ObjectiveTo explore the therapeutic effect of selective antagonist-AM630 of cannabionid receptor 2 (CB2) in treatment of the titanium particles-induced inflammatory osteolysis.MethodsForty-five female BALB/c mice, 6-8 weeks old, were involved in the study, of which 15 mice were used as skull donors and the rest experimental animals were randomly divided into three groups, ie, black group, control group and treatment group, 10 mice per group.The mice model with air-pouch osteolysis induced by the titanium particles were established.The mice in the treatment group were injected with CB2 selective antagonist-AM630 (200 μg · kg-1 · d-1) intraperitoneally from two days before establishment of the air-pouch osteolysis model to two weeks after establishment of the model.Then, the mice were sacrificed and the pouch tissues were collected for molecular and histological analyses.The pouch membrane thickness and cell infiltration were tested by using computerized image analysis system and HE staining respectively.Osteoclast-like cells in the pouch membrane were determined by using tartrate-resistant acid phosphatase (TRAP) staining.Quantitative real-time polymerase chain reaction (RT-PCR) was employed to detect the mRNA levels of CB2, IL-1 β, TNF-α, receptor activator of NF-κB ligand (RANKL)and receptor activator of NF-κB (RANK).ResultsThere exhibited apparent erythematous and oedematous changes in the control group, which was mitigated around the bone implants with AM630 treatment.Quantitative image analysis of the histological sections revealed significant difference of the pouch membrane thickness among three groups, (192.2 ± 19.4)μm in control group, (88.5 ± 14.7) μm in blank group and (122.1 ± 15.2) μm in treatment group (F = 101.74, P ＜ 0.05).Intensive TRAP staining was identified much in the control group but markedly reduced after AM630 treatment in the pouch tissues.RT-PCR showed that titanium particle stimulation could enhance the expressions of CB2, IL-1 β, TNF-α, RANKL and RANK gene in the air pouch tissues.However, the mRNA levels of these genes were markedly reduced after AM630 treatment, with statistical difference compared with control group (P ＜ 0.05).ConclusionsCB2 selective antagonist AM630 can inhibit the process of titanium particlesstimulated inflammatory reaction and osteoclast activation.Therefore, CB2 represents a new suitable therapeutic candidate for the prevention and treatment of aseptic loosening of the artificial joint.

Objective To evaluate the clinical application of early total care (ETC) for polytrauma patients combined with thoracolumbar fractures.Methods A retrospective case control study was conducted to analyze the clinical data of 137 polytrauma patients combined with thoracolumbar fractures admitted to the First Affiliated Hospital of Soochow University and the Third People's Hospital of Zhang,jiagang from January 2012 to October 2015.There were 90 males and 47 females,aged 26-69 years,with an average age of 48.2 years.The patients were divided into ETC group (n =59) and TMC group (n =78).In the ETC group,physicians from different departments evaluated the patients and developed individualized therapeutic regimens to allow the patients to undergo surgery at early stage after injury.The TMC group preferentially stabilized the patient's condition or transferred the patients to specialist treatment,and then the surgery was performed electively after the condition of the patient was stable.The ISS of the ETC group was (22.15 ± 9.28)points,and that of the TMC group was (23.37 ± 10.74) points.All patients underwent conventional posterior pedicle screw internal fixation.For patients with burst fracture and nerve injury,posterior spinal canal decompression was performed.The thoracolumbar injury classification and severity score (TLICS),spinal load sharing classification (LSC),preoperative and postoperative Glasgow coma score (GCS),Frankel classification,hospitalization time and postoperative complications were compared between the two groups.Results The TLICS scores of ETC group were significantly lower than those of TMC group (P ＜ 0.05) while the LSC scores showed no significant differences between the two groups (P ＞ 0.05).ETC group had shorter hospitalization time [(11.8 ± 3.7)days ∶ (17.5 ±4.5)days] and lower pressure ulcer incidence [(5％ ∶ 21％)] than the TMC group (P ＜ 0.05 or 0.01),but the former had significantly higher wound infection rate [(17％ ∶ 15％)] (P ＜ 0.05).There was no significant difference in pulmonary infection and deep venous thrombosis incidence between the two groups (P ＞ 0.05).No significant differences were found in the preoperative GCS scores between the two group (P ＞ 0.05) while the postoperative GCS scores of TMC group were higher than those of ETC group (P ＜ 0.01).Postoperative GCS scores in both groups were significantly higher than their preoperative GCS (P ＜ 0.05).The results of postoperative Frankel classification in the ETC group were as follows:Grade A in one patient,Grade B in one,Grade C in three,Grade D in four and Grade E in two patients,with an improvement rate of 82％.The results of postoperative Frankel classification in the TMC group were as follows:Grade A in three patients,Grade B in three,Grade C in three,Grade D in four and Grade E in four,with an improvement rate of 65％.Conclusions For polytrauma patients combined with thoracolumbar fractures,ETC can shorten hospitalization time,reduce the pressure ulcer incidence,and better facilitate the recovery of nerve function,yet with higher wound infection risk compared with TMC.TMC was preferred subjectively for patients with unstable thoracolumbar fractures and high TLICS.

Objective:To investigate the efficacy of a machine learning model based on radiomics of brain lesions on T 2WI in differentiating multiple sclerosis (MS) from neuromyelitis optica spectrum disorders (NMOSD). Methods:Totally 223 MS and NMOSD patients who were treated from January 2009 to September 2018 in Beijing Tiantan Hospital Affiliated to Capital Medical University, Donghu Branch of the First Affiliated Hospital of Nanchang University, Tianjin Medical University General Hospital, and Xuanwu Hospital of Capital Medical University were analyzed retrospectively, and according to the proportion of 7∶3, 223 patients were completely randomly divided into training set (156 cases) and test set (67 cases). A total of 74 patients with MS and NMOSD who were treated in Huashan Hospital Affiliated to Fudan University and China-Japan Friendship Hospital of Jilin University from January 2009 to September 2018 and in Xianghu Branch of the First Affiliated Hospital of Nanchang University from March 2020 to September 2021 were collected as an independent external validation set. All patients underwent brain cross-sectional MR T 2WI, radiomics features were extracted from T 2WI, and features were selected by max-relevance and min-redundancy and least absolute shrinkage and selection operator algorithms. Then various machine learning classifier models (logistic regression, decision tree, AdaBoost, random forest or support vector machine) were constructed to differentiate MS from NMOSD. The area under curve (AUC) of receiver operating characteristics was used to evaluate the performance of each classifier model in the training set, test set and external validation set. Results:Based on multi-center T 2WI, a total of 11 radiomics features related to the discrimination between MS and NMOSD were extracted and classifier models were constructed. Among them, the random forest model had the best efficiency in distinguishing MS from NMOSD, and its AUC values for distinguishing MS from NMOSD in the training set, test set and external validation set were 1.000, 0.944 and 0.902, with specificity of 100%, 76.9% and 86.0%, and sensitivity of 100%, 92.1% and 79.7%, respectively. Conclusion:The random forest model based on the radiomic features of T 2WI of brain lesions can effectively distinguish MS from NMOSD.