Association of X-linked TLR-7 gene polymorphism with the risk of knee osteoarthritis: a case-control study.

Knee osteoarthritis (OA) is the most prevalent type of OA, and Toll-like receptor 7 (TLR7) may lead to the pathogenesis of OA. Recently, X-linked TLR7 polymorphism has been confirmed to be associated with arthritis. However, there is a lack of studies on TLR7 gene polymorphism associated with knee OA susceptibility. The current study aimed to determine whether TLR7 gene polymorphism is associated with the risk of knee OA. Genotyping of two polymorphic sites (rs3853839 and rs179010) in the TLR7 gene was performed in 252 OA patients, and 265 healthy controls using the SNaPshot sequencing technique. Data were analyzed statistically by Chi-square tests and logistic regression. Rs3853839-C allele showed frequencies of 28% and 27% in the healthy control and female knee OA groups, respectively. The differences were not statistically significant (P > 0.05). The rs3853839-CG genotype frequency was significantly lower in the female knee OA group as compared to the healthy control group (OR 0.60; 95%CI 0.36-0.99; P = 0.044). In the male hemizygote population, the rs3853839-CC showed significantly lower frequencies in the male knee OA group as compared to the healthy control group (OR 0.35; 95%CI 0.17-0.71; P = 0.0025). Regarding rs179010, there were no differences in the genotype distribution and allele frequencies between OA patients and healthy subjects under any models (P > 0.05). Stratified analysis showed that the frequency of the rs3853839-CG genotypes was lower in high Kellgren-Lawrence grades (KLG) (OR 0.48; 95%CI 0.21-1.08; P = 0.066), and significantly lower in OA patients with effusion synovitis (OR 0.38; 95%CI 0.17-0.88; P = 0.013). TLR7 rs3853839 polymorphism may play a role in the susceptibility of knee OA in the Chinese Han Population and may be associated with OA severity and the risk of effusion synovitis in Knee OA.

Domain-Adversarial-Guided Siamese Network for Unsupervised Cross-Domain 3-D Object Retrieval.

Recent advances in 3-D sensors and 3-D modeling have led to the availability of massive amounts of 3-D data. It is too onerous and time consuming to manually label a plentiful of 3-D objects in real applications. In this article, we address this issue by transferring the knowledge from the existing labeled data (e.g., the annotated 2-D images or 3-D objects) to the unlabeled 3-D objects. Specifically, we propose a domain-adversarial guided siamese network (DAGSN) for unsupervised cross-domain 3-D object retrieval (CD3DOR). It is mainly composed of three key modules: 1) siamese network-based visual feature learning; 2) mutual information (MI)-based feature enhancement; and 3) conditional domain classifier-based feature adaptation. First, we design a siamese network to encode both 3-D objects and 2-D images from two domains because of its balanced accuracy and efficiency. Besides, it can guarantee the same transformation applied to both domains, which is crucial for the positive domain shift. The core issue for the retrieval task is to improve the capability of feature abstraction, but the previous CD3DOR approaches merely focus on how to eliminate the domain shift. We solve this problem by maximizing the MI between the input 3-D object or 2-D image data and the high-level feature in the second module. To eliminate the domain shift, we design a conditional domain classifier, which can exploit multiplicative interactions between the features and predictive labels, to enforce the joint alignment in both feature level and category level. Consequently, the network can generate domain-invariant yet discriminative features for both domains, which is essential for CD3DOR. Extensive experiments on two protocols, including the cross-dataset 3-D object retrieval protocol (3-D to 3-D) on PSB/NTU, and the cross-modal 3-D object retrieval protocol (2-D to 3-D) on MI3DOR-2, demonstrate that the proposed DAGSN can significantly outperform state-of-the-art CD3DOR methods.