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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.

Funding Information: This manuscript has been co‐authored by UT‐Battelle, LLC, under contract DE‐AC05‐00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe‐public‐access‐plan ). Funding Information: This material is based upon work supported in part by: the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under contracts and awards ERKJ314, ERKJ331, ERKJ345; the AEOLUS (Advances in Experimental Design, Optimization and Learning for Uncertain Complex Systems) Department of EnergyMathematical Multifaceted Capabilities Center; the Scientific Discovery through Advanced Computing (SciDAC) program through the FASTMath Institute under Contract No. DE‐AC02‐05CH11231; and by the Laboratory Directed Research and Development program at the Oak Ridge National Laboratory, which is operated by UT‐Battelle, LLC., for the U.S. Department of Energy under contract DE‐AC05‐00OR22725. Publisher Copyright: © 2022 The Authors. Advanced Materials Technologies published by Wiley-VCH GmbH 2D transition-metal carbides (Ti3C2Tx MXene) intercalated with organic molecules have been widely used in batteries and supercapacitors, but are quite rarely reported for gas sensing. Since Ti3C2Tx is sensitive to oxygen, most methods for preparing the intercalated Ti3C2Tx involve stirring the reactants with Ti3C2Tx for several hours under nitrogen protection. Herein, a method to prepare a hybrid of Ti3C2Tx and intercalated polysquaraine through microwave-assisted in situ polymerization that takes only a few minutes without the need of using a protective atmosphere is demonstrated. Owing to the increased interlayer space of the Ti3C2Tx after the polymerization, the gas sensors based on the hybrid exhibit a good sensing performance for NH3 detection, being able to detect at least 500 ppb NH3 with a 2.2% ppm−1 of sensitivity. This study provides a facile preparation method for developing intercalated MXenes, which are expected to be useful for a wide range of applications.

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.

Objective Joint injuries may lead to degeneration of cartilage tissue and initiate development of posttraumatic osteoarthritis. Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of articular cartilage quality is subjective. Fourier transform infrared spectroscopy combined with fiber optics and attenuated total reflectance crystal could be used for the assessment of tissue quality during arthroscopy. We hypothesize that fiber-optic mid-infrared spectroscopy can detect enzymatically and mechanically induced damage similar to changes occurring during progression of osteoarthritis. Design Bovine patellar cartilage plugs were extracted and degraded enzymatically and mechanically. Adjacent untreated samples were utilized as controls. Enzymatic degradation was done using collagenase and trypsin enzymes. Mechanical damage was induced by (1) dropping a weight impactor on the cartilage plugs and (2) abrading the cartilage surface with a rotating sandpaper. Fiber-optic mid-infrared spectroscopic measurements were conducted before and after treatments, and spectral changes were assessed with random forest, partial least squares discriminant analysis, and support vector machine classifiers. Results All models had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. Random forest models achieved accuracies between 90.3% and 77.8%, while partial least squares model accuracies ranged from 95.8% to 84.7%, and support vector machine accuracies from 91.7% to 80.6%. Conclusions The results suggest that fiber-optic Fourier transform infrared spectroscopy attenuated total reflectance spectroscopy is a viable way to detect minor and major degeneration of articular cartilage. Objective measures provided by fiber-optic spectroscopic methods could improve arthroscopic evaluation of cartilage damage.

Raman spectroscopy is promising in vivo tool in various biomedical applications; moreover, in recent years, its use for characterizing articular cartilage degeneration has been developing. It has also shown potential for scoring the severity of cartilage lesions, which could be useful in determining the optimal treatment strategy during cartilage repair surgery. However, the effect of different cartilage injury types on Raman spectra is unknown. This study aims to investigate the potential of Raman spectroscopy for detecting changes in cartilage due to different injury types. Artificial injuries were induced in cartilage samples using established mechanical and enzymatic approaches to mimic trauma‐induced and natural degeneration. Mechanical damage was induced using surface abrasion (ABR, n = 12) or impact loading (IMP, n = 12), while enzymatic damage was induced using three different treatments: 30 min trypsin digestion (T30, n = 12), 90 min collagenase digestion (C90, n = 12), and 24 h collagenase digestion (C24, n = 12). Raman spectra were obtained from all specimens, and partial least squares discriminant analysis (PLS‐DA) was used to distinguish cartilage injury types from their respective controls. PLS‐DA cross‐validation accuracies were higher for C24 (88%) and IMP (79%) than for C90 (67%), T30 (63%), and ABR (58%) groups. This study indicates that Raman spectroscopy, combined with multivariate analysis, can discern different cartilage injury types. This knowledge could be useful in clinical decision‐making, for example, selecting the optimal treatment remedy during cartilage repair surgery.

