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. This biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. The 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. Osteolysis is 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. These 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.

Antimicrobial resistance and population structure of Staphylococcus aureus recovered from pigs farms.

Staphylococcus aureus is a burden in human and veterinary medicine. During the last decade, an increasing number of studies reported the presence of livestock-associated methicillin-resistant S. aureus (LA-MRSA) clonal complex (CC) 398 in pigs. During 2013, a survey was performed in pig farms (n=328) randomly selected over Belgium, to monitor the current epidemiological situation of LA-MRSA among asymptomatic pigs and compare with former data to determine possible evolutions. Per farm, nose swabs were taken from 20 animals and pooled. MRSA was detected in 215 farms. Most isolates belonged to CC398 (n=211), and the remaining were ST9/t337 (n=1), ST80/t044 (n=2) and ST239/t4150 (n=1). A large diversity (n=19) of spa-types was found in the CC398 isolates. More than 90% of the isolates were non-wild type (NWT) to tetracycline and trimethoprim. NWT isolates were also found for ciprofloxacin (61.1%), clindamycin (64.4%), erythromycin (57.8%), kanamycin (43.1%) and gentamicin (45.5%). Microarray analysis showed that most CC398 isolates carried genes encoding resistance to tetracycline [tet(M)], macrolide-lincosamide-streptogramin group [erm(B), erm(C), lnu(A), vga(A)], aminoglycosides (aacA-aphD,aa dD, aphA3, sat) and/or phenicols (fexA). One CC398 isolate carried the multi-resistance gene cfr. The non-CC398 isolates carried virulence genes, as the egc-like cluster. The ST80 strain carried the Panton-Valentine leukocidin gene and corresponded to the community-acquired (CA-)MRSA ST80-IV European clone. The MRSA prevalence among pigs in Belgium remains similar to previous studies but a larger diversity in spa-types has been detected in this study. The recovery of CA-MRSA from livestock indicates that one should remain vigilant to the evolution of LA-MRSA in pigs.