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Orientador(es)
Resumo(s)
In this paper we present three studies that demonstrate the applicability of spectroscopy methods and optical clearing treatments in pathology identification and monitoring. In the first study, by obtaining the absorption spectra of human healthy and pathological (adenocarcinoma) colorectal mucosa tissues, it was possible to identify a higher content of a pigment in the diseased tissues. This study also shows that machine learning methods can be used to reach the same differentiated results in vivo through diffuse reflectance spectroscopy. In the second study, the combination of collimated transmittance spectroscopy with optical clearing treatments allowed to obtain the diffusion coefficients of glucose in healthy and pathological colorectal mucosa as: Dglucose=5.8x10–7 cm2/s and Dglucose=4.4x10–7 cm2/s, respectively. This study also demonstrated that the diseased tissues contains about 5% more mobile water than the healthy tissues. The third study was performed to evaluate the protein dissociation mechanism of optical clearing. By treating both healthy and pathological colorectal mucosa tissues with 93%-glycerol, a protein dissociation rate of about 3 times higher was obtained for the pathological mucosa. All the discriminating parameters that result from these studies can be obtained in the in vivo situation through diffuse reflectance spectroscopy and further studies to evaluate their values in different stages of cancer progression are of great importance to develop disease monitoring protocols.
Descrição
Palavras-chave
Colorectal cancer Enhanced tissue spectroscopy Cancer detection Optical clearing mechanisms Tissue optical clearing
Contexto Educativo
Citação
Fernandes, L., Silva, H., Martins, I., Carvalho, S., Carneiro, I., Henrique, R., Tuchin, V., Oliveira, L. M. (2021). Tissue spectroscopy and optical clearing of colorectal mucosa in the pursuit of new cancer diagnostic approaches. J. Biomed. Phot. & Eng., vol. 7, no. 4, 040302. DOI: 10.18287/JBPE21.07.040302
Editora
Samara University