Raman microspectroscopy demonstrates reduced mineralization of subchondral bone marrow lesions in knee osteoarthritis patients

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms

Introduce

Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis.

Methods

This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm.

Result

The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87.

Conclusion

The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.

An auxiliary diagnostic technology and clinical efficacy evaluation in knee osteoarthritis based on serum surface-enhanced Raman spectroscopy

Knee osteoarthritis (KOA), a progressive joint disease, is a leading source of chronic pain and disability, and its diagnosis mainly depends on medical imaging findings and clinical symptoms. This study aimed to explore an auxiliary diagnostic technology and clinical efficacy evaluation in KOA based on surface-enhanced Raman scattering (SERS). Three sequential experiments were performed: 1) preliminary observation of the therapeutic effects of icariin (ICA); 2) using serum SERS spectra obtained from rat models belonging to sham group, KOA group and icariin treatment group, respectively, to analyze the KOA-related expression profiles; 3) employing partial least squares (PLS) and support vector machines (SVM) algorithms to establish KOA diagnosis model. Pathological changes verified the efficacy of icariin in KOA. Raman peak assignment combined with spectral difference analysis reflected the biochemical changes associated with KOA, including amino acid, carbohydrates and collagen. ICA intervention significantly reversed these changes, although full recovery could not be achieved. Based on PLS-SVM approach, the sensitivity, specificity and accuracy of 100%, 98.33% and 98.89%, respectively, were obtained for screening KOA. This work proves that SERS has great potential to be used as an auxiliary diagnostic technology for KOA, and is also helpful for the exploration of novel KOA treatment agent.

Insights into S. aureus-Induced Bone Deformation in a Mouse Model of Chronic Osteomyelitis Using Fluorescence and Raman Imaging

Osteomyelitis is an infection of the bone that is often difficult to treat and causes a significant healthcare burden. Staphylococcus aureus is the most common pathogen causing osteomyelitis. Osteomyelitis mouse models have been established to gain further insights into the pathogenesis and host response. Here, we use an established S. aureus hematogenous osteomyelitis mouse model to investigate morphological tissue changes and bacterial localization in chronic osteomyelitis with a focus on the pelvis. X-ray imaging was performed to follow the disease progression. Six weeks post infection, when osteomyelitis had manifested itself with a macroscopically visible bone deformation in the pelvis, we used two orthogonal methods, namely fluorescence imaging and label-free Raman spectroscopy, to characterise tissue changes on a microscopic scale and to localise bacteria in different tissue regions. Hematoxylin and eosin as well as Gram staining were performed as a reference method. We could detect all signs of a chronically florid tissue infection with osseous and soft tissue changes as well as with different inflammatory infiltrate patterns. Large lesions dominated in the investigated tissue samples. Bacteria were found to form abscesses and were distributed in high numbers in the lesion, where they could occasionally also be detected intracellularly. In addition, bacteria were found in lower numbers in surrounding muscle tissue and even in lower numbers in trabecular bone tissue. The Raman spectroscopic imaging revealed a metabolic state of the bacteria with reduced activity in agreement with small cell variants found in other studies. In conclusion, we present novel optical methods to characterise bone infections, including inflammatory host tissue reactions and bacterial adaptation.

Greater heterogeneity of the bone mineralisation density distribution and low bone matrix mineralisation characterise tibial subchondral bone marrow lesions in knee osteoarthritis patients

Tibial subchondral bone marrow lesions (BMLs) identified by MRI have been recognised as potential disease predictors in knee osteoarthritis (KOA), and may associate with abnormal bone matrix mineralisation and reduced bone quality. However, these tissue-level changes of BMLs have not been extensively investigated. Thus, the aim of this study was to quantify the degree of subchondral bone matrix mineralisation (both plate and trabeculae) in relation to histomorphometric parameters of bone remodelling and osteocyte lacunae (OL) characteristics in the tibial plateau (TP) of KOA patients with and without BMLs (OA-BML and OA No-BML, respectively) in comparison to nonOA cadaveric controls (CTL). Osteochondral (cartilage-bone) tissue was sampled from the BML signal region within the medial compartment for each OA-BML TP, and from a corresponding medial region for OA No-BML and CTL TPs. The tissue samples were embedded in resin, and sections stained with Von-Kossa Haematoxylin and Eosin (H&E) for quantitation of static indices of bone remodelling. Resin blocks were then further polished, and carbon-coated for quantitative backscattered electron imaging (qBEI) to determine the bone mineralisation density distribution (BMDD), as well as OL characteristics. It was found that OA-BML contained higher osteoid volume per tissue volume (OV/TV; %) and per bone volume (OV/BV; %) in both subchondral plate and trabecular bone compared to OA No-BML and CTL. The BMDD of OA-BML in both subchondral plate and trabecular bone was shifted toward a lower degree of mineralisation. Typically, an increase in both the heterogeneity of mineralisation density (Ca Width; wt%Ca) and the percentage of lower calcium (Ca Low; % B.Ar) in trabecular bone with OA-BML versus CTL was observed. Further, unmineralised OL density (#/mm²) in subchondral plate was distinctly higher in OA-BML samples compared to CTL. The KOA patients with and without BMLs had significantly decreased density of mineralised OL (#/mm²) in trabecular bone compared to CTL. Taken together, these findings indicate that tibial BMLs in advanced KOA patients are characterised by significantly hypo-mineralised subchondral bone compared with CTL. These differences associated with evidence of increased bone remodelling in OA-BML, and may influence the mechanical properties of the subchondral bone, with implications for the overlying cartilage.