Pub Date : 2021-06-30DOI: 10.18287/jbpe21.07.020309
I. Matveeva, L. Bratchenko, O. Myakinin, E. Tupikova, V. Zakharov
Changes in the concentration of free amino acids in biological tissues is a sign of impaired protein metabolism in patients with cancer. Recently, Raman spectroscopy has been used for early diagnostics of oncological diseases. The concentrations of individual components of biological tissue (for instance, the concentrations of amino acids) can be obtained by decomposing the tissue Raman spectrum. This study was designed to evaluate the effect of noise in the Raman spectra of individual amino acids on the result of the decomposition of the spectra of an amino acid mixture. As a decomposition method, we used Multivariate Curve Resolution-Alternating Least Squares (MCR–ALS) analysis and investigate experimental Raman spectra of amino acids and mathematically simulated Raman spectra of amino acid mixtures. Noise with different signal-to-noise ratios (SNR) was artificially added to both the experimental spectra of pure amino acids and the spectra of the mixtures. Concentration values for each amino acid obtained as a result of applying the MCR–ALS analysis have been compared with the corresponding true values and the correlation coefficients have been calculated. The results show a less pronounced negative effect of noise in the case when the spectra of pure amino acids (which were used as a basis for the MCR–ALS analysis) are noisy, and a more pronounced negative effect when the spectrum of the mixture is noisy. The accuracy of reconstruction of an amino acid is also negatively affected by strong background fluorescence in the amino acid spectrum. Moreover, the results indicate that using the basis spectra with a high SNR (SNR = 5) makes it possible to successfully estimate the amino acid concentrations in a mixture even when the Raman spectrum of the mixture is noisy and has a low SNR (SNR < 5).
{"title":"The Effect of Noise in Raman Spectra on the Reconstruction of the Concentration of Amino Acids in the Mixture by Multivariate Curve Resolution (MCR) Analysis","authors":"I. Matveeva, L. Bratchenko, O. Myakinin, E. Tupikova, V. Zakharov","doi":"10.18287/jbpe21.07.020309","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020309","url":null,"abstract":"Changes in the concentration of free amino acids in biological tissues is a sign of impaired protein metabolism in patients with cancer. Recently, Raman spectroscopy has been used for early diagnostics of oncological diseases. The concentrations of individual components of biological tissue (for instance, the concentrations of amino acids) can be obtained by decomposing the tissue Raman spectrum. This study was designed to evaluate the effect of noise in the Raman spectra of individual amino acids on the result of the decomposition of the spectra of an amino acid mixture. As a decomposition method, we used Multivariate Curve Resolution-Alternating Least Squares (MCR–ALS) analysis and investigate experimental Raman spectra of amino acids and mathematically simulated Raman spectra of amino acid mixtures. Noise with different signal-to-noise ratios (SNR) was artificially added to both the experimental spectra of pure amino acids and the spectra of the mixtures. Concentration values for each amino acid obtained as a result of applying the MCR–ALS analysis have been compared with the corresponding true values and the correlation coefficients have been calculated. The results show a less pronounced negative effect of noise in the case when the spectra of pure amino acids (which were used as a basis for the MCR–ALS analysis) are noisy, and a more pronounced negative effect when the spectrum of the mixture is noisy. The accuracy of reconstruction of an amino acid is also negatively affected by strong background fluorescence in the amino acid spectrum. Moreover, the results indicate that using the basis spectra with a high SNR (SNR = 5) makes it possible to successfully estimate the amino acid concentrations in a mixture even when the Raman spectrum of the mixture is noisy and has a low SNR (SNR < 5).","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44738132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.18287/jbpe21.07.020308
I. Bratchenko, Y. Khristoforova, L. Bratchenko, A. Moryatov, S. Kozlov, E. Borisova, V. Zakharov
In this work, Raman and autofluorescence spectroscopy in the near-infrared region has been used for examining amelanotic melanoma as the most dangerous type of malignant melanoma. There were 9 patients with amelanotic melanoma, 60 with pigmented melanoma and 120 with basal cell carcinoma enrolled in this study. We studied 9 amelanotic melanoma cases to differentiate them from basal cell carcinoma (n = 120) and pigmented malignant melanoma (n = 60) using portable spectroscopy setup with laser excitation source at 785 nm and low-cost spectrometer. The spectra of the different tumor type were classified using projection on the latent structure analysis with 10-Fold cross-validation. The results of the tumor classification were presented using box-plot diagrams and ROC analysis. We obtained 0.53 and 0.88 ROC AUCs for distinguishing amelanotic melanoma versus (1) pigmented melanoma and (2) basal cell carcinoma respectively based on the joint autofluorescence and Raman spectroscopy analysis that allowed one to diagnose amelanotic melanoma as true melanoma but no basal cell carcinoma.