Dental caries is the most common oral disease that causes demineralization of the enamel and later of the dentin. Depth-wise assessment of the demineralization process could be used to help in treatment planning. In this study, we aimed to provide baseline information for the development of a Raman probe by characterizing the mineral composition of the dental tissues from large composition maps (6 × 3 mm² with 15 μm step size) using Raman microspectroscopy. Ten human wisdom teeth with different stages of dental caries lesions were examined. All of the teeth were cut in half at representative locations of the caries lesions and then imaged with a Raman imaging microscope. The pre-processed spectral maps were combined into a single data matrix, and the spectra of the enamel, dentin, and caries were identified by K-means cluster analysis. Our results showed that unsupervised identification of dental caries is possible with the K-means clustering. The compositional analysis revealed that the carious lesions are less mineralized than the healthy enamel, and when the lesions extend into the dentin, they are even less mineralized. Furthermore, there were more carbonate imperfections in the mineral crystal lattice of the caries tissues than in healthy tissues. Interestingly, we observed gradients in the sound enamel showing higher mineralization and greater mineral crystal perfection towards the tooth surface. To conclude, our results provide a baseline for the methodological development aimed at clinical diagnostics for the early detection of active caries lesions.

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 at...

This study investigates the influence of osteoarthritis (OA) disease severity on the bio-composition of the osteochondral junction at the human tibial plateau using Raman microspectroscopy. We specifically aim to analyze the spatial composition of mineralized osteochondral tissues, i.e., calcified cartilage (CC) and subchondral bone plate (SBP) from unfixed, hydrated specimens. We hypothesize that the mineralization of CC and SBP decreases in advanced OA. Twenty-eight cylindrical osteochondral samples (d = 4 mm) from tibial plateaus of seven cadaveric donors were harvested and sorted into three groups following histopathological grading: healthy (n = 5), early OA (n = 8), and advanced OA (n = 15). Raman spectra were subjected to multivariate cluster analyses to identify different tissues. Finally, the tissue-specific composition was analyzed, and the impact of OA was statistically evaluated with linear mixed models. Cluster analyses of Raman spectra successfully distinguished CC and SBP as well as a tidemark region and uncalcified cartilage. CC was found to be more mineralized and the mineral was more crystalline compared with SBP. Both tissues exhibited similar compositional changes as a function of histopathological OA severity. In early OA, the mineralization tends to increase, and the mineral contains fewer carbonate substitutions. Compared with early OA, mineral crystals are rich in carbonate while the overall mineralization decreases in advanced OA. This Raman spectroscopic study advances the methodology for investigating the complex osteochondral junction from native tissue. The developed methodology can be used to elucidate detailed tissue-specific changes in the chemical composition with advancing OA. Statement of Significance In this study, Raman microspectroscopy was utilized to investigate the influence of osteoarthritic degeneration on the tissue-specific biochemical composition of the human osteochondral junction. Multivariate cluster analyses allowed us to characterize subtle compositional changes in the calcified cartilage and subchondral bone plate as well as in the tidemark region. The compositional differences found between the calcified cartilage and subchondral bone plate in both organic and mineral phases will serve as critical benchmark parameters when designing biomaterials for osteochondral repair. We found tissue-specific changes in the mineralization and carbonate substitution as a function of histopathological OA severity. Our developed methodology can be used to investigate the metabolic changes in the osteochondral junction associated with osteoarthritis.

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.

Summary Objective To investigate the feasibility of near-infrared (NIR) spectroscopy (NIRS) for evaluation of human articular cartilage biomechanical properties during arthroscopy. Design A novel arthroscopic NIRS probe designed in our research group was utilized by an experienced orthopedic surgeon to measure NIR spectra from articular cartilage of human cadaver knee joints (ex vivo, n = 18) at several measurement locations during an arthroscopic surgery. Osteochondral samples (n = 265) were extracted from the measurement sites for reference analysis. NIR spectra were remeasured in a controlled laboratory environment (in vitro), after which the corresponding cartilage thickness and biomechanical properties were determined. Hybrid multivariate regression models based on principal component analysis and linear mixed effects modeling (PCA-LME) were utilized to relate cartilage in vitro spectra and biomechanical properties, as well as to account for the spatial dependency. Additionally, a k-nearest neighbors (kNN) classifier was employed to reject outlying ex vivo NIR spectra resulting from a non-optimal probe-cartilage contact. Model performance was evaluated for both in vitro and ex vivo NIR spectra via Spearman's rank correlation (ρ) and the ratio of performance to interquartile range (RPIQ). Results Regression models accurately predicted cartilage thickness and biomechanical properties from in vitro NIR spectra (Model: 0.77 ≤ ρ ≤ 0.87, 2.03 ≤ RPIQ ≤ 3.0; Validation: 0.74 ≤ ρ ≤ 0.84, 1.87 ≤ RPIQ ≤ 2.90). When predicting cartilage properties from ex vivo NIR spectra (0.33 ≤ ρ ≤ 0.57 and 1.02 ≤ RPIQ ≤ 2.14), a kNN classifier enhanced the accuracy of predictions (0.52 ≤ ρ ≤ 0.87 and 1.06 ≤ RPIQ ≤ 1.88). Conclusion Arthroscopic NIRS could substantially enhance identification of damaged cartilage by enabling quantitative evaluation of cartilage biomechanical properties. The results demonstrate the capacity of NIRS in clinical applications.

