Objective: To observe the clinical efficacy of puncturing lumbar acupoints in treating knee joint pain. Methods: Thirty-seven patients suffered from knee joint pain were treated by puncturing lumbar acupoints combined with cupping therapy. Visual Analog Scale (VAS) was scored before and after the first treatment as well as the second treatment. Result: Before treatment, VAS score was 5.3±1.2. After the first treatment, VAS score descended to 1.9±0.8. Before the second treatment, VAS score was 2.1±0.7. After the second treatment, VAS score descended to 0.7±0.6. Conclusion: Puncturing lumbar acupoints is quite effective in treating knee joint pain caused by lumbar diseases.

Objective To study the expression of Mage A1、A2、A3 in breast cancer tissues and four breast cancer cell lines Methods The expression of Mage A1、A2、A3 in breast cancer tissues and four breast cancer cell lines, MCF 7、Sk Br 3、MDA MB 435s and TM40D was detected by reverse transcription polymerase chain reaction (RT PCR) Results The positive expression rate of Mage A in breast cancer tissues was 13/33 (39%), of Mage A1 was 4/33 (12%), of Mage A2 was 8/33 (24%), and of Mage A3 was 7/33 (21%), respectively Both Mage A1 and Mage A3 were positive in breast cancer cell line MCF 7 and Sk Br 3 MDA MB 435s expressed Mage A2 and Mage A3 Mage A3 was positive in TM40D Conclusion Mage genes were often expressed in breast cancer, but expression of Mages varies in the breast cancer cell lines Mage genes encoding proteins are eligible for Mage peptide based active immunotherapy

Objective To promote the migratory ability and immunological effect of bone marrow-derived dendritic cells ( BMDC) loaded with breast carcinoma antigen. Methods DCs were cultured by the medium containing rmGM-CSF and rmIL-4. After loaded with breast carcinoma antigen, DCs were stimulated with PGE2 for 1day. CD86, CD80, and CCR7 were measured by flow cytometry. The expression of CCR7 on surface of BMDC was also detected by RT-PCR and Western blotting. The chemotaxis assay was measured by migration through a polycarbonate filter in transwell chambers. The competence of inducing mixed lymphocyte response (MLR) and specific cytotoxic T lymphocyte ( CTL) were detected with MTT. The effect of DC blocking tumor growth in breast carcinoma model were also studied. Results Compared with control group, PGE2 upregulated surface markers of CD86, CD80, and CCR7 (P

BackgroundDiabetes distress is highly prevalent and has adverse impacts in adolescent patients with type 1 diabetes. However， the related factors of diabetes distress in adolescents with type 1 diabetes are not clear. ObjectiveTo identify the factors associated with diabetes distress in adolescents with type 1 diabetes using Meta-analysis， and to provide a scientific evidence for effective prevention and improvement of diabetes distress in adolescents with type 1 diabetes. MethodsOn December 1， 2022， a computerized search was conducted on databases including PubMed， Cochrane Library， Web of Science， Embase， CNKI， Wanfang Data， VIP and China Biomedical Literature Database， and studies relevant to diabetes distress in adolescents with type 1 diabetes were systematically included. Quality assessment of cross-sectional and cohort studies was carried out using criteria defined by the Agency for Healthcare Research and Quality （AHRQ） and Newcastle-Ottawa Scale （NOS）. Then the included studies were pooled in a Meta-analysis using Revman 5.3. ResultsA total of 22 studies were included， involving 6 442 adolescents with type 1 diabetes. Meta-analysis denoted that the occurrence of diabetes distress among adolescents with type 1 diabetes was correlated with age （r=0.094，95% CI： 0.042~0.145）， HbA1c （r=0.291， 95% CI： 0.248~0.335）， trait anxiety （r=0.585， 95% CI： 0.526~0.639）， depressive symptoms （r=0.635， 95% CI： 0.590~0.676）， resilience （r=-0.410， 95% CI： -0.528~-0.276） and parents' diabetes distress （r=0.462， 95% CI： 0.421~0.501）. ConclusionFactors including age， HbA1c， trait anxiety， depressive symptoms， resilience and parents' diabetes distress are correlated with diabetes distress in adolescents with type 1 diabetes. ［Funded by Sichuan Science and Technology Program （number， 24KJPX0034）］

BackgroundThe occurrence rate of dangerous behaviors in patients with severe mental disorders is higher than that of the general population. In China， there is limited research on the prediction of dangerous behaviors in community-dwelling patients with severe mental disorders， particularly in terms of predicting models using data mining techniques other than traditional methods. ObjectiveTo explore the influencing factors of dangerous behaviors in community-dwelling patients with severe mental disorders and testing whether the classification decision tree model is superior to the Logistic regression model. MethodsA total of 11 484 community-dwelling patients with severe mental disorders who had complete follow-up records from 2013 to 2022 were selected on December 2023. The data were divided into a training set （n=9 186） and a testing set （n=2 298） in an 8∶2 ratio. Logistic regression and classification decision trees were separately used to establish predictive models in the training set. Model discrimination and calibration were evaluated in the testing set. ResultsDuring the follow-up period， 1 115 cases （9.71%） exhibited dangerous behaviors. Logistic regression results showed that urban residence， poverty， guardianship， intellectual disability， history of dangerous behaviors， impaired insight and positive symptoms were risk factors for dangerous behaviors （OR=1.778， 1.459， 2.719， 1.483， 3.890， 1.423， 2.528， 2.124， P<0.01）. Being aged ≥60 years， educated， not requiring prescribed medication and having normal social functioning were protective factors for dangerous behaviors （OR=0.594， 0.824， 0.422， 0.719， P<0.05 or 0.01）. The predictive effect in the testing set showed an area under curve （AUC） of 0.729 （95% CI： 0.692~0.766）， accuracy of 70.97%， sensitivity of 59.71%， and specificity of 72.05%. The classification decision tree results showed that past dangerous situations， positive symptoms， overall social functioning score， economic status， insight， household registration， disability status and age were the influencing factors for dangerous behaviors. The predictive effect in the testing set showed an AUC of 0.721 （95% CI： 0.705~0.737）， accuracy of 68.28%， sensitivity of 64.46%， and specificity of 68.60%. ConclusionThe classification decision tree does not have a greater advantage over the logistic regression model in predicting the risk of dangerous behaviors in patients with severe mental disorders in the community. ［Funded by Chengdu Medical Research Project （number， 2020052）］