Osteolysis: a literature review of basic science and potential computer-based Image processing detection methods

Osteolysis is one of the most prominent reasons of revision surgeries in total joint arthroplasty. is biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. e eventual responses of osteolysis are the activation of macrophages leading to bone resorption and prosthesis failure. Various factors are involved in the initiation of osteolysis from biological issues, design, material specifications, and model of the prosthesis to the health condition of the patient. Nevertheless, the factors leading to osteolysis are sometimes preventable. Changes in implant design and polyethylene manufacturing are striving to improve overall wear. Osteolysisis clinically asymptomatic and can be diagnosed and analyzed during follow-up sessions through various imaging modalities and methods, such as serial radiographic, CT scan, MRI, and image processing-based methods, especially with the use of artificial neural network algorithms. Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics for the detection and segmentation of tumors. ese deep learning algorithms can effectively detect and analyze osteolytic lesions well in advance during follow-up sessions in order to administer proper treatments before reaching a critical point. Osteolysis can be treated surgically or nonsurgically with medications. However, revision surgeries are the only solution for the progressive osteolysis. In this literature review, the underlying causes, mechanisms, and treatments of osteolysis are discussed with the main focus on the possible computer-based methods and algorithms that can be effectively employed for the detection of osteolysis

Predictive role of adiponectin and high-sensitivity C-reactive protein for prediction of cardiovascular event in an Iranian cohort study: The Isfahan Cohort Study

BACKGROUND: Numerous studies have been conducted on the predictive effects of high-sensitivity C-reactive protein (hs-CRP) on cardiovascular events. Few studies have been conducted to investigate the effects of adiponectin for the prediction of the incident of cardiovascular events in the Middle East area. This study compared the predictive effect of hs-CRP and adiponectin on healthy volunteers for the prediction of cerebrovascular disease (CVD). METHODS: This nested case-control in original Isfahan Cohort Study (ICS) was conducted from 2001 to 2011. Participants were selected from ICS. The case group included participants with CVD while the control group included participants without CVD. The level of hs-CRP and adiponectin was measured in the blood samples collected in the year 2007. Thereafter, the statistical analyses were performed to determine the predictive value of hs-CRP and adiponectin in CVD prediction. RESULTS: The results showed that before the elimination of diabetes effect; there was a significant difference between the two groups, in terms of the mean of adiponectin (P = 0.019) and no significant difference was observed in hs-CRP levels (P = 0.673). However, after eliminating the factor of diabetes, there was no significant difference between the case and control groups in adiponectin and hs-CRP levels (P = 0.184, P = 0.946). The results showed that the odds ratio (OR) of the adiponectin level was 0.879 [95% confidence interval (CI): 0.719-1.075, P = 0.210] while the OR of hs-CRP was 1.045 (95% CI: 0.922-1.185, P = 0.491). Furthermore, it was shown that after adjustment for age, sex, and diabetes; the OR of adiponectine was 0.875 (95% CI: 0.701-1.091, P = 0.235) and that of hs-CRP was 1.068 (95% CI: 0.935-1.219, P = 0.333). CONCLUSION: The results show that adiponectin and hs-CRP cannot be predictors for cardiovascular events in a healthy population. Risk factors such as diabetes limit the use of adiponectin as a CVD predictor.&nbsp;</div