Recognition of breast cancer subtypes using FTIR hyperspectral data.

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.

Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods.

Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithm-based method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.

Monoketonic Curcuminoid-Lidocaine Co-Deliver Using Thermosensitive Organogels: From Drug Synthesis to Epidermis Structural Studies.

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'/G″ 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.

Assessment of bone dose response using ATR-FTIR spectroscopy: A potential method for biodosimetry.

The health care application of ionizing radiation has expanded worldwide during the last several decades. While the health impacts of ionizing radiation improved patient care, inaccurate handling of radiation technology is more prone to potential health risks. Therefore, the present study characterizes the bone dose response using bovine femurs from a slaughterhouse. The gamma irradiation was designed into low-doses (0.002, 0.004 and 0.007 kGy) and high-doses (1, 10, 15, 25, 35, 50 and 60 kGy), all samples received independent doses. The combination of FTIR spectroscopy and PLS-DA allows the detection of differences in the control group and the ionizing dose, as well as distinguishing between high and low radiation doses. In this way, our findings contribute to future studies of the dose response to track ionizing radiation effects on biological systems.

Osteoporosis evaluation through full developed speckle imaging.

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.

Assessment of simulated osteoporosis in alveolar bone using optical coherence tomography.

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.