{"title":"Optical Biopsy of Amelanotic Melanoma with Raman and Autofluorescence Spectra Stimulated by 785 nm Laser Excitation","authors":"I. Bratchenko, Y. Khristoforova, L. Bratchenko, A. Moryatov, S. Kozlov, E. Borisova, V. Zakharov","doi":"10.18287/jbpe21.07.020308","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020308","url":null,"abstract":"In this work, Raman and autofluorescence spectroscopy in the near-infrared region has been used for examining amelanotic melanoma as the most dangerous type of malignant melanoma. There were 9 patients with amelanotic melanoma, 60 with pigmented melanoma and 120 with basal cell carcinoma enrolled in this study. We studied 9 amelanotic melanoma cases to differentiate them from basal cell carcinoma (n = 120) and pigmented malignant melanoma (n = 60) using portable spectroscopy setup with laser excitation source at 785 nm and low-cost spectrometer. The spectra of the different tumor type were classified using projection on the latent structure analysis with 10-Fold cross-validation. The results of the tumor classification were presented using box-plot diagrams and ROC analysis. We obtained 0.53 and 0.88 ROC AUCs for distinguishing amelanotic melanoma versus (1) pigmented melanoma and (2) basal cell carcinoma respectively based on the joint autofluorescence and Raman spectroscopy analysis that allowed one to diagnose amelanotic melanoma as true melanoma but no basal cell carcinoma.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44899262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-30DOI: 10.18287/jbpe21.07.020307
I. Shatalov, A. Shatalova, L. Plotnikova, A. Shleikin
Present paper describes features of the component composition in the secondary structure of BSA–containing protein complexes isolated from ultra-pasteurized (UHT), sterilized (SHT) and powdered (DRY) milk. We have found β – sheets to present in all complexes investigated. However, the smallest number of such components have been revealed in samples derived from sterilized milk with less β – sheets in 1621–1626 cm–1 region. The composition study of the complexes originated from UHT milk has shown random coils to be the rarest in them. When considering the structure of the complexes isolated from powdered milk, the α – 310 – heliсes were more characteristic for such samples, then the α – helix. Moreover, during spray–drying, the number of random structures increase with a simultaneous decrease in the number of β – sheets, whereas in UHT – and SHT – processing the number of random structures is inversely proportional to the number of α – helices.
