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Objective Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, are gaining significant research interest for characterizing connective tissues, particularly articular cartilage, because there is still a largely unmet need for rapid, accurate and objective methods for assessing tissue integrity in real-time during arthroscopic surgery. This study aims to identify the NIR spectral range that is optimal for characterizing cartilage integrity by ( a) identifying the contribution of its major constituents (collagen and proteoglycans) to its overall spectrum using proxy constituent models and ( b) determining constituent-specific spectral contributions that can be used for assessment of cartilage in its physiological state. Design The NIR spectra of cartilage matrix constituent models were measured and compared with specific molecular components of organic compounds in the NIR spectral range in order to identify their bands and molecular assignments. To verify the identified bands, spectra of the model compounds were compared with those of native cartilage. Since water obscures some bands in the NIR range, spectral measurements of the native cartilage were conducted under conditions of decreasing water content to amplify features of the solid matrix components. The identified spectral bands were then compared and examined in the resulting spectra of the intact cartilage samples. Results As water was progressively eliminated from cartilage, the specific contribution of the different matrix components was observed to correspond with those identified from the proxy cartilage component models. Conclusion Spectral peaks in the regions 5500 to 6250 cm−1 and 8100 to 8600 cm−1 were identified to be effective for characterizing cartilage proteoglycan and collagen contents, respectively.

The lateral resolution of infrared spectroscopy has been inadequate for accurate biochemical characterization of the cell microenvironment, a region regulating biochemical and biomechanical signals to cells. In this study, we demonstrate the capacity of a high-resolution Fourier transform infrared microspectroscopy (HR-FTIR-MS) to characterize the collagen content of this region. Specifically, we focus on the collagen content in the cartilage cell (chondrocyte) microenvironment of healthy and osteoarthritic (OA) cartilage. Human tibial cartilage samples (N = 28) were harvested from 7 cadaveric donors and graded for OA severity (healthy, early OA, advanced OA). HR-FTIR-MS was used to analyze the collagen content of the chondrocyte microenvironment of five distinct zones across the tissue depth. HR-FTIR-MS successfully showed collagen content distribution across chondrocytes and their environment. In zones 2 and 3 (10 - 50% of the tissue thickness), we observed that collagen content was smaller (P < 0.05) in early OA compared to the healthy tissue in the vicinity of cells (pericellular region). The collagen content loss was extended to the extracellular matrix in advanced OA tissue. No significant differences in the collagen content of the chondrocyte microenvironment were observed between the groups in the most superficial (0–10%) and deep zones (50–100%). HR-FTIR-MS revealed collagen loss in the early OA cartilage pericellular region before detectable changes in the extracellular matrix in advanced OA. HR-FTIR-MS-based compositional assessment enables a better understanding of OA-related changes in tissues. This technique can be used to identify new disease mechanisms enabling better intervention strategies. Statement of significance: Osteoarthritis (OA) is the most common degenerative joint disease causing pain and disability. While significant progress has been made in OA research, OA pathogenesis is still poorly understood and current OA treatments are mainly palliative. This study demonstrates that high-resolution FTIR microspectroscopy (HR-FTIR-MS) can characterize OA-induced compositional changes in the cell microenvironment (pericellular matrix) during the early disease stages before tissue changes in the extracellular matrix become apparent. This technique may further enable the identification of new OA mechanisms and improve our current understanding of OA pathogenesis, thus, enabling the development of better treatment methods.

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.

