BACKGROUND:There were stil lacking related clinical researches in the aspects of whether the total blood loss and hidden blood loss were connected with pathogenesis, whether the total blood loss and hidden blood loss were different among the patients who conducted total hip arthroplasty under different pathogenesis, and whether the preoperative intervention should be conducted for a particular cause? OBJECTIVE:To compare and analyze the hidden blood loss of patients with hip osteoarthritis and femoral neck fracture after total hip replacement. METHODS:The clinical data of 150 patients who received the unilateral total hip arthroplasty treatment from June 2013 to January 2015 were colected and analyzed, including 54 patients with hip osteoarthritis (30 male cases and 24 female cases ), 96 patients with femoral neck fracture (41 male cases and 55 female cases). The pre-and post-operative blood routine and intro-and post-operative blood loss and transfusion were recorded, and hidden blood loss during pen-operation period was evaluated. RESULTS AND CONCLUSION:Total blood loss was (1 616±216) mL, hidden blood loss was (699±102) mL, and hidden blood loss accounted for 43.3% of the total blood loss. The total blood loss was (1 742±254) mL in the hip osteoarthritis group, hidden blood loss was (758±127) mL, hidden blood loss accounted for 44.6% of the total blood loss; The average total blood loss was (1 470±189) mL in the femoral neck fracture group, hidden blood loss was (625±98) mL, hidden blood loss accounts for 42.1% of the total blood loss. The total blood loss and hidden blood loss in hip osteoarthritis group were significantly higher than those in the femoral neck fracture group (P< 0.05). However, there was no significant difference on the hidden blood loss accounts for the proportion of the total blood loss between two groups (P=0.419 3). These results suggest that the total blood loss and hidden blood loss are different for the patients who underwent total hip arthroplasty in the premise of both pathogenesis. Therefore, before the total hip arthroplasty, we should fuly take into account the primary cause of patients and estimate the total blood loss and hidden blood loss, so as to take appropriate preventive measures in time to ensure the safety of the replacement process.

Objective:To investigate the clinical efficacy of arthroscopic limited incision of the articular capsule to repair the glenoid labrum in the treatment of borderline developmental dysplasia of the hip (BDDH) complicated with labral tear.Methods:A retrospective study was conducted to analyze the data of 18 patients with BDDH complicated with labral tear who had been admitted to Department of Orthopaedics, The Second Hospital Affiliated to Suzhou University from January 2016 to December 2019 (observation group). There were 12 males and 6 females with an age of (41.8 ± 8.5) years. Simultaneously, another 18 patients were selected as the control group whose hip development was normal but age and gender were matched with those in the observation group. There were 9 males and 9 females with an age of (43.5 ± 10.3) years. Both groups were treated by arthroscopic limited incision of the articular capsule to repair the glenoid labrum. The 2 groups were compared in terms of modified Harris hip score (MHHS), Hip Outcome Score Activities of Daily Living Subscale (HOS-ADL), and visual analogue scale (VAS).Results:There was no significant difference in the demographic data like age, gender ratio, body mass index, severity of labral tear or time from injury to operation between the 2 groups, indicating comparability between groups ( P>0.05). The observation and control groups were followed up for (38 ± 7) and (43 ± 6) months, respectively. For the observation and control groups, respectively, MHHS was (97.1 ± 3.3) points and (95.4 ± 4.2) points, HOS-ADL (92.6 ± 2.8) points and (91.4 ± 4.1) points, and VAS (0.6 ± 0.5) points and (1.0 ± 0.8) points, all showing no significant difference between groups ( P>0.05). Conclusion:In the treatment of BDDH patients complicated with labral tear, simple arthroscopic limited incision of the articular capsule to repair the glenoid labrum can lead to the same good medium-term efficacy as it can in those with normal hip development.

BACKGROUND:Rheumatoid arthritis is a condition that affects the entire immune system in the body and is known for causing inflammatory hyperplasia in the joints and destruction of articular cartilage.The pathogenesis of rheumatoid arthritis is still unclear;therefore,there is an urgent need to discover new highly sensitive and specific diagnostic biomarkers. OBJECTIVE:To identify and screen key genes in the synovium of rheumatoid arthritis patients using bioinformatics techniques and machine learning algorithms and to construct and validate a rheumatoid arthritis prediction model. METHODS:Three datasets containing synovial tissue samples from rheumatoid arthritis patients(GSE77298,GSE55235,GSE55457)were downloaded from the Gene Expression Omnibus(GEO)database.GSE77298 and GSE55235 were used as the training set,while GSE55457 served as the test set,with a total of 66 samples,including 39 samples from rheumatoid arthritis patients and 27 normal synovial samples.Differentially expressed genes in the training set were selected using R language,and then the weighted gene co-expression network analysis was used to modularize the genes in the training set.The most relevant module was selected,and feature genes within this module were identified.Differentially expressed genes and the feature genes from the module were intersected for the subsequent machine learning analysis.Three machine learning methods,namely the least absolute shrinkage and selection operator algorithm,support vector machine with recursive feature elimination,and random forest algorithm,were employed to further analyze the intersected genes and identify the hub genes.The hub genes obtained from these three machine learning algorithms were intersected again to obtain the key genes in the synovium of rheumatoid arthritis.A predictive rheumatoid arthritis model was constructed using these key genes as variables,and the risk of developing rheumatoid arthritis in patients was inferred based on the model.The receiver operating characteristic curve was used to determine the diagnostic value of the rheumatoid arthritis prediction model and its key genes. RESULTS AND CONCLUSION:Through the differential analysis,a total of 730 differentially expressed genes were identified in the training set,and 185 feature genes were identified in the weighted gene co-expression network analysis feature modules.There were 159 intersected genes obtained.There were 4 hub genes identified by the least absolute shrinkage and selection operator algorithm,11 hub genes by the support vector machine with recursive feature elimination algorithm,and 5 hub genes by the random forest algorithm.After intersection,2 key genes(TNS3 and SDC1)were obtained.Based on the two key genes,a nomogram model was constructed in the training and test sets,with good fit between the calibration prediction curve and the standard curve,and good clinical efficacy in predicting the onset of rheumatoid arthritis.These findings indicate that TNS3 and SDC1,obtained based on bioinformatics and machine learning algorithms,may become key targets for the diagnosis and treatment of rheumatoid arthritis.