Histological scoring of articular cartilage alone provides an incomplete picture of osteoarthritic disease progression

Purpose: To ascertain whether molecularsubcategories of disease progression exist withinestablished histological grades of articular cartilage(AC). Methods: Based on H&E and safranin-O stainingof AC sections obtained from 18 knee arthroplastysurgeries, 30 samples ranging from Mankin ScoringSystem grade 1 through 5 were identified. Immuno-histochemical (IHC) analysis for collagen type II andaggrecan was performed on serial sections of theparaffin-embedded AC samples. Six AC samples fromeach of the five Mankin Scoring System grades wereexamined. Results: Significant IHC differences incollagen type II and aggrecan deposition were seenwithin AC samples from all five histological grades. Therange of IHC differences in collagen type II andaggrecan increased with increasing histological grade. Achange in the pattern of collagen type II deposition wasobserved in MG-3 AC that was consistent with a switchin collagen type II metabolism. Conclusions: IHCstaining of collagen type II and aggrecan can identifydifferences within histological grades of AC that areconsistent with the existence of molecular subcategories.These differences were detectable even within the lowesthistological grades; therefore the use of IHC staining canfurther enhance and refine the scoring of ACdeterioration in early osteoarthritis (OA). Furthermore,the changes seen in the deposition pattern for bothaggrecan and collagen type II suggest that they could beused to monitor key molecular events in OAprogression. These findings also underscore the need forthe development of IHC scoring criteria

Permeation of dimethyl sulfoxide into articular cartilage at subzero temperatures*

Osteochondral allografting has been proved to be a useful method to treat diseased or damaged areas of joint surfaces. Operational long-term stocks of grafts which supply a buffer between procurement and utilization would contribute to the commercialization or industrialization of this technology. Vitrification has been thought to be a promising method for successful preservation of articular cartilage (AC), but high concentration cryoprotectants (CPAs) are used which may cause high cellular toxicity. An effective way to reduce CPA toxicity is to increase CPA concentration gradually while the temperature is lowered. Understanding the mechanism of CPA permeation at subzero temperatures is important for designing the cryopreservation protocol. In this research, the permeation of dimethyl sulfoxide (Me2SO) in ovine AC at subzero temperatures was studied experimentally. Pretreated AC discs were exposed in Me2SO solutions for different time (0, 5, 15, 30, 50, 80, and 120 min) at three temperature levels (−10, −20, and −30 °C). The Me2SO concentration within the tissue was determined by ultraviolet (UV) spectrophotometry. The diffusion coefficients were estimated to be 0.85×10−6, 0.48×10−6, and 0.27×10−6 cm2/s at −10, −20, and −30 °C, respectively, and the corresponding activation energy was 29.23 kJ/mol. Numerical simulation was performed to compare two Me2SO addition protocols, and the results demonstrated that the total loading duration could be effectively reduced with the knowledge of permeation kinetics

A Machine Learning Algorithm to Identify Patients at Risk of Unplanned Subsequent Surgery After Intramedullary Nailing for Tibial Shaft Fractures

Objectives: In the SPRINT trial, 18% of patients with a tibial shaft fracture (TSF) treated with intramedullary nailing (IMN) had one or more unplanned subsequent surgical procedures. It is clinically relevant for surgeon and patient to anticipate unplanned secondary procedures, other than operations that can be readily expected such as reconstructive procedures for soft tissue defects. Therefore, the objective of this study was to develop a machine learning (ML) prediction model using the SPRINT data that can give individual patients and their care team an estimate of their particular probability of an unplanned second surgery. Methods: Patients from the SPRINT trial with unilateral TSFs were randomly divided into a training set (80%) and test set (20%). Five ML algorithms were trained in recognizing patterns associated with subsequent surgery in the training set based on a subset of variables identified by random forest algorithms. Performance of each ML algorithm was evaluated and compared based on (1) area under the ROC curve, (2) calibration slope and intercept, and (3) the Brier score. Results: Total data set comprised 1198 patients, of whom 214 patients (18%) underwent subsequent surgery. Seven variables were used to train ML algorithms: (1) Gustilo-Anderson classification, (2) Tscherne classification, (3) fracture location, (4) fracture gap, (5) polytrauma, (6) injury mechanism, and (7) OTA/AO classification. The best-performing ML algorithm had an area under the ROC curve, calibration slope, calibration intercept, and the Brier score of 0.766, 0.954, -0.002, and 0.120 in the training set and 0.773, 0.922, 0, and 0.119 in the test set, respectively. Conclusions: An ML algorithm was developed to predict the probability of subsequent surgery after IMN for TSFs. This ML algorithm may assist surgeons to inform patients about the probability of subsequent surgery and might help to identify patients who need a different perioperative plan or a more intensive approach.Orthopaedics, Trauma Surgery and Rehabilitatio