Background: As nocturnal hypoxemia and heart rate variability are associated with excessive daytime sleepiness (EDS) related to OSA, we hypothesize that the power spectral densities (PSD) of nocturnal pulse oximetry signals could be utilized in the assessment of EDS. Thus, we aimed to investigate if PSDs contain features that are related to EDS and whether a convolutional neural network (CNN) could detect patients with EDS using self-learned PSD features. Methods: A total of 915 OSA patients who had undergone polysomnography with multiple sleep latency test on the following day were investigated. PSDs for nocturnal blood oxygen saturation (SpO), heart rate (HR), and photoplethysmogram (PPG), as well as power in the 15–35 mHz band in SpO (P) and HR (P), were computed. Differences in PSD features were investigated between EDS groups. Additionally, a CNN classifier was developed for identifying severe EDS patients based on spectral data. Results: SpO power content increased significantly (p < 0.002) with increasing severity of EDS. Furthermore, a significant (p < 0.001) increase in HR-PSD was found in severe EDS (mean sleep latency < 5 min). Elevated odds of having severe EDS was found in P (OR = 1.19–1.29) and P (OR = 1.81–1.83). Despite these significant spectral differences, the CNN classifier reached only moderate sensitivity (49.5%) alongside high specificity (80.4%) in identifying patients with severe EDS. Conclusions: We conclude that PSDs of nocturnal pulse oximetry signals contain features significantly associated with OSA-related EDS. However, CNN-based identification of patients with EDS is challenging via pulse oximetry.

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (R SVM 2 = 0.81, R RF 2 = 0.74, R linear_regression 2 = 0.60; ρSVM = 0.76, ρRF = 0.76, ρlinear_regression = 0.71) and lower error (RMSESVM = 0.67 mL/g/min, RMSERF = 0.77 mL/g/min, RMSElinear_regression = 0.96 mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.

To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS).In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (The PCA-LME models performed well (independent test: averageNIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy.There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.

Knee ligaments and tendons play an important role in stabilizing and controlling the motions of the knee. Injuries to the ligaments can lead to abnormal mechanical loading of the other supporting tissues (e.g., cartilage and meniscus) and even osteoarthritis. While the condition of knee ligaments can be examined during arthroscopic repair procedures, the arthroscopic evaluation suffers from subjectivity and poor repeatability. Near infrared spectroscopy (NIRS) is capable of non-destructively quantifying the composition and structure of collagen-rich connective tissues, such as articular cartilage and meniscus. Despite the similarities, NIRS-based evaluation of ligament composition has not been previously attempted. In this study, ligaments and patellar tendon of ten bovine stifle joints were measured with NIRS, followed by chemical and histological reference analysis. The relationship between the reference properties of the tissue and NIR spectra was investigated using partial least squares regression. NIRS was found to be sensitive towards the water (R2CV = .65) and collagen (R2CV = .57) contents, while elastin, proteoglycans, and the internal crimp structure remained undetectable. As collagen largely determines the mechanical response of ligaments, we conclude that NIRS demonstrates potential for quantitative evaluation of knee ligaments. publishedVersion

Introduction: Fibrosis is significantly associated with nearly all forms of heart and kidney disease. Clinical diagnosis of fibrosis is currently reliant on conventional methods that do not have the sensitivity and specificity required for effective diagnosis. Hypothesis: An handheld portable near-infrared (NIR) spectrometer, usable intraoperatively coupled to machine learning algorithms can discriminate between fibrotic and healthy cardiac and renal tissue. We sought to validate this in an animal model. Methods: 10 Male Sprague Dawley rats (SDR) with either induced cardiac (SDR-H) and renal (SDR-K) fibrosis (n=5) compare to normal controls (n=5). Hearts from all rats were used as tissue screening in model validation as they contain a high amount of collagen. Multiple tissue samples were harvested from SDR-K ( n fibrosis = 12, n control = 4) and SDR-H ( n fibrosis = 12, n control = 4) groups. NIR spectra (1350 - 2500 nm) were acquired from all tissue sections. Results: Stained sections showed insignificant differences between the fibrotic SDR-H and their corresponding controls as collagen fibrils dominated both groups. SDR-K showed distinguishable features between examined groups. NIR absorption at 1509, 1725, 2055, and 2306 nm were found to be highly indicative of fibrosis (Figure1). PCA (57% explained variance) and PLS-DA (sensitivity: 96%) showed excellent discrimination for SDR-K groups while the heart shows no meaningful discrimination for SDR-H groups. SVM and LR analysis corroborated these results by achieving a 98% classification accuracy for SDR-K and no discrimination for SDR-H. All machine learning models were cross-validated with outcomes of histological staining to establish a robust interpretation and underpin their pathological meanings. Conclusions: NIR accurately diagnoses cardiac and renal fibrosis in rats model. There is potential for this technology to be translated into an intraoperative instrument for tissue diagnosis