Raman spectroscopy is promising in vivo tool in various biomedical applications; moreover, in recent years, its use for characterizing articular cartilage degeneration has been developing. It has also shown potential for scoring the severity of cartilage lesions, which could be useful in determining the optimal treatment strategy during cartilage repair surgery. However, the effect of different cartilage injury types on Raman spectra is unknown. This study aims to investigate the potential of Raman spectroscopy for detecting changes in cartilage due to different injury types. Artificial injuries were induced in cartilage samples using established mechanical and enzymatic approaches to mimic trauma‐induced and natural degeneration. Mechanical damage was induced using surface abrasion (ABR, n = 12) or impact loading (IMP, n = 12), while enzymatic damage was induced using three different treatments: 30 min trypsin digestion (T30, n = 12), 90 min collagenase digestion (C90, n = 12), and 24 h collagenase digestion (C24, n = 12). Raman spectra were obtained from all specimens, and partial least squares discriminant analysis (PLS‐DA) was used to distinguish cartilage injury types from their respective controls. PLS‐DA cross‐validation accuracies were higher for C24 (88%) and IMP (79%) than for C90 (67%), T30 (63%), and ABR (58%) groups. This study indicates that Raman spectroscopy, combined with multivariate analysis, can discern different cartilage injury types. This knowledge could be useful in clinical decision‐making, for example, selecting the optimal treatment remedy during cartilage repair surgery.

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.

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.Sources of FundingThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Summary Background Articular cartilage degeneration is the hallmark change of osteoarthritis, a severely disabling disease with high prevalence and considerable socioeconomic and individual burden. Early, potentially reversible cartilage degeneration is characterized by distinct changes in cartilage composition and ultrastructure, while the tissue’s morphology remains largely unaltered. Hence, early degenerative changes may not be diagnosed by clinical standard diagnostic tools. Methods Against this background, this study introduces a novel method to determine the tissue composition non-invasively. Our method involves quantitative MRI parameters (i.e., T1, T1ρ, T2 and T 2 ∗ maps), compositional reference measurements (i.e., microspectroscopically determined local proteoglycan [PG] and collagen [CO] contents) and machine learning techniques (i.e., artificial neural networks [ANNs] and multivariate linear models [MLMs]) on 17 histologically grossly intact human cartilage samples. Results Accuracy and precision were higher in ANN-based predictions than in MLM-based predictions and moderate-to-strong correlations were found between measured and predicted compositional parameters. Conclusion Once trained for the clinical setting, advanced machine learning techniques, in particular ANNs, may be used to non-invasively determine compositional features of cartilage based on quantitative MRI parameters with potential implications for the diagnosis of (early) degeneration and for the monitoring of therapeutic outcomes.

Articular cartilage (AC) is a soft connective tissue that covers the ends of articulating bones. Joint injuries may lead to degeneration of cartilage tissue and initiate development of post-traumatic osteoarthritis (OA). Arthroscopic surgeries can be used to treat joint injuries, but arthroscopic evaluation of cartilage quality is subjective. Therefore, new methods are needed for objective assessment of cartilage degeneration. Fourier transform infrared (FTIR) spectroscopy can be used to assess tissue composition based on the fundamental molecular vibrations. When combined with fiber optics and attenuated total reflectance (ATR) crystal, the measurements can be done flexibly without any sample processing. We hypothesize that Fourier transform infrared attenuated total reflection (FTIR-ATR) spectroscopy can detect enzymatically and mechanically induced changes similar to changes occurring during progression of OA. Fresh bovine patellar cartilage plugs (n = 60) were extracted and degraded enzymatically and mechanically. Adjacent untreated control samples (n = 60) were utilized as controls. Enzymatic degradation was implemented by 90-min and 24-hour collagenase as well as 30-min trypsin treatments. Mechanical damage was induced by: 1) dropping a weight impactor on the cartilage plugs, and 2) abrading the cartilage surface with a rotating sandpaper. Fiber optic FTIR-ATR spectroscopic measurements were conducted for control and degraded samples, and spectral changes were assessed with random forest (RF), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM) classifiers. RF (accuracy 93.1 % to 79.2 %), PLS-DA (accuracy 95.8% to 81.9%), and SVM (accuracy 91.7% to 80.6%) all had excellent classification performance for detecting the different enzymatic and mechanical damage on cartilage matrix. The results suggest that fiber optic FTIR-ATR spectroscopy is a viable way to detect minor degeneration of AC.

Combination of FTIR spectroscopy with fiber optics provides a powerful diagnostic tool for diagnosing of human diseases, including osteoarthritis. To detect cartilage degradation, an arthroscopic probe based on polycrystalline fibers was developed and evaluated on equine cartilage specimen. The hook shape allows reaching a significant portion of the articular surface; the flat tip ensures avoidance of tissue destruction. Efficient QCL-coupling and stable transmission of PIR fibers under bending allows the assembling of effective thin arthroscopy probes and customized multispectral systems for medical diagnostic applications. The presented work was performed within the MIRACLE project (Grant Agreement No 780598, Horizon 2020).

We examine a hybrid multivariate regression technique to account for the spatial dependency in spectroscopic data due to adjacent measurement locations in the same joint by combining dimension reduction methods and linear mixed effects (LME) modeling. Spatial correlation is a common limitation (assumption of independence) encountered in diagnostic applications involving adjacent measurement locations, such as mapping of tissue properties, and can impede tissue evaluations. Near-infrared spectra were collected from equine joints (n = 5) and corresponding biomechanical (n = 202), compositional (n = 530), and structural (n = 530) properties of cartilage tissue were measured. Subsequently, hybrid regression models for estimating tissue properties from the spectral data were developed in combination with principal component analysis (PCA-LME) scores and least absolute shrinkage and selection operator (LASSO-LME). Performance comparison of PCA-LME and principal component regression, and LASSO-LME and LASSO regression was conducted to evaluate the effects of spatial dependency. A systematic improvement in calibration models’ correlation coefficients and a decrease in cross validation errors were observed when accounting for spatial dependency. Our results indicate that accounting for spatial dependency using a LME-based approach leads to more accurate prediction models.

Purpose: We investigated the feasibility of quantitative susceptibility mapping (QSM) for assessing degradation of articular cartilage by measuring ex vivo bovine cartilage samples subjected to different degradative treatments. Specimens were scanned at several orientations to study if degradation affects the susceptibility anisotropy. T2*‐mapping, histological stainings, and polarized light microscopy were used as reference methods. Additionally, simulations of susceptibility in layered geometry were performed. Methods: Samples (n = 9) were harvested from the patellae of skeletally mature bovines. Three specimens served as controls, and the rest were artificially degraded. MRI was performed at 9.4T using a 3D gradient echo sequence. QSM and T2* images and depth profiles through the centers of the samples were compared with each other and the histological findings. A planar isotropic model with depth‐wise susceptibility variation was used in the simulations. Results: A strong diamagnetic contrast was seen in the deep and calcified layers of cartilage, while T2* maps reflected the typical trilaminar structure of the collagen network. Anisotropy of susceptibility in cartilage was observed and was found to differ from the T2* anisotropy. Slight changes were observed in QSM and T2* following the degradative treatments. In simulations, anisotropy was observed. Conclusions: The results suggest that QSM is not sensitive to cartilage proteoglycan content, but shows sensitivity to the amount of calcification and to the integrity of the collagen network, providing potential for assessing osteoarthritis. The simulations suggested that the anisotropy of susceptibility might be partially explained by the layered geometry of susceptibility in cartilage.

The aim of the present study was to evaluate the applicability of ultrasound imaging to quantitative assessment of human meniscus in vitro. Meniscus samples (n = 26) were harvested from 13 knee joints of non-arthritic human cadavers. Subsequently, three locations (anterior, center and posterior) from each meniscus were imaged with two ultrasound transducers (frequencies 9 and 40 MHz), and quantitative ultrasound parameters were determined. Furthermore, partial-least-squares regression analysis was applied for ultrasound signal to determine the relations between ultrasound scattering and meniscus integrity. Significant correlations between measured and predicted meniscus compositions and mechanical properties were obtained (R2 = 0.38–0.69, p

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 (R2Tissue thickness = 75.6%, R2Dynamic 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.

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., published version, peerReviewed

We introduce classification models based on partial least squares discriminant-analysis (PLS-DA) for estimating cartilage integrity (assessed by OARSI grade) based on mid-infrared spectra of cartilage matrix. The best model achieved accuracy of 84%.

This study investigates the influence of osteoarthritis (OA) disease severity on the bio-compositional properties of the osteochondral junction at human tibial plateau using Raman micro-spectroscopy. We specifically aimed to analyze the spatial composition of mineralized osteochondral tissues, i.e. calcified cartilage (CC) and subchondral bone plate (SBP). We hypothesize that the mineralization of CC and SBP decreases in OA. Twenty-eight cylindrical osteochondral samples (d=4mm) from tibial plateaus of cadaveric donors (N=7) were harvested and sorted into three groups following histopathological grading: healthy (n=5), early OA (n=8), and advanced OA (n=15). Raman spectra were subjected to multivariate cluster analyses to identify different tissue types. Finally, the tissue-specific composition was analyzed, and the impact of OA was statistically evaluated with linear mixed models. Cluster analyses of Raman spectra successfully distinguished CC and SBP as well as a tidemark region and uncalcified cartilage. CC was found to be more mineralized and the mineral was more crystalline compared to SBP. Both tissues exhibited similar compositional changes with OA progression. In early OA, the degree of mineralization increases, and the mineral contains fewer carbonate substitutions. Compared to early OA, mineral crystals are rich in carbonate while the overall mineralization decreases in advanced stages. This study provides insight into both mineral and organic phases of the osteochondral junction with OA progression in human tissue. The results show that the mineralization is altered already from the early OA in this biomechanically crucial junction, which advances our understanding of the initiation of OA.

