Osteoarthritis (OA) is the most common joint disease associated with pain and disability. OA patients are at a high risk for venous thrombosis (VTE). Here, we developed an interpretable machine learning (ML)-based model to predict VTE risk in patients with OA. To establish a prediction model, we used six ML algorithms, of which 35 variables were employed. Recursive feature elimination (RFE) was used to screen the most related clinical variables associated with VTE. SHapley additive exPlanations (SHAP) were applied to interpret the ML mode and determine the importance of the selected features. Overall, 3169 patients with OA (average age: 66.52 ± 7.28 years) were recruited from Xi’an Honghui Hospital. Of these, 352 and 2817 patients were diagnosed with and without VTE, respectively. The XGBoost algorithm showed the best performance. According to the RFE algorithms, 15 variables were retained for further modeling with the XGBoost algorithm. The top three predictors were Kellgren–Lawrence grade, age, and hypertension. Our study showed that the XGBoost model with 15 variables has a high potential to predict VTE risk in patients with OA.

This meta-analysis aims to assess the impact of robotic and open-cut forms on postoperative complications in knee replacement surgeries. A comprehensive and systematic search was conducted on four significant databases using keywords derived from 'robot,' 'robot-assisted knee replacement (TAKR),' 'osteoarthritis,' 'knee arthroplasty,' 'wound healing,' 'knee replacement,' and 'postoperation' from the earliest available records until March 2024. A total of 590 publications were explored, and 16 related randomized clinical trials (RCTs) were analyzed in depth to unveil the role of robots in knee replacement surgery. In this meta-review, different parameters were adopted to evaluate various aspects: operation duration was used to measure the time from the primary incision to the wound closure after the surgery; Knee Society Score (KSS) and Hip Knee Ankle Angle (HKA) were used to evaluate the recovery and imaging features of the knees. Data suggests there was little difference between the two methods except that the operation duration was higher in the robot-assisted group than in the conventional ones. In broad terms, this meta-analysis indicates that employing robotics does not decrease specific post-surgical complications, such as wound infections and painful feelings. However, this advanced medical technology of robots' application helps reduce blood loss and drainage compared to patients with conventional surgery. Consequently, utilizing a robotic approach for knee replacement surgeries may only provide moderate promotion for wound recovery after the surgery. This insight is valuable for surgeons when choosing the most appropriate operative approach.

With the acceleration of global population aging, the incidence of knee osteoarthritis (KOA) has risen significantly, placing unprecedented pressure on healthcare resources and creating an urgent need for automated segmentation technologies to enhance clinical diagnostic efficiency. Therefore, this paper proposes a novel multi-target tissue segmentation network for knee joints, SwMrNet, which integrates improved Swin Transformer units and a proposed multi-scale residual module within the decoder to enhance both segmentation accuracy and robustness. Firstly, a sliding-window mechanism is used to iteratively exchange feature information, allowing for the extraction of global tissue features. Then, features are extracted at multiple scales, with residual connections preserving the fine details of each tissue type. Through the repeated fusion of global and local features, the SwMrNet segmentation performance and robustness are significantly enhanced. Finally, the proposed model was evaluated on a public knee MRI dataset and a local clinical knee MRI dataset. On the public dataset, the model achieved a Dice score of 98.2%, with Dice scores for all segmented tissues exceeding 94%. On the local clinical dataset, the model showed visually consistent segmentation results, suggesting its potential as an efficient multi-tissue segmentation tool for automated knee joint analysis and auxiliary clinical assessment.

Objective: Kashin-Beck disease (KBD) is a kind of endemic and chronic osteochondropathy in China. This study aims to explore the functional relevance and potential mechanism of Wnt-inducible signaling pathway protein 1 (WISP1) in the pathogenesis of KBD. Design: KBD and control cartilage specimens were collected for tissue section observation and primary chondrocyte culture. Firstly, the morphological and histopathological observations were made under a light and electron microscope. Then, the expression levels of WISP1 as well as molecular markers related to the autophagy pathway and extracellular matrix (ECM) synthesis were detected in KBD and control chondrocytes by qRT-PCR, Western blot, and immunohistochemistry. Furthermore, the lentiviral transfection technique was applied to make a WISP1 knockdown cell model based on KBD chondrocytes. In vitro intervention experiments were conducted on the C28/I2 human chondrocyte cell line using human recombinant WISP1 (rWISP1). Results: The results showed that the autolysosome appeared in the KBD chondrocytes. The expression of WISP1 was significantly higher in KBD chondrocytes. Additionally, T-2 toxin, a risk factor for KBD onset, could up-regulate the expression of WISP1 in C28/I2. The autophagy markers ATG4C and LC3II were upregulated after the low-concentration treatment of T-2 toxin and downregulated after the high-concentration treatment. After knocking down WISP1 expression in KBD chondrocytes, MAP1LC3B decreased while ATG4C and COL2A1 increased. Moreover, the rWISP1 protein treatment in C28/I2 chondrocytes could upregulate the expression of ATG4C and LC3II at the beginning and downregulate them then. Conclusions: Our study suggested that WISP1 might play a role in the pathogenesis of KBD through autophagy.