Injuries to the ligaments of the knee commonly impact vulnerable and physically active individuals. These injuries can lead to the development of degenerative diseases such as post-traumatic osteoarthritis (PTOA). Non-invasive optical modalities, such as infrared and Raman spectroscopy, provide means for quantitative evaluation of knee joint tissues and have been proposed as potential quantitative diagnostic tools for arthroscopy. In this study, we evaluate Raman spectroscopy as a viable tool for estimating functional properties of collateral ligaments. Artificial trauma was induced by anterior cruciate ligament transection (ACLT) in the left or right knee joint of skeletally mature New Zealand rabbits. The corresponding contralateral (CL) samples were extracted from healthy unoperated joints along with a separate group of control (CNTRL) animals. The rabbits were sacrificed at 8 weeks after ACLT. The ligaments were then harvested and measured using Raman spectroscopy. A uniaxial tensile stress-relaxation testing protocol was adopted for determining several biomechanical properties of the samples. Partial least squares (PLS) regression models were then employed to correlate the spectral data with the biomechanical properties. Results show that the capacity of Raman spectroscopy for estimating the biomechanical properties of the ligament samples varies depending on the target property, with prediction error ranging from 15.78% for tissue cross-sectional area to 30.39% for stiffness. The hysteresis under cyclic loading at 2 Hz (RMSE = 6.22%, Normalized RMSE = 22.24%) can be accurately estimated from the Raman data which describes the viscous damping properties of the tissue. We conclude that Raman spectroscopy has the potential for non-destructively estimating ligament biomechanical properties in health and disease, thus enhancing the diagnostic value of optical arthroscopic evaluations of ligament integrity.

The scourge of malaria infection continues to strike hardest against pregnant women and children in Africa and South East Asia. For global elimination, testing methods that are ultrasensitive to low-level ring-staged parasitemia are urgently required. In this study, we used a novel approach for diagnosis of malaria infection by combining both electronic ultraviolet-visible (UV/vis) spectroscopy and near infrared (NIR) spectroscopy to detect and quantify low-level (1-0.000001%) ring-staged malaria-infected whole blood under physiological conditions uisng Multiclass classification using logistic regression, which showed that the best results were achieved using the extended wavelength range, providing an accuracy of 100% for most parasitemia classes. Likewise, partial least-squares regression (PLS-R) analysis showed a higher quantification sensitivity (R2 = 0.898) for the extended spectral region compared to UV/vis and NIR (R2 = 0.806 and 0.556, respectively). For quantifying different-stage blood parasites, the extended wavelength range was able to detect and quantify all thePlasmodium falciparum accurately compared to testing each spectral component separately. These results demonstrate the potential of a combined UV/vis-NIR spectroscopy to accurately diagnose malaria-infected patients without the need for elaborate sample preparation associated with the existing mid-IR approaches.

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.

Nanomaterials are currently the state-of-the-art in the development of advanced biomedical devices and applications where classical approaches have failed. To date, majority of the literature on nanomaterial interaction with cells have largely focused on the biological responses of cells obtained via assays, with little interest on their biophysical responses. However, recent studies have shown that the biophysical responses of cells, such as stiffness and adhesive properties, play a significant role in their physiological function. In this paper, we investigate cell biophysical responses after uptake of nanoparticles. Atomic force microscopy was used to study changes in cell stiffness and adhesion upon boron nitride (BN) and hydroxyapatite (HAP) nanoparticle uptake. Results show increase in cell stiffness with varying nanoparticle (BN and HAP) concentration, while a decrease in cell adhesion trigger by uptake of HAP. In addition, changes in the biochemical response of the cell membrane were observed via Raman spectroscopy of nanoparticle treated cells. These findings have significant implications in biomedical applications of nanoparticles, e.g. in drug delivery, advanced prosthesis and surgical implants.

Optical properties of biological tissues in the NIR spectral range have demonstrated significant potential for in vivo diagnostic applications and are critical parameters for modelling light interaction in biological tissues. This study aims to investigate the optical properties of articular cartilage as a function of tissue depth and integrity. The results suggest consistent wavelength-dependent variation in optical properties between cartilage depth-wise zones, as well as between healthy and degenerated tissue. Also, statistically significant differences (p0.05) in both optical properties were observed between the different cartilage depth-wise zones and as a result of tissue degeneration. When taken into account, the outcome of this study could enable accurate modelling of light interaction in cartilage matrix and could provide useful diagnostic information on cartilage integrity.

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.

Traditional sleep staging with non-overlapping 30-second epochs overlooks multiple sleep-wake transitions. We aimed to overcome this by analyzing the sleep architecture in more detail with deep learning methods and hypothesized that the traditional sleep staging underestimates the sleep fragmentation of obstructive sleep apnea (OSA) patients. To test this hypothesis, we applied deep learning-based sleep staging to identify sleep stages with the traditional approach and by using overlapping 30-second epochs with 15-, 5-, 1-, or 0.5-second epoch-to-epoch duration. A dataset of 446 patients referred for polysomnography due to OSA suspicion was used to assess differences in the sleep architecture between OSA severity groups. The amount of wakefulness increased while REM and N3 decreased in severe OSA with shorter epoch-to-epoch duration. In other OSA severity groups, the amount of wake and N1 decreased while N3 increased. With the traditional 30-second epoch-to-epoch duration, only small differences in sleep continuity were observed between the OSA severity groups. With 1-second epoch-to-epoch duration, the hazard ratio illustrating the risk of fragmented sleep was 1.14 (p = 0.39) for mild OSA, 1.59 (p 0.01) for moderate OSA, and 4.13 (p0.01) for severe OSA. With shorter epoch-to-epoch durations, total sleep time and sleep efficiency increased in the non-OSA group and decreased in severe OSA. In conclusion, more detailed sleep analysis emphasizes the highly fragmented sleep architecture in severe OSA patients which can be underestimated with traditional sleep staging. The results highlight the need for a more detailed analysis of sleep architecture when assessing sleep disorders.

Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.

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.

Articular cartilage is a connective tissue that enables smooth movements between bones in articulating joints. Cartilage consists of extracellular matrix (ECM) and chondrocytes – the cells responsible for synthesis of the ECM. The ECM consists of type II collagen, proteoglycans, water, and some other minor components. Cartilage is prone to degenerative joint conditions, such as osteoarthritis, due to its weak repair capacity resulting from a lack of vascular, neural, and lymphatic networks. Osteoarthritis causes erosion of the cartilage matrix and therefore inhibits its function, resulting in joint pain, loss of mobility, and significant global socioeconomic burden. Currently, surgical treatment of cartilage pathologies is carried out during arthroscopy with variable outcomes. This variability occurs due to the subjective nature of arthroscopy, which relies on manual palpation and visual evaluation of the tissue surface. Diffuse optical spectroscopy in the near-infrared spectral region probes tissue structure and composition via a relationship with its optical properties (the absorption and reduced scattering coefficients). Due to its avascular nature, healthy cartilage is translucent. It thus has low absorption in the near-infrared region, providing the necessary conditions for light to traverse deep into the tissue. This research reports, for the first time, cartilage absorption and reduced scattering coefficients in the near-infrared spectral range and assess their capacity for characterizing the depth-wise profile of cartilage proteoglycan content. The results revealed that cartilage optical properties are strong predictors of its proteoglycan content. The best performance was observed with the prediction of the proteoglycan content by the absorption coefficient.

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.

Background Histological evaluation of articular cartilage, such as using the Mankin scoring system, is the gold standard for characterization of tissue integrity. This scoring system takes into account several parameters indicative of the tissue's health; however, the collagen integrity, which is a primary indicator of cartilage health is not taken into consideration. Thus, there is need to enhance histological grading of articular cartilage by incorporating explicit scoring of collagen degeneration into the Modified Mankin grading system. This paper explores a new histological grading parameter for collagen network degradation and how this information can be used to augment a widely used grading scheme like the Modified Mankin grading system. Methods Intact and degenerated human cartilage were examined histologically and then subjected to second harmonic generation imaging, leading to qualitative and quantitative description of collagen disruption emanating from the surface to subsurface layers of the tissue. This data was then incorporated into the Modified Mankin grading system. Findings Second harmonic generation image analysis reveals a relationship between changes in collagen architecture and histologically observed tissue disruption in degenerated articular cartilage. Interpretation Histological tissue disruption in degenerated human articular cartilage is directly related to the reorganization of collagen fibrils in the form of intense fibril aggregation, either as a result of degeneration or aging. This method of mapping disrupted tissue regions to quantitative collagen fibril damage can be coded into cartilage grading systems and could inform clinical practice and scientific research.

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.

Objective We investigate the potential of a prototype multimodality arthroscope, combining ultrasound, optical coherence tomography (OCT) and arthroscopic indentation device, for assessing cartilage lesions, and compare the reliability of this approach with conventional arthroscopic scoring ex vivo. Design Areas of interest (AIs, N = 43) were selected from equine fetlock joints (N = 5). Blind-coded AIs were independently scored by two equine surgeons employing International Cartilage Repair Society (ICRS) scoring system via conventional arthroscope and multimodality arthroscope, in which high-frequency ultrasound and OCT catheters were attached to an arthroscopic indentation device. In addition, cartilage stiffness was measured with the indentation device, and lesions in OCT images scored using custom-made automated software. Measurements and scorings were performed twice in two separate rounds. Finally, the scores were compared to histological ICRS scores. Results OCT and arthroscopic examinations showed the highest average agreements (55.2%) between the scoring by surgeons and histology scores, whereas ultrasound had the lowest (50.6%). Average intraobserver agreements of surgeons and interobserver agreements between rounds were, respectively, for conventional arthroscope (68.6%, 69.8%), ultrasound (68.6%, 68.6%), OCT (65.1%, 61.7%) and automated software (65.1%, 59.3%). Conclusions OCT imaging supplemented with the automated software provided the most reliable lesion scoring. However, limited penetration depth of light limits the clinical potential of OCT in assessing human cartilage thickness; thus, the combination of OCT and ultrasound could be optimal for reliable diagnostics. Present findings suggest imaging and quantitatively analyzing the entire articular surface to eliminate surgeon-related variation in the selection of the most severe lesion to be scored., final draft, peerReviewed