Objective: Our aim is to establish methods for quantifying morphometric properties of calcified cartilage (CC) from micro-computed tomography (mu CT). Furthermore, we evaluated the feasibility of these methods in investigating relationships between osteoarthritis (OA), tidemark surface morphology and open subchondral channels (OSCCs). Method: Samples (n = 15) used in this study were harvested from human lateral tibial plateau (n = 8). Conventional roughness and parameters assessing local 3-dimensional (3D) surface variations were used to quantify the surface morphology of the CC. Subchondral channel properties (percentage, density, size) were also calculated. As a reference, histological sections were evaluated using Histopathological osteoarthritis grading (OARSI) and thickness of CC and subchondral bone (SCB) was quantified. Results: OARSI grade correlated with a decrease in local 3D variations of the tidemark surface (amount of different surface patterns (r(s) = -0.600, P = 0.018), entropy of patterns (EP) (r(s) = -0.648, P = 0.018), homogeneity index (HI) (r(s) = 0.555, P = 0.032)) and tidemark roughness (TMR) (r(s) = -0.579, P = 0.024). Amount of different patterns (ADP) and EP associated with channel area fraction (CAF) (r(p) = 0.876, P

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.

In this study, we demonstrate the potential of mid-infrared (MIR) and near infrared (NIR) spectroscopies to reveal and differentiate between superficial changes in articular cartilage (AC) after mechanical or enzymatic degradation.

A lot has been invested into understanding how to assemble cartilage tissue in vitro and various designs have been developed to manufacture cartilage tissue with native-like biological properties. So far, no satisfactory design has been presented. Bovine primary chondrocytes are used to self-assemble scaffold-free constructs to investigate whether mechanical loading by centrifugal force would be useful in manufacturing cartilage tissue in vitro. Six million chondrocytes were laid on top of defatted bone disks placed inside an agarose well in 50-ml culture tubes. The constructs were centrifuged once or three times per day for 15 min at a centrifugal force of 771×g for up to 4 weeks. Control samples were cultured under the same conditions without exposure to centrifugation. The samples were analysed by (immuno)histochemistry, Fourier transform infrared imaging, micro-computed tomography, biochemical and gene expression analyses. Biomechanical testing was also performed. The centrifuged tissues had a more even surface covering a larger area of the bone disk. Fourier transform infrared imaging analysis indicated a higher concentration of collagen in the top and bottom edges in some of the centrifuged samples. Glycosaminoglycan contents increased along the culture, while collagen content remained at a rather constant level. Aggrecan and procollagen α1(II) gene expression levels had no significant differences, while procollagen α2(I) levels were increased significantly. Biomechanical analyses did not reveal remarkable changes. The centrifugation regimes lead to more uniform tissue constructs, whereas improved biological properties of the native tissue could not be obtained by centrifugation.

Objective: The aims of this study were: to 1) develop a novel sample processing protocol to visualize human articular cartilage (AC) chondrons using micro-computed tomography (mu CT), 2) develop and validate an algorithm to quantify the chondron morphology in 3D, and 3) compare the differences in chondron morphology between intact and osteoarthritic AC. Method: The developed protocol is based on the dehydration of samples with hexamethyldisilazane (HMDS), followed by imaging with a desktop mCT. Chondron density and depth, as well as volume and sphericity, were calculated in 3D with a custom-made and validated algorithm employing semiautomatic chondron selection and segmentation. The quantitative parameters were analyzed at three AC depth zones (zone 1: 0-10%; zone 2: 10-40%; zone 3: 40-100%) and grouped by the OARSI histological grades (OARSI grades 0-1.0, n = 6; OARSI grades 3.0-3.5, n = 6). Results: After semi-automatic chondron selection and segmentation, 1510 chondrons were approved for 3D morphometric analyses. The chondrons especially in the deeper tissue (zones 2 and 3) were significantly larger (P Conclusion: We have developed a novel sample processing protocol for chondron imaging in 3D, as well as a high-throughput algorithm to semi-automatically quantify chondron/ chondrocyte 3D morphology in AC. Our results also suggest that 3D chondron morphology is affected by the progression of osteoarthritis (OA). (c) 2018 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Objective: The aims of this study were: to 1) develop a novel sample processing protocol to visualize human articular cartilage (AC) chondrons using micro-computed tomography (mu CT), 2) develop and validate an algorithm to quantify the chondron morphology in 3D, and 3) compare the differences in chondron morphology between intact and osteoarthritic AC. Method: The developed protocol is based on the dehydration of samples with hexamethyldisilazane (HMDS), followed by imaging with a desktop mCT. Chondron density and depth, as well as volume and sphericity, were calculated in 3D with a custom-made and validated algorithm employing semiautomatic chondron selection and segmentation. The quantitative parameters were analyzed at three AC depth zones (zone 1: 0-10%; zone 2: 10-40%; zone 3: 40-100%) and grouped by the OARSI histological grades (OARSI grades 0-1.0, n = 6; OARSI grades 3.0-3.5, n = 6). Results: After semi-automatic chondron selection and segmentation, 1510 chondrons were approved for 3D morphometric analyses. The chondrons especially in the deeper tissue (zones 2 and 3) were significantly larger (P Conclusion: We have developed a novel sample processing protocol for chondron imaging in 3D, as well as a high-throughput algorithm to semi-automatically quantify chondron/ chondrocyte 3D morphology in AC. Our results also suggest that 3D chondron morphology is affected by the progression of osteoarthritis (OA). (c) 2018 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Objective: The aims of this study were: to 1) develop a novel sample processing protocol to visualize human articular cartilage (AC) chondrons using micro-computed tomography (mu CT), 2) develop and validate an algorithm to quantify the chondron morphology in 3D, and 3) compare the differences in chondron morphology between intact and osteoarthritic AC. Method: The developed protocol is based on the dehydration of samples with hexamethyldisilazane (HMDS), followed by imaging with a desktop mCT. Chondron density and depth, as well as volume and sphericity, were calculated in 3D with a custom-made and validated algorithm employing semiautomatic chondron selection and segmentation. The quantitative parameters were analyzed at three AC depth zones (zone 1: 0-10%; zone 2: 10-40%; zone 3: 40-100%) and grouped by the OARSI histological grades (OARSI grades 0-1.0, n = 6; OARSI grades 3.0-3.5, n = 6). Results: After semi-automatic chondron selection and segmentation, 1510 chondrons were approved for 3D morphometric analyses. The chondrons especially in the deeper tissue (zones 2 and 3) were significantly larger (P Conclusion: We have developed a novel sample processing protocol for chondron imaging in 3D, as well as a high-throughput algorithm to semi-automatically quantify chondron/ chondrocyte 3D morphology in AC. Our results also suggest that 3D chondron morphology is affected by the progression of osteoarthritis (OA). (c) 2018 The Authors. Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Articular cartilage undergoes structural and biochemical changes during maturation, but the knowledge on how these changes relate to articular cartilage function at different stages of maturation is lacking. Equine articular cartilage samples of four different maturation levels (newborn, 5-month-old, 11-month-old and adult) were collected (N = 25). Biomechanical tensile testing, Fourier transform infrared microspectroscopy (FTIR-MS) and polarized light microscopy were used to study the tensile, biochemical and structural properties of articular cartilage, respectively. The tensile modulus was highest and the breaking energy lowest in the newborn group. The collagen and the proteoglycan contents increased with age. The collagen orientation developed with age into an arcade-like orientation. The collagen content, proteoglycan content, and collagen orientation were important predictors of the tensile modulus (p p r = 0.79) and the breaking energy (r = 0.65). To conclude, the composition and structure of equine articular cartilage undergoes changes with depth that alter functional properties during maturation, with the typical properties of mature tissue reached at the age of 5–11 months.

We compared the performance of generalized versus anatomical-specific models for prediction of cartilage properties using visible and near infrared spectroscopy. The results indicate that anatomical-specific models have the potential for enhanced predictive performance.

On-chip energy storage and management will have transformative impacts in developing advanced electronic platforms with built-in energy needs for operation of integrated circuits driving a microprocessor. Though success in growing stand-alone energy storage elements such as electrochemical capacitors (super and pseusocapacitors) on a variety of substrates is a promising step towards this direction. In this work, on-chip energy storage is demonstrated using architectures of highly aligned vertical carbon nanotubes (CNTs) acting as supercapacitors, capable of providing large device capacitances. The efficiency of these structures is further increased by incorporating electrochemically active nanoparticles such as MnOx to form pseudocapacitive architectures thus enhancing device capacitance areal specific capacitance of 37 mF/cm2. The demonstrated on-chip integration is up and down-scalable, compatible with standard CMOS processes, and offers lightweight energy storage what is vital for portable and autonomous device operation with numerous advantages as compared to electronics built from discrete components.

Objective: Histopathological grading of osteochondral (OC) tissue is widely used in osteoarthritis (OA) research, and it is relatively common in post-surgery in vitro diagnostics. However, relying on thin tissue section, this approach includes a number of limitations, such as: (1) destructiveness, (2) sample processing artefacts, (3 ) 2D section does not represent spatial 3D structure and composition of the tissue, and (4) the final outcome is subjective. To overcome these limitations, we recently developed a contrast-enhanced mu CT (CE mu CT) imaging technique to visualize the collagenous extracellular matrix (ECM) of articular cartilage (AC). In the present study, we demonstrate that histopathological scoring of OC tissue from CE mu CT is feasible. Moreover, we establish a new, semi-quantitative OA mu CT grading system for OC tissue. Results: Pathological features were clearly visualized in AC and subchondral bone (SB) with mu CT and verified with histology, as demonstrated with image atlases. Comparison of histopathological grades (OARSI or severity (0-3)) across the characterization approaches, CE mu CT and histology, excellent (0.92, 95% CI = [0.84, 0.96], n = 30) or fair (0.50, 95% CI = [0.16, 0.74], n = 27) intra-class correlations (ICC), respectively. A new mu CT grading system was successfully established which achieved an excellent cross-method (mu CT vs histology) reader-to-reader intra-class correlation (0.78, 95% CI = [0.58, 0.89], n = 27). Conclusions: We demonstrated that histopathological information relevant to OA can reliably be obtained from CE mu CT images. This new grading system could be used as a reference for 3D imaging and analysis techniques intended for volumetric evaluation of OA pathology in research and clinical applications. (C) 2017 Published by Elsevier Ltd on behalf of Osteoarthritis Research Society International.

The structural integrity and mechanical environment of the articular cartilage matrix directly affect chondrocyte deformations. Rabbit models of early osteoarthritis at 9 weeks following anterior cruciate ligament transection (ACLT) have been shown to alter the deformation behavior of superficial zone chondrocytes in mechanically loaded articular cartilage. However, it is not fully understood whether these changes in cell mechanics are caused by changes in structural macromolecules in the extracellular matrix. Therefore, the purpose of this study was to characterize the proteoglycan content, collagen content, and collagen orientation at 9 weeks post ACLT using microscopic techniques, and relate these changes to the altered cell mechanics observed upon mechanical loading of cartilage. At 9 weeks following ACLT, collagen orientation was significantly (p < 0.05) altered and proteoglycan content was significantly (p < 0.05) reduced in the superficial zone cartilage matrix. These structural changes either in the extracellular or pericellular matrix (ECM and PCM) were also correlated significantly (p < 0.05) with chondrocyte width and height changes, thereby suggesting that chondrocyte deformation response to mechanical compression in early OA changes primarily because of alterations in matrix structure. However, compared to the normal group, proteoglycan content in the PCM from the ACLT group decreased less than that in the surrounding ECM. Therefore, PCM could play a key role to protect excessive chondrocyte deformations in the ACLT group. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:342-350, 2018.

Unique biomechanical behavior of articular cartilage is a result of its structure and composition. Interrelationships of tissue constituents (collagen, proteoglycans (PGs) and water) and tissue biomechanical parameters have been studied, but it is evident that no constituent alone explains the tissue mechanics. Fourier transform infrared (FT-IR) spectra can provide detailed information about the biochemical composition of articular cartilage. In this study, a chemometric approach to predict the biomechanical behavior of articular cartilage directly from the FT-IR spectra, i.e., without converting the data into collagen and PG information, was investigated. Partial least squares regression (PLSR) was used to predict equilibrium modulus (n=32) and dynamic modulus (n=24) of bovine cartilage samples from their average FT-IR spectra. The linear correlation coefficients between the reference and predicted values of Young's modulus and dynamic modulus were r=0.866 (p

Summary Objective Recombinant human type II collagen (rhCII) gels combined with autologous chondrocytes were tested as a scaffold for cartilage repair in rabbits in vivo . Method Autologous chondrocytes were harvested, expanded and combined with rhCII-gel and further pre-cultivated for 2 weeks prior to transplantation into a 4 mm diameter lesion created into the rabbit's femoral trochlea ( n = 8). Rabbits with similar untreated lesions ( n = 7) served as a control group. Results Six months after the transplantation the repair tissue in both groups filled the lesion site, but in the rhCII-repair the filling was more complete. Both repair groups also had high proteoglycan and type II collagen contents, except in the fibrous superficial layer. However, the integration to the adjacent cartilage was incomplete. The O'Driscoll grading showed no significant differences between the rhCII-repair and spontaneous repair, both representing lower quality than intact cartilage. In the repair tissues the collagen fibers were abnormally organized and oriented. No dramatic changes were detected in the subchondral bone structure. The repair cartilage was mechanically softer than the intact tissue. Spontaneously repaired tissue showed lower values of equilibrium and dynamic modulus than the rhCII-repair. However, the differences in the mechanical properties between all three groups were insignificant. Conclusion When rhCII was used to repair cartilage defects, the repair quality was histologically incomplete, but still the rhCII-repairs showed moderate mechanical characteristics and a slight improvement over those in spontaneous repair. Therefore, further studies using rhCII for cartilage repair with emphasis on improving integration and surface protection are required.

NMR experiments carried out at magnetic fields below 1 T provide new relaxation parameters unavailable with conventional clinical scanners. Contrast of T₁ generally becomes larger towards low fields, as slow molecular reorientation processes dominate relaxation at the corresponding Larmor frequencies. This advantage has to be considered in the context of lower sensitivity and frequently reduced spatial resolution. The layered structure of cartilage is one example where a particularly strong variation of T₁ across the tissue occurs, being affected by degenerative diseases such as osteoarthritis (OA). Furthermore, the presence of ¹H‐¹⁴N cross‐relaxation, leading to so‐called quadrupolar dips in the ¹H relaxation time dispersion, provide insight into the concentration and mobility of proteoglycans and collagen in cartilage, both being affected by OA. In this study, low‐field imaging and variable‐field NMR relaxometry were combined for the first time for tissue samples, employing unidirectional load to probe the mechanical properties. 20 human knee cartilage samples were placed in a compression cell, and studied by determining relaxation profiles without and with applied pressure (0.6 MPa) at 50 μm in‐plane resolution, and comparing with volume‐averaged T₁ dispersion. Samples were subsequently stored in formalin, prepared for histology and graded according to the Mankin score system. Quadrupolar dips and thickness change under load showed the strongest correlation with Mankin grade. Average T₁ and change of maximum T₁ under load, as well as its position, correlate with thickness and thickness change. Furthermore, T₁(ω) above 25 mT was found to correlate with thickness change. While volume‐averaged T₁ is not a suitable indicator for OA, its change due to mechanical load and its extreme values are suggested as biomarkers available in low‐field MRI systems. The shape of the dispersion T₁(ω) represents a promising access to understanding and quantifying molecular dynamics in tissue, pointing toward future in vivo tissue studies.

