Study of Blood Serum in Rats with Transplanted Cholangiocarcinoma Using Raman Spectroscopy

Mankova, A.A.; Черкасова, О. П.; Lazareva, Ekaterina N.; Bucharskaya, Alla B.; Dyachenko, Polina A.; Kistenev, Yu. V.; Vrazhnov, Denis A.; Skiba, Victor; Tuchin, Valery V.; Shkurinov, A.P.

doi:10.1134/s0030400x20070115

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…Many feature-extraction techniques have different benefits; among the most reliable are loadings matrix analysis in PCA (LMPCA), and Shapley values [32,33]. PCA has proven its effectiveness in multiple studies of THz, IR, and Raman spectra [12,13,16,34]. Yet LMPCA is a linear method, which might not be effective for complex data but gives repeatable results.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning

et al. 2022

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In this research, an experimental U87 glioblastoma small animal model was studied. The association between glioblastoma stages and the spectral patterns of mouse blood serum measured in the terahertz range was analyzed by terahertz time-domain spectroscopy (THz-TDS) and machine learning. The THz spectra preprocessing included (i) smoothing using the Savitsky–Golay filter, (ii) outlier removing using isolation forest (IF), and (iii) Z-score normalization. The sequential informative feature-selection approach was developed using a combination of principal component analysis (PCA) and a support vector machine (SVM) model. The predictive data model was created using SVM with a linear kernel. This model was tested using k-fold cross-validation. Achieved prediction accuracy, sensitivity, specificity were over 90%. Also, a relation was established between tumor size and the THz spectral profile of blood serum samples. Thereby, the possibility of detecting glioma stages using blood serum spectral patterns in the terahertz range was demonstrated.

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Section: Introductionmentioning

confidence: 99%

Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning

et al. 2022

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“…DXR Raman microscope (Thermo Fisher Scientific, Waltham, MA USA) with a magnification of 10×, excitation wavelengths of 532 nm, and range 80–4000 cm –1 was used. Each sample of blood serum was a droplet with a volume of 10 μL placed on a special aluminum plate [ 51 , 52 ]. This plate had identical holes in the form of a funnel with a diameter of 5 and a depth of 2 mm (see Figure S1 ).…”

Section: Methodsmentioning

confidence: 99%

“…Also, we applied adaptive iteratively reweighed penalized least squares method to remove background noise [ 53 ]. A Python library BaselineRemoval contains several methods to remove the noise, caused by fluorescence in Raman spectra, and according to our previous findings [ 54 ], the airPLS algorithm [ 51 ] has a good performance. Principal component analysis (PCA) was used to extract informative features [ 55 , 56 ].…”

Section: Methodsmentioning

confidence: 99%

Discovering Glioma Tissue through Its Biomarkers’ Detection in Blood by Raman Spectroscopy and Machine Learning

et al. 2023

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The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.

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A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

Yang

Arakawa

2023

Marine Pollution Bulletin

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Study of Blood Serum in Rats with Transplanted Cholangiocarcinoma Using Raman Spectroscopy

Cited by 9 publications

References 37 publications

Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning

Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning

Discovering Glioma Tissue through Its Biomarkers’ Detection in Blood by Raman Spectroscopy and Machine Learning

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

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