ObjectiveTo detect the metabolites of the serum and joint fluid from rabbits’ osteoarthritis model with 1H nuclear magnetic resonance spectroscopy (NMR) technique, study the metabolic differences and connections of serum, synovial and cartilage of rabbits after the articular cavity injection of sodium hyaluronate, and explore osteoarthritis and metabolic mechanism in the process of treating sodium hyaluronate using sodium hyaluronate, thus provide new ideas and basis of the specific mechanisms in the treatment of osteoarthritis via sodium hyaluronate.MethodsWe selected 30 healthy New Zealand white rabbits, 6 months old, and randomly divided them into three groups as follows: blank control group, model phosphate buffer saline (PBS) liquid injection group and model injection of sodium hyaluronate group, with 10 rabbits in each group. Ten weeks after surgery, all experimental animals were put to death and observed in correlation studies regarding general condition, imaging examination, and histological examination. Metabolites 1H NMR detection and data preprocessing were performed in the serum and joint fluid samples.ResultsThe results considering general condition, general sample observation, imaging examination and histology indicated advantages in sodium hyaluronate group over PBS group. Metabolomics analysis showed statistically significant changes of metabolites in the serum and joint fluid compared with the PBS group and the blank control group (P<0.05). According to the relevant ways of differences metabolites retrieval, analysis found that the effect of sodium hyaluronate on osteoarthritis might be related to protein biosynthesis, amino acid circulation, the metabolic process of pyruvic acid, gluconeogenesis and other metabolic pathways.ConclusionsBased on the research of 1H-NMR metabolomics, the results suggest that the effect of sodium hyaluronate on osteoarthritis is mainly related with the activation of protein metabolism, abnormal lipid and energy metabolic pathways. This study provides new ideas and basis on the concrete mechanism in the treatment of knee osteoarthritis using sodium hyaluronate.
Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.
ObjectiveTo investigate the expression of gasdermin (GSDM) gene family in primary liver cancer and its clinical significance. MethodsThe Gene Expression Profile Data Dynamic Analysis (GEPIA2) database was used to analyze the expression levels of GSDM gene family in primary liver cancer and normal tissues, and survival analysis was performed to explore its relationship with prognosis; GEPIA2 database was used to explore the relationship between GSDM gene family and TNM staging of patients with primary liver cancer. We used GeneMANIA database to predict genes that may interact with GSDM gene family, and used Metascape website for functional enrichment analysis. Finally, we used TIMER database to explore the relationship of expression of GSDM gene family and immune cell infiltration in the tumor microenvironment of primary liver cancer. ResultsCompared with normal liver tissues, GSDMA, GSDMC, GSDMD, and GSDME were highly expressed in primary liver cancer (P<0.050), and GSDMB and DFNB59 were low expressed (P<0.050); results of univariate Cox proportional hazard regression model showed that the differential expressions of GSDMD, GSDME, and DFNB59 were related to the overall survival of patients (P<0.050), and the results of the multivariate Cox proportional hazard regression model showed that GSDME could be used as an independent predictor of the prognosis of liver cancer patients (P<0.050). With the increase of TNM staging in patients with liver cancer, the expressions of GSDMA and GSDMC also gradually increased (P<0.050). Further enrichment analysis showed that the GSDM gene family was involved in pyrolysis and various immune-related biological processes. ConclusionThe GSDM gene is differentially expressed in primary liver cancer, participates in immune-related biological processes, and its expression is related to clinicopathological staging and patients’ prognosis.
ObjectiveBased on the clinical data of patients with foot and ankle deformities in the QIN Sihe Orthopaedic Surgery Database, to analyze the characteristics and treatment strategies of foot and ankle deformities, and provide a basis for clinical decision-making. Methods A total of 22 062 patients with foot and ankle deformities who received orthopedic surgery between May 25, 1978 and December 31, 2020 were searched in the QIN Sihe Orthopedic Surgery Database. The gender, age at operation, regional distribution, etiology, type of deformity, operation method, postoperative fixation method, and other information were collected. Results Among the 22 062 patients, there were 13 046 males (59.13%) and 9 016 females (40.87%); the age at operation ranged from 1 to 77 years, with a median of 17 years, and 20 026 cases (90.77%) were aged 5 to 40 years. The patients came from 32 provinces, municipalities, and autonomous regions across the China and 5 countries including India and the United States, et al. The etiology and diseases type covered 154 kinds (of which sequelae of poliomyelitis, cerebral palsy, spina bifida and tethered spinal cord, congenital equinovarus foot, post-traumatic foot and ankle deformity, and Charcot-Marie-Tooth disease accounted for the highest proportion). The types of deformities included varus foot, equinus foot, valgus foot, talipes calcaneus, equinocavus, high arched foot, claw toe, and flail foot. Surgical methods included tendon lengthening, soft tissue release, tendon transposition, osteotomy orthopedics, and ankle arthrodesis. The 36 620 operations were performed, including 11 561 cases of hip, knee, and lower leg operations to correct the foot and ankle deformities. Postoperative fixation methods included Ilizarov external fixator in 2 709 cases (12.28%), combined external fixator in 3 966 cases (17.98%), and plaster or brace fixation in 15 387 cases (69.74%). ConclusionMale patients with foot and ankle deformities account for a large proportion, and the population distribution is mainly adolescents, with a wide distribution of regions, causes and diseases, and talipes equinovarus and varus foot are the main types of deformities. Foot and ankle deformities are often combined with deformities of other parts of the lower limb, which requires a holistic treatment concept. The application of foot soft tissue and bone surgery combined with Ilizarov external fixator and combined external fixators provides a guarantee for the correction of complex foot and ankle deformities.
Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.