Conventional arthroscopic evaluation of articular cartilage is subjective and poorly reproducible. Therefore, implementation of quantitative diagnostic techniques, such as near infrared spectroscopy (NIRS) and optical coherence tomography (OCT), is essential. Locations (n = 44) with various cartilage conditions were selected from mature equine fetlock joints (n = 5). These locations and their surroundings were measured with NIRS and OCT (n = 530). As a reference, cartilage proteoglycan (PG) and collagen contents, and collagen network organization were determined using quantitative microscopy. Additionally, lesion severity visualized in OCT images was graded with an automatic algorithm according to International Cartilage Research Society (ICRS) scoring system. Artificial neural network with variable selection was then employed to predict cartilage composition in the superficial and deep zones from NIRS data, and the performance of two models, generalized (including all samples) and condition-specific models (based on ICRS-grades), was compared. Spectral data correlated significantly (p, published version, peerReviewed

Collagen forms an organized network in articular cartilage to give tensile stiffness to the tissue. Due to its long half-life, collagen is susceptible to cross-links caused by advanced glycation end-products. The current standard method for determination of cross-link concentrations in tissues is the destructive high-performance liquid chromatography (HPLC). The aim of this study was to analyze the cross-link concentrations nondestructively from standard unstained histological articular cartilage sections by using Fourier transform infrared (FTIR) microspectroscopy. Half of the bovine articular cartilage samples ( n = 27 ) were treated with threose to increase the collagen cross-linking while the other half ( n = 27 ) served as a control group. Partial least squares (PLS) regression with variable selection algorithms was used to predict the cross-link concentrations from the measured average FTIR spectra of the samples, and HPLC was used as the reference method for cross-link concentrations. The correlation coefficients between the PLS regression models and the biochemical reference values were r = 0.84 ( p < 0.001 ), r = 0.87 ( p < 0.001 ) and r = 0.92 ( p < 0.001 ) for hydroxylysyl pyridinoline (HP), lysyl pyridinoline (LP), and pentosidine (Pent) cross-links, respectively. The study demonstrated that FTIR microspectroscopy is a feasible method for investigating cross-link concentrations in articular cartilage, published version, peerReviewed

The structural integrity and mechanical environment of the articular cartilage matrix directly affect chondrocyte deformations. Rabbit models of early osteoarthritis at 9 weeks following anterior cruciate ligament transection (ACLT) have been shown to alter the deformation behavior of superficial zone chondrocytes in mechanically loaded articular cartilage. However, it is not fully understood whether these changes in cell mechanics are caused by changes in structural macromolecules in the extracellular matrix. Therefore, the purpose of this study was to characterize the proteoglycan content, collagen content, and collagen orientation at 9 weeks post ACLT using microscopic techniques, and relate these changes to the altered cell mechanics observed upon mechanical loading of cartilage. At 9 weeks following ACLT, collagen orientation was significantly (p 0.05) altered and proteoglycan content was significantly (p 0.05) reduced in the superficial zone cartilage matrix. These structural changes either in the extracellular or pericellular matrix (ECM and PCM) were also correlated significantly (p 0.05) with chondrocyte width and height changes, thereby suggesting that chondrocyte deformation response to mechanical compression in early OA changes primarily because of alterations in matrix structure. However, compared to the normal group, proteoglycan content in the PCM from the ACLT group decreased less than that in the surrounding ECM. Therefore, PCM could play a key role to protect excessive chondrocyte deformations in the ACLT group. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:342-350, 2018.

Summary Objective Articular cartilage (AC) exhibits specific zonal structure that follows the organization of collagen network and concentration of tissue constituents. The aim of this study was to investigate the potential of unsupervised clustering analysis applied to Fourier transform infrared (FTIR) microspectroscopy to detect depth-dependent structural and compositional differences in intact AC. Method Seven rabbit and eight bovine intact patellae AC samples were imaged using FTIR microspectroscopy and normalized raw spectra were clustered using the fuzzy C-means algorithm. Differences in mean spectra of clusters were investigated by quantitative estimation of collagen and proteoglycan (PG) contents, as well as by careful visual investigation of locations of spectral changes. Results Clustering revealed the typical layered structure of AC in both species. However, more distinct clusters were found for rabbit samples, whereas bovine AC showed more complex layered structure. In both species, clustering structure corresponded with that in polarized light microscopic (PLM) images; however, some differences were also observed. Spectral differences between clusters were identified at the same spectral locations for both species. Estimated PG/collagen ratio decreased significantly from superficial to middle or deep zones, which might explain the difference in clustering results compared to PLM. Conclusion FTIR microspectroscopy in combination with cluster analysis allows detailed examination of spatial changes in AC. As far as we know, no previous single technique could reveal a layered structure of AC without any a priori information.