Co-Expression and Co-Localization of Cartilage Glycoproteins CHI3L1 and Lubricin in Osteoarthritic Cartilage: Morphological,Immunohistochemical and Gene Expression Profiles

Osteoarthritis is the most common human arthritis characterized by degeneration of articular cartilage. Several studies reported that levels of human cartilage glycoprotein chitinase 3-like-1 (CHI3L1) are known as a potential marker for the activation of chondrocytes and the progression of Osteoarthritis (OA), whereas lubricin appears to be chondroprotective. The aim of this study was to investigate the co-expression and co-localization of CHI3L1 and lubricin in normal and osteoarthritic rat articular cartilage to correlate their modified expression to a specific grade of OA. Samples of normal and osteoarthritic rat articular cartilage were analyzed by the Kellgren–Lawrence OA severity scores, the Kraus’ modified Mankin score and the Histopathology Osteoarthritis Research Society International (OARSI) system for histomorphometric evaluations, and through CHI3L1 and lubricin gene expression, immunohistochemistry and double immuno-staining analysis. The immunoexpression and the mRNA levels of lubricin increased in normal cartilage and decreased in OA cartilage (normal vs. OA, p < 0.01). By contrast, the immunoexpression and the mRNA levels of CHI3L1 increased in OA cartilage and decreased in normal cartilage (normal vs. OA, p < 0.01). Our findings are consistent with reports suggesting that these two glycoproteins are functionally associated with the development of OA and in particular with grade 2/3 of OA, suggesting that in the future they could be helpful to stage the severity and progression of the disease.     

Inhibitors of hyaluronan export from hops prevent osteoarthritic reactions

Scope: An early reaction in osteoarthritic chondrocytes is hyaluronan overproduction followed by proteoglycan loss and collagen degradation. We recently found that hyaluronan is exported by the ATP‐binding cassette transporter multidrug resistance associated protein 5 (MRP5) in competition with cGMP and that some phosphodiesterase 5 inhibitors also inhibited hyaluronan export. These inhibitors also prevented osteoarthritic reactions in cartilage. In an effort to identify the improved inhibitors directed primarily toward MRP5, we analyzed the flavonoids Methods and results: Prenylflavonoids from hop xanthohumol, isoxanthohumol and 8‐prenylnaringenin inhibited MRP5 export at lower concentrations than phosphodiesterase 5 activity. They were analyzed for their effect on IL‐induced osteoarthritic reactions in bovine chondrocytes. Xanthohumol was the superior compound to inhibit hyaluronan export, as well as proteoglycan and collagen loss. It also prevented the shedding of metalloproteases into the culture medium. It directly inhibited MRP5, because it reduced the export of the MRP5 substrate fluorescein immediately and did not influence the hyaluronan synthase activity. Conclusions: Xanthohumol may be a natural compound to prevent hyaluronan overproduction and subsequent reactions in osteoarthritis.     

Non‐Contact Evaluation of Osmosis‐Induced Shrinkage and Swelling Behavior of Articular Cartilage In‐Situ Using High‐Frequency Ultrasound

Abstract

The aim of the present study was to use high‐frequency ultrasound for the investigation of the transient osmosis‐induced free shrinkage‐swelling behaviors of normal articular cartilage in situ. Full‐thickness cartilage‐bone specimens were prepared from normal bovine patellae. The transient shrinkage and swelling strains of the cartilage induced by changing the bathing solution between physiological saline (0.15 M) and hypertonic saline (2 M) were monitored using a 50 MHz focused ultrasound beam. Both shrinkage and swelling strains showed temporary overshoots, followed by relaxation phases. The absolute peak value of the shrinkage strain (1.01%±0.62%) was significantly larger (p<0.05) than that of the swelling strain (0.40%±0.33%). It was found that the change of the mean ultrasound speed in cartilage could be approximately represented by an exponential function of time after the change of saline concentration.

