This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.Aims
Methods
This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.Aims
Methods
The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset.Aims
Methods
We investigated the role of ion release in the assessment of fixation of the implant after total knee replacement and hypothesised that ion monitoring could be a useful parameter in the diagnosis of prosthetic loosening. We enrolled 59 patients with unilateral procedures and measured their serum aluminium, titanium, chromium and cobalt ion levels, blinded to the clinical and radiological outcome which was considered to be the reference standard. The cut-off levels for detection of the ions were obtained by measuring the levels in 41 healthy blood donors who had no implants. Based on the clinical and radiological evaluation the patients were divided into two groups with either stable (n = 24) or loosened (n = 35) implants. A significant increase in the mean level of Cr ions was seen in the group with failed implants (p = 0.001). The diagnostic accuracy was 71% providing strong evidence of failure when the level of Cr ions exceeded the cut-off value. The possibility of distinguishing loosening from other causes of failure was demonstrated by the higher diagnostic accuracy of 83%, when considering only patients with failure attributable to loosening. Measurement of the serum level of Cr ions may be of value for detecting failure due to loosening when the diagnosis is in doubt. The other metal ions studies did not have any diagnostic value.