{"title":"Features of the Secondary Structure of BSA – Containing Protein Complexes, Isolated from Milk of High Temperature Processing","authors":"I. Shatalov, A. Shatalova, L. Plotnikova, A. Shleikin","doi":"10.18287/jbpe21.07.020307","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020307","url":null,"abstract":"Present paper describes features of the component composition in the secondary structure of BSA–containing protein complexes isolated from ultra-pasteurized (UHT), sterilized (SHT) and powdered (DRY) milk. We have found β – sheets to present in all complexes investigated. However, the smallest number of such components have been revealed in samples derived from sterilized milk with less β – sheets in 1621–1626 cm–1 region. The composition study of the complexes originated from UHT milk has shown random coils to be the rarest in them. When considering the structure of the complexes isolated from powdered milk, the α – 310 – heliсes were more characteristic for such samples, then the α – helix. Moreover, during spray–drying, the number of random structures increase with a simultaneous decrease in the number of β – sheets, whereas in UHT – and SHT – processing the number of random structures is inversely proportional to the number of α – helices.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47089548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.18287/jbpe21.07.020306
M. Aref, R. Abdlaty, M. Abbass, Ibrahim H. Aboughaleb, A. Nassar, A. Youssef
Background and Objective: Thermal ablation modalities such as Radiofrequency ablation (RFA) / Microwave ablation (MWA) are deliberately used for marginally invasive tumor removal by escalating tissue temperature. For precise tumor extinguish, thermal ablation outcomes need routine monitoring for tissue necrosis in a challenging research task. The study aims to exploit hyperspectral imaging (HSI) to evaluate the impact of the liver tissue ablation. Materials and Methods: RFA with temperature range (≥80 °C) was accomplished on the ex vivo animal liver and evaluated using a spectral camera (400~1000 nm). The spectral signatures were extracted from the HSI data after the following processing steps: capturing three spectral data cubes for each liver sample with total 7-samples (before ablation, after ablation, and after ablation with sample slicing) using an HSI optical configuration. The custom HSI processing comprises “Top-hat and Bottom-hat transform” combined with “watershed transform” image segmentation to increase the intensity for a region of interest (ROI) of the investigated tissue, linking spectral and spatial data. Additionally, statistical analysis for HSI data was performed to exclusively select the best spectral band that discriminates between the normal, thermally-damaged, and ablated liver regions. Results: The variation of the optical parameters for the investigated liver samples provides variable interaction with the light diffuse reflection (Ŗd) over the spectrum range (400~1000 nm). Where, the extracting spectral information of the various tissue zones from the induced RFA linked to the hemoglobin, methemoglobin, and water permits variations. The generated spectral image after image enhancement utilizing “Top-hat and Bottom-hat transform” followed by “watershed segmentation”, showed high contrast between normal and thermal regions at a wavelength (600 nm). However, the wavelength (900 nm) shows a high variance between the normal and ablated regions. Finally, delineation of the thermal and ablated regions on the complemented enhanced image. Conclusion: HSI is considered a promising optical noninvasive technique for monitoring the RFA toward enhancing the ablation-based treatment for liver tumor outcomes.
{"title":"Optical Signature Analysis of Liver Ablation Stages Exploiting Spatio-Spectral Imaging","authors":"M. Aref, R. Abdlaty, M. Abbass, Ibrahim H. Aboughaleb, A. Nassar, A. Youssef","doi":"10.18287/jbpe21.07.020306","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020306","url":null,"abstract":"Background and Objective: Thermal ablation modalities such as Radiofrequency ablation (RFA) / Microwave ablation (MWA) are deliberately used for marginally invasive tumor removal by escalating tissue temperature. For precise tumor extinguish, thermal ablation outcomes need routine monitoring for tissue necrosis in a challenging research task. The study aims to exploit hyperspectral imaging (HSI) to evaluate the impact of the liver tissue ablation. Materials and Methods: RFA with temperature range (≥80 °C) was accomplished on the ex vivo animal liver and evaluated using a spectral camera (400~1000 nm). The spectral signatures were extracted from the HSI data after the following processing steps: capturing three spectral data cubes for each liver sample with total 7-samples (before ablation, after ablation, and after ablation with sample slicing) using an HSI optical configuration. The custom HSI processing comprises “Top-hat and Bottom-hat transform” combined with “watershed transform” image segmentation to increase the intensity for a region of interest (ROI) of the investigated tissue, linking spectral and spatial data. Additionally, statistical analysis for HSI data was performed to exclusively select the best spectral band that discriminates between the normal, thermally-damaged, and ablated liver regions. Results: The variation of the optical parameters for the investigated liver samples provides variable interaction with the light diffuse reflection (Ŗd) over the spectrum range (400~1000 nm). Where, the extracting spectral information of the various tissue zones from the induced RFA linked to the hemoglobin, methemoglobin, and water permits variations. The generated spectral image after image enhancement utilizing “Top-hat and Bottom-hat transform” followed by “watershed segmentation”, showed high contrast between normal and thermal regions at a wavelength (600 nm). However, the wavelength (900 nm) shows a high variance between the normal and ablated regions. Finally, delineation of the thermal and ablated regions on the complemented enhanced image. Conclusion: HSI is considered a promising optical noninvasive technique for monitoring the RFA toward enhancing the ablation-based treatment for liver tumor outcomes.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46004820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-02DOI: 10.18287/jbpe21.07.020305
I. Sergeeva, Anna Petrova, A. Shlenskaya, G. Petrova
Real–time digestion of type I collagen molecules by bacterial collagenase from Clostridium histolyticum has been monitored using Dynamic Light Scattering method. Time dependencies for translation diffusion coefficient (Dt) and hydrodynamic radius (RH) on were obtained for pure “collagen + collagenase” Tris–HCl buffer solution at different temperatures and for solutions with added CaCl2, ZnCl2 and MgCl2 and EDTA. It was shown that digestion of type I collagen molecules by bacterial collagenase is the first-order reaction. Reaction rate coefficients were calculated.
{"title":"Influence of Activators and Inhibitors on the Collagenase with Collagen Interaction Monitored by Dynamic Light Scattering in Solutions","authors":"I. Sergeeva, Anna Petrova, A. Shlenskaya, G. Petrova","doi":"10.18287/jbpe21.07.020305","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020305","url":null,"abstract":"Real–time digestion of type I collagen molecules by bacterial collagenase from Clostridium histolyticum has been monitored using Dynamic Light Scattering method. Time dependencies for translation diffusion coefficient (Dt) and hydrodynamic radius (RH) on were obtained for pure “collagen + collagenase” Tris–HCl buffer solution at different temperatures and for solutions with added CaCl2, ZnCl2 and MgCl2 and EDTA. It was shown that digestion of type I collagen molecules by bacterial collagenase is the first-order reaction. Reaction rate coefficients were calculated.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48905979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.18287/jbpe21.07.020001
{"title":"Issue Info: J of Biomedical Photonics & Eng 7(2)","authors":"","doi":"10.18287/jbpe21.07.020001","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020001","url":null,"abstract":"","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47771030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-01DOI: 10.18287/jbpe21.07.020000
{"title":"Cover: J of Biomedical Photonics & Eng 7(2)","authors":"","doi":"10.18287/jbpe21.07.020000","DOIUrl":"https://doi.org/10.18287/jbpe21.07.020000","url":null,"abstract":"","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44750403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-15DOI: 10.18287/jbpe21.07.010001
{"title":"Issue Info: J of Biomedical Photonics & Eng 7(1)","authors":"","doi":"10.18287/jbpe21.07.010001","DOIUrl":"https://doi.org/10.18287/jbpe21.07.010001","url":null,"abstract":"","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68179192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.18287/JBPE20.06.040201
Ala H. Sabeeh, V. Tuchin
Modern studies of the penetration of light into biological tissues is becoming very important in various medical applications. This is an important factor for determining the optical dose in many diagnostic and therapeutic procedures. The absorption and scattering properties of the tissue under study determine how deeply the light will penetrate into the tissue. However, these optical properties are highly dependent on the wavelength of the light source and tissue condition. This overview paper analyzes the transmission of light through different areas of human and animal head tissues, and the optimal laser wavelength and power density required to reach different parts of the brain are determined using lasers with different wavelengths by comparing the distribution of fluence, penetration depth and the mechanism of interaction between laser light and head tissues. The power variation in different regions of the head is presented, as estimated using Monte Carlo (MC) simulations. Data are analyzed for the absorption and scattering coefficients of the head tissue layers (scalp, skull, brain), calculated using integrating sphere measurements and inverse problem solving algorithms such as inverse MC (IMC) and adding-doubling (IAD). This study not only offered a quantitative comparison between wavelengths in terms of light transmission efficiency, but also anticipated the exciting opportunity for online, accurate and visible optimization of LLLT lighting parameters.
