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- Invasive and minimally invasive optical detection of pigment accumulation in brain cortexPublication . Oliveira, Luís; Gonçalves, Tânia; Pinheiro, Maria; Fernandes, Luís; Martins, Inês; Silva, Hugo; Oliveira, Hélder; Tuchin, Valery; Oliveira, LuísThe estimation of the spectral absorption coefficient of biological tissues provides valuable information that can be used in diagnostic procedures. Such estimation can be made using direct calculations from invasive spectral measurements or though machine learning algorithms based on noninvasive or minimally invasive spectral measurements. Since in a noninvasive approach, the number of measurements is limited, an exploratory study to investigate the use of artificial generated data in machine learning techniques was performed to evaluate the spectral absorption coefficient of the brain cortex. Considering the spectral absorption coefficient that was calculated directly from invasive measurements as reference, the similar spectra that were estimated through different machine learning approaches were able to provide comparable information in terms of pigment, DNA and blood contents in the cortex. The best estimated results were obtained based only on the experimental measurements, but it was also observed that artificially generated spectra can be used in the estimations to increase accuracy, provided that a significant number of experimental spectra are available both to generate the complementary artificial spectra and to estimate the resulting absorption spectrum of the tissue.
- Tissue Spectroscopy and Optical Clearing of Colorectal Mucosa in the Pursuit of New Cancer Diagnostic ApproachesPublication . Fernandes, Luís; Silva, Hugo; Martins, Inês; Carvalho, Sónia; Carneiro, Isa; Henrique, Rui; Tuchin, Valery V.; Oliveira, LuísIn 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.