The identification of sleep stages is essential in the diagnostics of sleep disorders, among which obstructive sleep apnea (OSA) is one of the most prevalent. However, manual scoring of sleep stages is time-consuming, subjective, and costly. To overcome this shortcoming, we aimed to develop an accurate deep learning approach for automatic classification of sleep stages and to study the effect of OSA severity on the classification accuracy. Overnight polysomnographic recordings from a public dataset of healthy individuals (Sleep-EDF, n = 153) and from a clinical dataset (n = 891) of patients with suspected OSA were used to develop a combined convolutional and long short-term memory neural network. On the public dataset, the model achieved sleep staging accuracy of 83.7% (κ = 0.77) with a single frontal EEG channel and 83.9% (κ = 0.78) when supplemented with EOG. For the clinical dataset, the model achieved accuracies of 82.9% (κ = 0.77) and 83.8% (κ = 0.78) with a single EEG channel and two channels (EEG+EOG), respectively. The sleep staging accuracy decreased with increasing OSA severity. The single-channel accuracy ranged from 84.5% (κ = 0.79) for individuals without OSA diagnosis to 76.5% (κ = 0.68) for patients with severe OSA. In conclusion, deep learning enables automatic sleep staging for suspected OSA patients with high accuracy and expectedly, the accuracy decreased with increasing OSA severity. Furthermore, the accuracies achieved in the public dataset were superior to previously published state-of-the-art methods. Adding an EOG channel did not significantly increase the accuracy. The automatic, single-channel-based sleep staging could enable easy, accurate, and cost-efficient integration of EEG recording into diagnostic ambulatory recordings.

Study Objectives Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term assessment of sleep relies on actigraphy differentiating only between wake and sleep periods without identifying specific sleep stages and having low reliability in identifying wake periods after sleep onset. To address these issues, we aimed to develop an automatic method for identifying the sleep stages from the photoplethysmogram (PPG) signal obtained with a simple finger pulse oximeter. Methods PPG signals from the diagnostic polysomnographies of susptected OSA patients (n = 894) were utilized to develop a combined convolutional and recurrent neural network. The deep learning model was trained individually for three-stage (wake/NREM/REM), four-stage (wake/N1+N2/N3/REM), and five-stage (wake/N1/N2/N3/REM) classification of sleep. Results The three-stage model achieved an epoch-by-epoch accuracy of 80.1% with Cohen’s κ of 0.65. The four- and five-stage models achieved 68.5% (κ = 0.54), and 64.1% (κ = 0.51) accuracies, respectively. With the five-stage model, the total sleep time was underestimated with a mean bias error (SD) of of 7.5 (55.2) minutes. Conclusion The PPG-based deep learning model enabled accurate estimation of sleep time and differentiation between sleep stages with a moderate agreement to manual EEG-based scoring. As PPG is already included in ambulatory polygraphic recordings, applying the PPG-based sleep staging could improve their diagnostic value by enabling simple, low-cost, and reliable monitoring of sleep and help assess otherwise overlooked conditions such as REM-related OSA.

Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral region are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets.

Nano-textured surfaces (NTS) are critical to organisms as self-adaptation and survival tools. These NTS have been actively mimicked in the process of developing bactericidal surfaces for diverse biomedical and hygiene applications. To design and fabricate bactericidal topographies effectively for various applications, understanding the bactericidal mechanism of NTS in nature is essential. The current mechanistic explanations on natural bactericidal activity of nanopillars have not utilized recent advances in microscopy to study the natural interaction. This research reveals the natural bactericidal interaction between E.coli and a dragonfly wing's NTS using advanced microscopy techniques and propose a model. Contrary to the existing mechanistic models, this experimental approach demonstrated that the NTS of dragonfly wings has two prominent nanopillar populations and the resolved interface shows membrane damage occurred without direct contact of the bacterial cell membrane with the nanopillars. We propose that the bacterial membrane damage is initiated by a combination of strong adhesion between nanopillars and bacterium EPS layer as well as shear force when immobilised bacterium attempt to move on the NTS. These findings could help guide the design of novel bio-mimetic nanomaterials by maximising the synergies between both biochemical and mechanical bactericidal effects.Nano-textured surfaces (NTS) are critical to organisms as self-adaptation and survival tools. These NTS have been actively mimicked in the process of developing bactericidal surfaces for diverse biomedical and hygiene applications. To design and fabricate bactericidal topographies effectively for various applications, understanding the bactericidal mechanism of NTS in nature is essential. The current mechanistic explanations on natural bactericidal activity of nanopillars have not utilized recent advances in microscopy to study the natural interaction. This research reveals the natural bactericidal interaction between E.coli and a dragonfly wing's NTS using advanced microscopy techniques and propose a model. Contrary to the existing mechanistic models, this experimental approach demonstrated that the NTS of dragonfly wings has two prominent nanopillar populations and the resolved interface shows membrane damage occurred without direct contact of the bacterial cell membrane with the nanopillars. We propose that the bacterial membrane damage is initiated by a combination of strong adhesion between nanopillars and bacterium EPS layer as well as shear force when immobilised bacterium attempt to move on the NTS. These findings could help guide the design of novel bio-mimetic nanomaterials by maximising the synergies between both biochemical and mechanical bactericidal effects.

