The quantitative and qualitative criteria for the neoplasm type differentiation by the change of the curvature and the rate of decrease of the approximating curve are found. It is shown that the proposed approach allows the diagnostics of malignant melanomas with the accuracy of 88.4% for ex vivo studies, and 86.2 % for in vivo ones.

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Impact of e‐cigarette liquid on porcine lung tissue—Ex vivo confocal Raman micro‐spectroscopy study

Jaafar,

Albarazanchi,

Kadhim

et al. 2023

Journal of Biophotonics

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Ex vivo porcine lung immersed in e‐liquid was investigated in‐depth using confocal Raman micro‐spectroscopy to assess the e‐liquid influence on the lung. It was found that lung‐related Raman band intensities at 1002, 1548, 1618 and 1655 cm−1 increased after first and second treatments except the surface, which was attributed to the well‐known optical clearing effect due to alveoli filling with e‐liquid resulting in light scattering reduction. The autofluorescence enhancement was explained by oxidative stress induced in lung during exposure to e‐liquid. Moreover, e‐liquid induced collagen dehydration was revealed by the I937/I926 Raman band intensity ratio change. The effect was enhanced after the second treatment of the same lung tissue that indicates the possibility of multi‐step OC treatment. We hypothesize that the nicotine‐flavour free e‐liquids containing glycerol and propylene glycol could potentially be used in clinical protocols as optical clearing agent for enhanced in‐depth Raman‐guided bronchoscopy.This article is protected by copyright. All rights reserved.

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Lung neoplasm diagnostics using Raman spectroscopy and autofluorescence analysis

Cited by 8 publications

References 18 publications

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Method of autofluorescence diagnostics of skin neoplasms in the near infrared region

Impact of e‐cigarette liquid on porcine lung tissue—Ex vivo confocal Raman micro‐spectroscopy study

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