This study successfully demonstrated that a high‐frequency focused ultrasound beam could be used to monitor the transient osmosis‐induced deformation of articular cartilage in a non‐contact way. Since the osmosis‐swelling behavior of cartilage relates to its compositional and structural characteristics and degeneration status, the reported ultrasound method may have potential for the characterization of cartilage degeneration, such as osteoarthritis.     

35例膝骨关节炎患者软骨厚度的超声检查

目的本文探讨了超声波在膝骨关节炎的诊断中价值和意义,用超声波检测膝骨关节炎患者软骨的厚度。方法对70个患骨关节炎的膝关节不同部位软骨厚度,包括滑车中央沟,股骨髁前方、负重区、后方7个部位进行超声波测量。并取10例20个正常膝关节软骨厚度作对照。结论高频超声波能够较准确地评价膝骨关节炎软骨厚度的变化。     

Structure and secretory activity of cultured chondrocytes from patients with osteoarthritis.

Cartilage samples were taken from OA patients in order to describe and quantify pro-inflammatory mediators. Samples were cultured under aseptic conditions in Dulbecco's modified Eagle medium at 37 degrees C for 10 days. Control samples, taken from non-inflammatory cartilage, were cultured under the same conditions. The levels of NO(-)2 and NO(-)3 were measured in the supernatant using a spectrophotometric assay. The activity of MMP-1 was quantified by ELISA. The concentration of NO(-)x was 47.3 +/- 4.1 microM in the OA cartilague and 10.7 +/- 1.8 microM in the controls. The average MMP-1 activity was 3,650 +/- 387 ng/ml in the OA cartilage and 2,150 +/- 190 ng/ml in the control samples. These increased values of MMP-1 and NO(-)x observed in the OA cartilage suggest a higher catabolic activity. A morphological analysis of OA chondral tissue using light microscopy shows that the surface of the tissue is characterized by the presence of aggregated chondrocytes or "clones" but in the deeper areas isolated cells are found. These results could be a significant contribution towards the identification of biological markers indicating the presence of OA activity.     

Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images. Moreover, deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis. Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging. This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning. It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.     

红光治疗仪治疗78例膝关节炎患者的效果分析

目的:探讨红光治疗仪治疗膝关节炎的临床效果。方法:选择我院自2013年3月至2014年3月期间收治的膝关节炎患者78例,将其平均分为观察组(n=39)和对照组(n=39)。观察组采用红光治疗仪进行治疗,对照组采用双氯芬酸钠进行治疗,观察和统计患者接受治疗后的临床效果及生活质量改善情况。结果:观察组患者接受治疗后的临床效果明显优于对照组患者(P0.05);观察组患者生活质量改善情况明显优于对照组患者(P0.05)。结论:红光治疗仪治疗膝关节炎效果显著,改善了患者的生活质量,值得推广。     

Automatic Detection and Classification of Human Knee Osteoarthritis Using Convolutional Neural Networks

Knee Osteoarthritis (KOA) is a degenerative knee joint disease caused by ‘wear and tear’ of ligaments between the femur and tibial bones. Clinically, KOA is classified into four grades ranging from 1 to 4 based on the degradation of the ligament in between these two bones and causes suffering from impaired movement. Identifying this space between bones through the anterior view of a knee X-ray image is solely subjective and challenging. Automatic classification of this process helps in the selection of suitable treatment processes and customized knee implants. In this research, a new automatic classification of KOA images based on unsupervised local center of mass (LCM) segmentation method and deep Siamese Convolutional Neural Network (CNN) is presented. First-order statistics and the GLCM matrix are used to extract KOA anatomical Features from segmented images. The network is trained on our clinical data with 75 iterations with automatic weight updates to improve its validation accuracy. The assessment performed on the LCM segmented KOA images shows that our network can efficiently detect knee osteoarthritis, achieving about 93.2% accuracy along with multi-class classification accuracy of 72.01% and quadratic weighted Kappa of 0.86.