Fourier Transform Infrared Spectroscopic Imaging and Multivariate Regression for Prediction of Proteoglycan Content of Articular Cartilage

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O –stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used

Vibrational spectroscopy of articular cartilage

Abstract Articular cartilage is a connective tissue that is located at the ends of long bones. Type II collagen, proteoglycans, water, and chondrocytes are the main constituents of articular cartilage. Osteoarthritis, the most common joint disease in the world, causes degenerative changes in articular cartilage tissue. Fourier transform infrared (FTIR), Raman, and near infrared (NIR) spectroscopic techniques offer versatile tools to assess biochemical composition and quality of articular cartilage. These vibrational spectroscopic techniques can be used to broaden our understanding about the compositional changes during osteoarthritis, and they also hold promise in disease diagnostics. In this article, the current literature of articular cartilage spectroscopic studies is reviewed

Discrimination of melanoma cell lines with Fourier Transform Infrared (FTIR) spectroscopy

Abstract Among skin cancers, melanoma is the lethal form and the leading cause of death in humans. Melanoma begins in melanocytes and is curable at early stages. Thus, early detection and evaluation of its metastatic potential are crucial for effective clinical intervention. Fourier transform infrared (FTIR) spectroscopy has gained considerable attention due to its versatility in detecting biochemical and biological features present in the samples. Changes in these features are used to differentiate between samples at different stages of the disease. Previously, FTIR spectroscopy has been mostly used to distinguish between healthy and diseased conditions. With this study, we aim to discriminate between different melanoma cell lines based on their FTIR spectra. Formalin-fixed paraffin embedded samples from three melanoma cell lines (IPC-298, SK-MEL-30 and COLO-800) were used. Statistically significant differences were observed in the prominent spectral bands of three cell lines along with shifts in peak positions. A partial least square discriminant analysis (PLS-DA) model built for the classification of three cell lines showed an overall accuracy of 92.6% with a sensitivity of 85%, 95.75%, 96.54%, and specificity of 97.80%, 92.14%, 98.64% for the differentiation of IPC-298, SK-MEL-30, and COLO-800, respectively. The results suggest that FTIR spectroscopy can differentiate between different melanoma cell lines and thus potentially characterize the metastatic potential of melanoma

Optimization of measurement mode and sample processing for FTIR microspectroscopy in skin cancer research

Abstract The use of Fourier Transform Infrared (FTIR) microspectroscopy to study cancerous cells and tissues has gained popularity due to its ability to provide spatially resolved information at the molecular level. Transmission and transflection are the commonly used measurement modes for FTIR microspectroscopy, and the tissue samples measured in these modes are often paraffinized or deparaffinized. Previous studies have shown that variability in the spectra acquired using different measurement modes and sample processing methods affect the result of the analysis. However, there is no protocol that standardizes the mode of measurement and sample processing method to achieve the best classification result. This study compares the spectra of primary (IPC-298) and metastatic (SK-MEL-30) melanoma cell lines acquired in both transmission and transflection modes using paraffinized and deparaffinized samples to determine the optimal combination for accurate classification. Significant differences were observed in the spectra of the same cell line measured in different modes and with or without deparaffinization. The PLS-DA model built for the classification of two cell lines showed high accuracy in each case, suggesting that both modes and sample processing alternatives are suitable for differentiating cultured cell samples using supervised multivariate analysis. The biochemical information contained in the cells capable of discriminating two melanoma cell lines is present regardless of mode or sample type used. However, the paraffinized samples measured in transflection mode provided the best classification

The use of Fourier Transform Infrared (FTIR) spectroscopy in skin cancer research:a systematic review

Abstract Skin cancers are one of the most frequently occurring diseases in humans that pose severe health issues. Fourier Transform Infrared (FTIR) spectroscopy in cancer research has gained considerable attention because of its ability to provide biochemical information in addition to being compatible with traditional histopathology. With this review, we aim to identify all skin cancer studies which have been conducted using FTIR spectroscopy and depict different methodologies that have been used to analyze FTIR spectroscopic data of skin cancers. We conducted the systematic review following PRISMA guidelines for which three databases, Scopus, PubMed and Web of Science, were searched from commencement to 16 January 2019. All the studies which used FTIR spectroscopy for skin cancer research were included in the review. A total of 35 studies were found eligible to be included in the review. Of these, 21 studies were based on melanoma, 6 studies on BCC, 2 studies on SCC, and 2 on lymphocytes. The remaining 4 studies aimed to differentiate between various skin cancer types. The potential of FTIR spectroscopy for many relevant aspects of skin cancer research has already been demonstrated, but more work is needed to establish FTIR spectroscopy as a routine method in the field

Orientation anisotropy of quantitative MRI relaxation parameters in ordered tissue

Abstract In highly organized tissues, such as cartilage, tendons and white matter, several quantitative MRI parameters exhibit dependence on the orientation of the tissue constituents with respect to the main imaging magnetic field (B₀). In this study, we investigated the dependence of multiple relaxation parameters on the orientation of articular cartilage specimens in the B₀. Bovine patellar cartilage-bone samples (n = 4) were investigated ex vivo at 9.4 Tesla at seven different orientations, and the MRI results were compared with polarized light microscopy findings on specimen structure. Dependences of T₂ and continuous wave (CW)-T₁ᵨ relaxation times on cartilage orientation were confirmed. T₂ (and T₂*) had the highest sensitivity to orientation, followed by TRAFF2 and adiabatic T₂ᵨ. The highest dependence was seen in the highly organized deep cartilage and the smallest in the least organized transitional layer. Increasing spin-lock amplitude decreased the orientation dependence of CW-T1ρ. T₁ was found practically orientation-independent and was closely followed by adiabatic T₁ᵨ. The results suggest that T₁ and adiabatic T₁ᵨ should be preferred for orientation-independent quantitative assessment of organized tissues such as articular cartilage. On the other hand, based on the literature, parameters with higher orientation anisotropy appear to be more sensitive to degenerative changes in cartilage

Vibrational spectroscopy and its future applications in microbiology

Abstract Vibrational spectroscopic techniques, namely Fourier transform infrared (FTIR) and Raman spectroscopy, are based on the study of molecular vibrations, and they are complementary techniques to each other. This review provides an overview of the vibrational spectroscopic techniques applied in microbiology during the past decade. In addition, future applications of the elaborated spectroscopic techniques will be highlighted. The results of this review show that both FTIR and Raman spectroscopy are promising alternatives to conventional diagnostic approaches because they provide label-free and noninvasive bacterial detection, identification, and antibiotic susceptibility testing in a single step. Cost-effective, accurate, and rapid tests are needed in order to improve diagnostics and patient care, to decrease the use of unnecessary antimicrobial agents, to prevent resistant microbials, and to decrease the overall burden of outbreaks. Prior to that, however, the presented approaches need to be validated in a clinical workflow against the conventional diagnostic approaches

Suitable cathode NMP replacement for efficient sustainable printed Li-Ion batteries

Abstract N-methyl-2-pyrrolidone (NMP) is the most common solvent for manufacturing cathode electrodes in the battery industry; however, it is becoming restricted in several countries due to its negative environmental impact. Taking into account that ∼99% of the solvent used during electrode fabrication is recovered, dimethylformamide (DMF) is a considerable candidate to replace NMP. The lower boiling point and higher ignition temperature of DMF lead to a significant reduction in the energy consumption needed for drying the electrodes and improve the safety of the production process. Additionally, the lower surface tension and viscosity of DMF enable improved current collector wetting and higher concentrations of the solid material in the cathode slurry. To verify the suitability of DMF as a replacement for NMP, we utilized screen printing, a fabrication method that provides roll-to-roll compatibility while allowing controlled deposition and creation of sophisticated patterns. The battery systems utilized NMC (LiNixMnyCozO2) chemistry in two configurations: NMC523 and NMC88. The first, well-established NCM523, was used as a reference, while NMC88 was used to demonstrate the potential of the proposed method with high-capacity materials. The cathodes were used to create coin and pouch cell batteries that were cycled 1000 times. The achieved results indicate that DMF can successfully replace NMP in the NMC cathode fabrication process without compromising battery performance. Specifically, both the NMP blade-coated and DMF screen-printed batteries retained 87 and 90% of their capacity after 1000 (1C/1C) cycles for NMC523 and NMC88, respectively. The modeling results of the drying process indicate that utilizing a low-boiling-point solvent (DMF) instead of NMP can reduce the drying energy consumption fourfold, resulting in a more environmentally friendly battery production process

Histochemical quantification of collagen content in articular cartilage

Abstract Background: Articular cartilage (AC) is mainly composed of water, type II collagen, proteoglycans (PGs) and chondrocytes. The amount of PGs in AC is routinely quantified with digital densitometry (DD) from Safranin O-stained sections, but it is unclear whether similar method could be used for collagens. Objective: The aim of this study was to clarify whether collagens can be quantified from histological AC sections using DD. Material and methods: Sixteen human AC samples were stained with Masson’s trichrome or Picrosirius red. Optical densities of histological stains were compared to two commonly used collagen parameters (amide I and collagen CH2 side chain peak at 1338cm-1) measured using Fourier Transform Infrared (FTIR) spectroscopic imaging. Results: Optical density of Modified Masson’s trichrome staining, which included enzymatic removal of PGs before staining, correlated significantly with FTIR-derived collagen parameters at almost all depths of cartilage. The other studied staining protocols displayed significant correlations with the reference parameters at only few depth layers. Conclusions: Based on our findings, modified Masson’s trichrome staining protocol is suitable for quantification of AC collagen content. Enzymatic removal of PGs prior to staining is critical as us allows better staining of the collagen. Further optimization of staining protocols may improve the results in the future studies

Optimal regression method for near-infrared spectroscopic evaluation of articular cartilage

Abstract Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool ($R^2_{Tissue~thickness} = 75.6\%$, $R^2_{Dynamic~modulus} = 64.9\%$) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra