Classification of atherosclerotic rabbit aorta samples with an infrared attenuated total reflection catheter and multivariate data analysis

NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis

Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identification of tissue cancerization. In this study, NIR spectroscopic detection equipped with the lab-made NIR probe was performed to in situ explore the change of molecular compositions in breast cancerization, where the diffused NIR spectra were efficiently collected at different locations of cancerous and paracancerous areas. The breast cancerous-paracancerous discriminant model was established based on one-dimensional convolutional neural network (1D-CNN). By optimizing the structure of the neural network, the high classification accuracy (94.67%), recall/sensitivity (95.33%), specificity (94.00%), precision (94.08%) and F1 score (0.9470) were achieved, showing the better discrimination ability and reliability than the K-Nearest Neighbor (KNN, 88.34%, 98.21%, 76.11%, 83.59%, 0.9031) and Fisher Discriminant Analysis (FDA, 90.00%, 96.43%, 81.82%, 87.10%, 0.9153) methods. The experimental results indicate that the application of 1D-CNN can discriminate the cancerous and paracancerous breast tissues, and provide an intelligent method for clinical locating, diagnosis and treatment of breast cancer.

Analysis of human menisci degeneration via infrared attenuated total reflection spectroscopy

Degeneration of human meniscal tissue induces impairment of normal knee functions, and is a highly relevant etiology of knee joint tears and osteoarthritis.

Estimation of articular cartilage properties using multivariate analysis of optical coherence tomography signal

OBJECTIVE

The aim was to investigate the applicability of multivariate analysis of optical coherence tomography (OCT) information for determining structural integrity, composition and mechanical properties of articular cartilage.

DESIGN

Equine osteochondral samples (N = 65) were imaged with OCT, and their total attenuation and backscattering coefficients (μt and μb) were measured. Subsequently, the Mankin score, optical density (OD) describing the fixed charge density, light absorbance in amide I region (Aamide), collagen orientation, permeability, fibril network modulus (Ef) and non-fibrillar matrix modulus (Em) of the samples were determined. Partial least squares (PLS) regression model was calculated to predict tissue properties from the OCT signals of the samples.

RESULTS

Significant correlations between the measured and predicted mean collagen orientation (R(2) = 0.75, P < 0.0001), permeability (R(2) = 0.74, P < 0.0001), mean OD (R(2) = 0.73, P < 0.0001), Mankin scores (R(2) = 0.70, P < 0.0001), Em (R(2) = 0.50, P < 0.0001), Ef (R(2) = 0.42, P < 0.0001), and Aamide (R(2) = 0.43, P < 0.0001) were obtained. Significant correlation was also found between μb and Ef (ρ = 0.280, P = 0.03), but not between μt and any of the determined properties of articular cartilage (P > 0.05).

CONCLUSION

Multivariate analysis of OCT signal provided good estimates for tissue structure, composition and mechanical properties. This technique may significantly enhance OCT evaluation of articular cartilage integrity, and could be applied, for example, in delineation of degenerated areas around cartilage injuries during arthroscopic repair surgery.

Qualitative analysis using Raman spectroscopy and chemometrics: a comprehensive model system for narcotics analysis

The rapid, on-site identification of illicit narcotics, such as cocaine, is hindered by the diverse nature of the samples, which can contain a large variety of materials in a wide concentration range. This sample variance has a very strong influence on the analytical methodologies that can be utilized and in general prevents the widespread use of quantitative analysis of illicit narcotics on a routine basis. Raman spectroscopy, coupled with chemometric methods, can be used for in situ qualitative and quantitative analysis of illicit narcotics; however, careful consideration must be given to dealing with the extensive variety of sample types. To assess the efficacy of combining Raman spectroscopy and chemometrics for the identification of a target analyte under real-world conditions, a large-scale model sample system (633 samples) using a target (acetaminophen) mixed with a wide variety of excipients was created. Materials that exhibit problematic factors such as fluorescence, variable Raman scattering intensities, and extensive peak overlap were included to challenge the efficacy of chemometric data preprocessing and classification methods. In contrast to spectral matching analyte identification approaches, we have taken a chemometric classification model-based approach to account for the wide variances in spectral data. The first derivative of the Raman spectra from the fingerprint region (750-1900 cm(-1)) yielded the best classifications. Using a robust segmented cross-validation method, correct classification rates of better than ∼90% could be attained with regression-based classification, compared to ∼35% for SIMCA. This study demonstrates that even with very high degrees of sample variance, as evidenced by dramatic changes in Raman spectra, it is possible to obtain reasonably reliable identification using a combination of Raman spectroscopy and chemometrics. The model sample set can now be used to validate more advanced chemometric or machine learning algorithms being developed for the identification of analytes such as illicit narcotics.

Application of Fourier transform infrared spectroscopic imaging to the study of effects of age and dietary L-arginine on aortic lesion composition in cholesterol-fed rabbits

Diet-induced atherosclerotic lesions in the descending thoracic segment of rabbit aorta were analysed ex vivo by micro-attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopic imaging. The distribution and chemical character of lipid deposits within the arterial wall near intercostal branch ostia were assessed in histological sections from immature and mature rabbits fed cholesterol with or without l-arginine supplements. Previous studies have shown that both these properties change with age in cholesterol-fed rabbits, putatively owing to changes in the synthesis of nitric oxide (NO) from l-arginine. Immature animals developed lesions at the downstream margin of the branch ostium, whereas lipid deposition was observed at the lateral margins in mature animals. Dietary l-arginine supplements had beneficial effects in mature rabbit aorta, with overall disappearance of the plaques; on the other hand, they caused only a slight decrease of the lipid load in lesions at the downstream margin of the ostium in immature rabbits. ATR-FTIR imaging enabled differences in the lipid to protein density ratio of atherosclerotic lesions caused by age and diet to be visualized. Lipid deposits in immature rabbits showed higher relative absorbance values of their characteristic spectral bands compared with those in immature l-arginine-fed rabbits and mature rabbits. The multivariate methods of principal component analysis (PCA) and factor analysis (FA) were employed, and relevant chemical and structural information were obtained. Two distinct protein constituents of the intima-media layer at different locations of the wall were identified using the method of FA. This approach provides a valuable means of investigating the structure and chemistry of complex heterogeneous systems. It has potential for in vivo diagnosis of pathology.

Application of multivariate data-analysis techniques to biomedical diagnostics based on mid-infrared spectroscopy

The objective of this contribution is to review the application of advanced multivariate data-analysis techniques in the field of mid-infrared (MIR) spectroscopic biomedical diagnosis. MIR spectroscopy is a powerful chemical analysis tool for detecting biomedically relevant constituents such as DNA/RNA, proteins, carbohydrates, lipids, etc., and even diseases or disease progression that may induce changes in the chemical composition or structure of biological systems including cells, tissues, and bio-fluids. However, MIR spectra of multiple constituents are usually characterized by strongly overlapping spectral features reflecting the complexity of biological samples. Consequently, MIR spectra of biological samples are frequently difficult to interpret by simple data-analysis techniques. Hence, with increasing complexity of the sample matrix more sophisticated mathematical and statistical data analysis routines are required for deconvoluting spectroscopic data and for providing useful results from information-rich spectroscopic signals. A large body of work relates to the combination of multivariate data-analysis techniques with MIR spectroscopy, and has been applied by a variety of research groups to biomedically relevant areas such as cancer detection and analysis, artery diseases, biomarkers, and other pathologies. The reported results indeed reveal a promising perspective for more widespread application of multivariate data analysis in assisting MIR spectroscopy as a screening or diagnostic tool in biomedical research and clinical studies. While the authors do not mean to ignore any relevant contributions to biomedical analysis across the entire electromagnetic spectrum, they confine the discussion in this contribution to the mid-infrared spectral range as a potentially very useful, yet underutilized frequency region. Selected representative examples without claiming completeness will demonstrate a range of biomedical diagnostic applications with particular emphasis on the advantageous interaction between multivariate data analysis and MIR spectroscopy.

Nontoxic and chemically stable hollow optical fiber probe for fourier transform infrared spectroscopy

Remote spectroscopy systems based on hollow optical fiber probes are proposed and experimental results using a Fourier transform spectroscope are presented. A hollow optical-fiber probe with a silver and polymer inner coating is used to deliver incoherent light to a target and another separate hollow fiber is used to collect the reflected light. The reflectance spectra of teeth, skin, and oral mucosa were successfully measured with the probe even from surfaces with reflectances lower than 0.5%. The preliminary results obtained using attenuated total reflection spectroscopy are also presented. This remote infrared spectroscope is useful for endoscopic measurements inside the body because it is flexible, durable, nontoxic, and has the low transmission losses associated with hollow-fiber-based probes.

Strategies for label-free optical detection

A large number of methods using direct detection with label-free systems are known. They compete with the well-introduced fluorescence-based methods. However, recent applications take advantage of label-free detection in protein-protein interactions, high-throughput screening, and high-content screening. These new applications require new strategies for biosensors. It becomes more and more obvious that neither the transduction principle nor the recognition elements for the biomolecular interaction process alone determine the quality of the biosensor. Accordingly, the biosensor system has to be considered as a whole. This chapter focuses on strategies to optimize the detection platform and the biomolecular recognition layer. It concentrates on direct detection methods, with special focus on optical transduction. Since even this restriction still leaves a large number of methods, only microrefractometric and microreflectometric methods using planar transducers have been selected for a detailed description and a listing of applications. However, since many review articles on the physical principles exist, the description is kept short. Other methods are just mentioned in brief and for comparison. The outlook and the applications demonstrate the future perspectives of direct optical detection in bioanalytics.

Classification of atherosclerotic rabbit aorta samples by mid-infrared spectroscopy using multivariate data analysis

Atherosclerotic and normal rabbit aorta samples show a marked difference in chemical composition governed by the water, lipid, and protein content. The strongly overlapping infrared absorption features of the different constituents, and the complexity of the tissue matrix, render tissue classification by direct evaluation of molecular spectroscopic characteristics obtained from IR reflectance or attenuated total reflectance (ATR) measurements virtually impossible. We apply multivariate analysis and classification techniques based on partial least squares regression (PLS) and linear discriminant analysis to IR spectroscopic data obtained by IR-ATR measurements and reflectance IR microscopy with high predictive accuracy during blind testing. Training data are collected from atherosclerotic and normal rabbit aorta samples. These results demonstrate the potential of IR spectroscopy combined with multivariate classification strategies for the in-vitro identification of normal and atherosclerotic aorta tissue. The prospect for future in-vivo measurement concepts is also discussed.