Background Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level. Methods From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed. Conclusion EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration.

ObjectiveAn analysis of the relationship between rheumatoid arthritis (RA) and copper death-related genes (CRG) was explored based on the GEO dataset.MethodsBased on the differential gene expression profiles in the GSE93272 dataset, their relationship to CRG and immune signature were analysed. Using 232 RA samples, molecular clusters with CRG were delineated and analysed for expression and immune infiltration. Genes specific to the CRGcluster were identified by the WGCNA algorithm. Four machine learning models were then built and validated after selecting the optimal model to obtain the significant predicted genes, and validated by constructing RA rat models.ResultsThe location of the 13 CRGs on the chromosome was determined and, except for GCSH. LIPT1, FDX1, DLD, DBT, LIAS and ATP7A were expressed at significantly higher levels in RA samples than in non-RA, and DLST was significantly lower. RA samples were significantly expressed in immune cells such as B cells memory and differentially expressed genes such as LIPT1 were also strongly associated with the presence of immune infiltration. Two copper death-related molecular clusters were identified in RA samples. A higher level of immune infiltration and expression of CRGcluster C2 was found in the RA population. There were 314 crossover genes between the 2 molecular clusters, which were further divided into two molecular clusters. A significant difference in immune infiltration and expression levels was found between the two. Based on the five genes obtained from the RF model (AUC = 0.843), the Nomogram model, calibration curve and DCA also demonstrated their accuracy in predicting RA subtypes. The expression levels of the five genes were significantly higher in RA samples than in non-RA, and the ROC curves demonstrated their better predictive effect. Identification of predictive genes by RA animal model experiments was also confirmed.ConclusionThis study provides some insight into the correlation between rheumatoid arthritis and copper mortality, as well as a predictive model that is expected to support the development of targeted treatment options in the future.

BACKGROUND It is relatively rare for schwannomas to invade bone, but it is very rare for a large mass to form concurrently in the paravertebral region. Surgical resection is the only effective treatment. Because of the extensive tumor involvement and the many important surrounding structures, the tumor needs to be fully exposed. Most of the tumors are completely removed by posterior combined open-heart surgery to relieve spinal cord compression, restore the stability of the spine and maximize the recovery of nerve and spinal cord function. The main objective of this article is to present a schwannoma that had invaded the T5 and T6 vertebral bodies and formed a large paravertebral mass with simultaneous invasion of the spinal canal and compression of the spinal cord. CASE SUMMARY A 40-year-old female suffered from intermittent chest and back pain for 8 years. Computed tomography and magnetic resonance imaging scans showed a paravertebral tumor of approximately 86 mm × 109 mm × 116 mm, where the adjacent T5 and T6 vertebral bodies were invaded by the tumor, the right intervertebral foramen was enlarged, and the tumor had invaded the spinal canal to compress the thoracic medulla. The preoperative puncture biopsy diagnosed a benign schwannoma. Complete resection of the tumor was achieved by a two-step operation. In the first step, the thoracic surgeon adopted a lateral approach to separate the thoracic tumor from the lung. In the second step, a spine surgeon performed a posterior midline approach to dissect the tumor from the vertebral junction through removal of the tumor from the posterior side and further resection of the entire T5 and T6 vertebral bodies. The large bone defect was reconstructed with titanium mesh, and the posterior root arch was nail-fixed. Due to the large amount of intraoperative bleeding, we performed tumor angioembolization before surgery to reduce and avoid large intraoperative bleeding. The postoperative diagnosis of benign schwannoma was confirmed by histochemical examination. There was no sign of tumor recurrence or spinal instability during the 2-year follow-up. CONCLUSION Giant schwannoma is uncommon. In this case, a complete surgical resection of a giant thoracic nerve sheath tumor that invaded part of the vertebral body and compressed the spinal cord was safe and effective.

Abstract-Spatial variation of housing prices is an important contents of urban geography studies. Most efforts have studied space structure of residential district in big cities. The aim of this study was to apply it in a medium-sized city, Nanchang. Spatial autocorrelations analysis and Kriging interpolation method were used to explore the spatial distribution of residential district. Residential district data were based on 808 residential districts collected from two real estate websites at June 2015. In this transverse study, housing prices shows significant positive spatial correlation with significant characteristics of "shortcut" and "hydrophilic". Moreover, the spatial distribution of housing prices is unbalanced, which shows the feature of one major center and multiple sub-centers. Future research may benefit from additional longitudinal studies to confirm these findings.