This review describes recent developments and applications of near-infrared (NIR) spectroscopy for characterization of articular cartilage integrity. It summarizes the research findings in this area and presents some spectral ranges and peaks associated with the different properties and components of articular cartilage. We further describe recent adaptations of NIR spectroscopy for clinical evaluation of articular cartilage injury and degeneration. Critical to accurate decision-making during repair surgery is having clear knowledge of lesion severity and spread, and how to grade the quality of surrounding cartilage. Thus, in this review, we detail efforts aimed at quantification and classification of cartilage pathology using NIR spectroscopy. Finally, we present open questions and challenges with a view to guiding future directions in NIR spectroscopy research on articular cartilage.

Absorption and reduced scattering coefficients ( μ a , μ s ′ ) of biological tissues have shown significant potential in biomedical applications. Thus, they are effective parameters for the characterization of tissue integrity and provide vital information on the health of biological tissues. This study investigates the potential of optical properties ( μ a , μ s ′ ) for estimating articular cartilage composition and biomechanical properties using multivariate and machine learning techniques. The results suggest that μa could optimally estimate cartilage proteoglycan content in the superficial zone, in addition to its equilibrium modulus. While μ s ′ could effectively estimate the proteoglycan content of the middle and deep zones in addition to the instantaneous and dynamic moduli of articular cartilage.

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%.

We investigate the potential of near-infrared spectroscopy for estimating the properties of human meniscus during arthroscopy. In vitro predictive models were developed and tested on ex vivo arthroscopic near-infrared spectroscopy measurements.

Preprocessing is important for near infrared spectroscopy applications as it reduces noise and improves prediction accuracy of models. We present a toolbox for automatically combining different preprocessing strategies for spectral data.

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.

The severity of obstructive sleep apnea (OSA) is classified using apnea-hypopnea index (AHI). Accurate determination of AHI currently requires manual analysis and complicated registration setup making it expensive and labor intensive. Partially for these reasons, OSA is a heavily underdiagnosed disease as only 7% of women and 18% of men suffering from OSA have diagnosis. To resolve these issues, we introduce an artificial neural network (ANN) that estimates AHI and oxygen desaturation index (ODI) using only the blood oxygen saturation signal (SpO2), recorded during ambulatory polygraphy, as an input. Therefore, hypopneas associated only with an arousal were not considered in this study. SpO2 signals from 1692 patients were used for training and 99 for validation. Two test sets were used consisting of 198 and 1959 patients. In the primary test set, the median absolute errors of ANN estimated AHI and ODI were 0.78 events/hour and 0.68 events/hour respectively. Based on the ANN estimated AHI and ODI, 90.9% and 94.4% of the test patients were classified into the correct OSA severity category. In conclusion, AHI and ODI can be reliably determined using neural network analysis of SpO2 signal. The developed method may enable a more affordable screening of OSA.

Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1st (650–950 nm), 2nd (1100–1350 nm) and 3rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p st optical window and Sb.Th (R2 = 92.3%, error = 7.1%), Tb.Th (R2 = 88.4%, error = 6.7%), BV/TV (R2 = 83%, error = 9.8%) and SMI (R2 = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy.