Effectiveness of Yijinjing exercise in the treatment of early-stage knee osteoarthritis: a randomized controlled trial protocol.

INTRODUCTION: Knee osteoarthritis (KOA) is still a challenging degenerative joint disease with high morbidity and disease burden. Early-stage KOA, the focus of this study, could present a Window of Opportunity to arrest the disease process and reduce the disease burden. Yijinjing exercise is an important part of physical and psychological therapies in Traditional Chinese Exercise and may be an effective treatment. However, there is no clinical efficacy assessment of Yijinjing exercise for patients with early-stage KOA. Therefore, we designed a randomised controlled trial to evaluate the effectiveness of Yijinjing exercise on patients with early-stage KOA. METHODS AND ANALYSIS: This is a parallel-design, two-arm, analyst assessor-blinded, randomised controlled trial. In total, 60 patients with early-stage KOA will be recruited and randomly assigned to the Yijinjing exercise group (n=30) and health education group (n=30) at a ratio of 1:1, receiving 12 weeks of Yijinjing exercise or health education accordingly. The primary outcome will be measured with the Western Ontario and McMaster Universities Osteoarthritis Index, and the secondary outcomes will include the Visual Analogue Scale, Short-Form 36 Item Health Survey Questionnaire, Beck Depression Inventory, Perceived Stress Scale, Berg Balance Scale, and Gait Analysis for a comprehensive assessment. Outcome measures are collected at baseline, at 12 week ending intervention and at the 12 week, 24 week and 48 week ending follow-up. The primay time point will be 12 weeks postintervention. Adverse events will be recorded for safety assessment. ETHICS AND DISSEMINATION: This study has been approved by the ethical application of the Shanghai Municipal Hospital of Traditional Chinese Medicine Ethics Committee (2021SHL-KY-78). TRIAL REGISTRATION NUMBER: ChiCTR2200065178.

Tuina Intervention in Sodium Monoiodoacetate Injection-Induced Rat Model of Knee Osteoarthritis.

Knee osteoarthritis (KOA), a common degenerative joint disorder, is characterized by chronic pain and disability, which can progress to irreparable structural damage of the joint. Investigations into the link between articular cartilage, muscles, synovium, and other tissues surrounding the knee joint in KOA are of great importance. Currently, managing KOA includes lifestyle modifications, exercise, medication, and surgical interventions; however, the elucidation of the intricate mechanisms underlying KOA-related pain is still lacking. Consequently, KOA pain remains a key clinical challenge and a therapeutic priority. Tuina has been found to have a regulatory effect on the motor, immune, and endocrine systems, prompting the exploration of whether Tuina could alleviate KOA symptoms, caused by the upregulation of inflammatory factors, and further, if the inflammatory factors in skeletal muscle can augment the progression of KOA. We randomized 32 male Sprague Dawley (SD) rats (180-220 g) into four groups of eight animals each: antiPD-L1+Tuina (group A), model (group B), Tuina (group C), and sham surgery (group D). For groups A, B, and C, we injected 25 µL of sodium monoiodoacetate (MIA) solution (4 mg MIA diluted in 25 µL of sterile saline solution) into the right knee joint cavity, and for group D, the same amount of sterile physiological saline was injected. All the groups were evaluated using the least to most stressful tests (paw mechanical withdrawal threshold, paw withdrawal thermal latency, swelling of the right knee joint, Lequesne MG score, skin temperature) before injection and 2, 9, and 16 days after injection.

Graph Autoencoder with Preserving Node Attribute Similarity.

The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks.