RESUMO
Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.
Assuntos
Neoplasias da Mama , Humanos , Feminino , Neoplasias da Mama/diagnóstico , Espectroscopia de Infravermelho com Transformada de Fourier/métodos , Análise Discriminante , Análise de Componente Principal , Aprendizado de Máquina , Microambiente TumoralRESUMO
Organogels (ORGs) are remarkable matrices due to their versatile chemical composition and straightforward preparation. This study proposes the development of ORGs as dual drug-carrier systems, considering the application of synthetic monoketonic curcuminoid (m-CUR) and lidocaine (LDC) to treat topical inflammatory lesions. The monoketone curcuminoid (m-CUR) was synthesized by using an innovative method via a NbCl5-acid catalysis. ORGs were prepared by associating an aqueous phase composed of Pluronic F127 and LDC hydrochloride with an organic phase comprising isopropyl myristate (IPM), soy lecithin (LEC), and the synthesized m-CUR. Physicochemical characterization was performed to evaluate the influence of the organic phase on the ORGs supramolecular organization, permeation profiles, cytotoxicity, and epidermis structural characteristics. The physico-chemical properties of the ORGs were shown to be strongly dependent on the oil phase constitution. Results revealed that the incorporation of LEC and m-CUR shifted the sol-gel transition temperature, and that the addition of LDC enhanced the rheological G'/Gâ³ ratio to higher values compared to original ORGs. Consequently, highly structured gels lead to gradual and controlled LDC permeation profiles from the ORG formulations. Porcine ear skin epidermis was treated with ORGs and evaluated by infrared spectroscopy (FTIR), where the stratum corneum lipids were shown to transition from a hexagonal to a liquid crystal phase. Quantitative optical coherence tomography (OCT) analysis revealed that LEC and m-CUR additives modify skin structuring. Data from this study pointed ORGs as promising formulations for skin-delivery.
RESUMO
Osteoporosis is a disease characterized by bone mineral density reduction, weakening the bone structure. Its diagnosis is performed using ionizing radiation, increasing health risk. Optical techniques are safer, due to non-ionizing radiation use, but limited to the analyses of bone tissue. This limitation may be circumvented in the oral cavity. In this work we explored the use of laser speckle imaging (LSI) to differentiate the sound and osteoporotic maxilla and mandible bones in an in vitro model. Osteoporosis lesions were simulated with acid attack. The samples were evaluated by optical profilometry and LSI, using a custom software. Two image parameters were evaluated, speckle contrast ration and patches ratio. With the speckle contrast ratio, it was possible to differentiate sound from osteoporotic tissue. From speckle patches ratio it was observed a negative correlation with the roughness parameter. LSI is a promissory technique for assessment of osteoporosis lesions on alveolar bone.
Assuntos
Diagnóstico por Imagem , Osteoporose , Humanos , Osteoporose/diagnóstico por imagemRESUMO
Steoporosis is a skeletal disorder that compromises bone resistance and its diagnosis is usually performed using dual energy X-ray absorptiometry. Thus, the search for efficient diagnostic methods that do not involve the emission of ionizing radiation is necessary. This study proposed to use the Optical Coherence Tomography (OCT) to evaluate osteoporosis in alveolar bone. Osteoporosis lesions is simulated in vitro in porcine bones, and imaging is performed by OCT and micro-computed tomography (micro-CT). A developed algorithm is proposed to calculate the optical attenuation coefficient ( µ t ), mean optical attenuation coefficient ( µ¯t ), integrated reflectivity (ΔR) and bone density ( BD). The µ¯t , ΔR and BD parameters shows a good correlation to micro-CT parameters (bone volume/tissue volume and total porosity). The µ t and µ¯t methods are negatively impacted by non-uniform intensities distribution in osteoporosis images. In conclusion, BD and ΔR analysis demonstrates to be potential techniques for diagnosis and monitoring of osteoporosis using OCT.