{"title":"Recent Advances in the Laser Radiation Transport through the Head Tissues of Humans and Animals – A Review","authors":"Ala H. Sabeeh, V. Tuchin","doi":"10.18287/JBPE20.06.040201","DOIUrl":"https://doi.org/10.18287/JBPE20.06.040201","url":null,"abstract":"Modern studies of the penetration of light into biological tissues is becoming very important in various medical applications. This is an important factor for determining the optical dose in many diagnostic and therapeutic procedures. The absorption and scattering properties of the tissue under study determine how deeply the light will penetrate into the tissue. However, these optical properties are highly dependent on the wavelength of the light source and tissue condition. This overview paper analyzes the transmission of light through different areas of human and animal head tissues, and the optimal laser wavelength and power density required to reach different parts of the brain are determined using lasers with different wavelengths by comparing the distribution of fluence, penetration depth and the mechanism of interaction between laser light and head tissues. The power variation in different regions of the head is presented, as estimated using Monte Carlo (MC) simulations. Data are analyzed for the absorption and scattering coefficients of the head tissue layers (scalp, skull, brain), calculated using integrating sphere measurements and inverse problem solving algorithms such as inverse MC (IMC) and adding-doubling (IAD). This study not only offered a quantitative comparison between wavelengths in terms of light transmission efficiency, but also anticipated the exciting opportunity for online, accurate and visible optimization of LLLT lighting parameters.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47094978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.18287/JBPE20.06.040302
S. Salem, V. Tuchin
This study presents a theoretical model by using the COMSOL Multiphysics ® software to describe the behavior of magnetic nanoparticles through blood stream in a branched blood vessel under the influence of cylindrical permanent magnet that is located outside the vessel. The magnet is placed at one branched vessel to attract the magnetic particles towards targeted locations. The fluid (blood) is assumed being Newtonian; its flow is incompressible and laminar. Magnetic nanoparticles, such as superparamagnetic iron oxide (Fe 3 O 4 ) nanoparticles are used in this theoretical study. The mechanisms of magnetic nanoparticles travelling in the blood stream under influence of a localized static magnetic field are numerically studied. The equations of motion for particles in the flow are governed by a combination of magnetic equations for the permanent magnetic field and the Navier-Stokes equations for fluid.
{"title":"Magnetic Particle Trapping in a Branched Blood Vessel in the Presence of Magnetic Field","authors":"S. Salem, V. Tuchin","doi":"10.18287/JBPE20.06.040302","DOIUrl":"https://doi.org/10.18287/JBPE20.06.040302","url":null,"abstract":"This study presents a theoretical model by using the COMSOL Multiphysics ® software to describe the behavior of magnetic nanoparticles through blood stream in a branched blood vessel under the influence of cylindrical permanent magnet that is located outside the vessel. The magnet is placed at one branched vessel to attract the magnetic particles towards targeted locations. The fluid (blood) is assumed being Newtonian; its flow is incompressible and laminar. Magnetic nanoparticles, such as superparamagnetic iron oxide (Fe 3 O 4 ) nanoparticles are used in this theoretical study. The mechanisms of magnetic nanoparticles travelling in the blood stream under influence of a localized static magnetic field are numerically studied. The equations of motion for particles in the flow are governed by a combination of magnetic equations for the permanent magnetic field and the Navier-Stokes equations for fluid.","PeriodicalId":52398,"journal":{"name":"Journal of Biomedical Photonics and Engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47490466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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