Objective To investigate the prevalence rate and the related risk factors of post traumatic stress disorder (PTSD) in rescue troops one year after Wenchuan earthquake as science proofs of mental interven-tion. Methods 1176 officers/soldiers from rescue troops were tested with the questionnaire composed by General state of health questionnaire, PTSD Checklist-Civilian Version (PCL-C), Symptom Checklist-90 (SCL-90), simple coping style questionnaire (SCSQ) and Eysenck Personality Questionnaire (EPQ). Students't test and correlation analysis were used to study the risk factors. Results One year after Wenehuan earthquake,the prevalence rate of PTSD was 3.45%. Compared with the norms of Chinese serviceman, some evaluative indexes' scores, including somatization(1.43±0.57),exterior(12.50±3.79)/interior and positive response factors(2.05±0.71), were higher(P<0.05), at the same time some evaluative indexes' scores, such as anxiety,depression,nervousness and negative response factor, were obviously lower(P<0.01). Correlation analysis showed total score of PLC-C was positively correlated with somatization, anxiety, depression and negative response factors, separately (P<0.01),but negatively correlated with exterior/interior and positive response factors, separately (P<0.01).Conclusion The psychological status of the earthquake-rescuers is basically normal one year after Wenchuan earthquake. The younger soldiers with less education,who have nervous,introversive character,are the emphasis crowd.

Rabdosia (Bl.) Hassk is a medicinal plant rich in enantiomers such as 15α-acetoxyl-6, 11α-epoxy-6α-hydroxy-20-oxo-6, 7-seco-ent-kaur-16-en-1, 7-olide and 15α-dihydroxy-6β-methoxy-6, 7-seco-6, 20-epoxy-1, 7-olide-ent-kaur-16-ene. It has high medicinal value and huge market potential, but the yield from wild Rabdosia (Bl.) Hassk species cannot meet the needs of marketization. Carrying out pollution-free cultivation of Rabdosia rubescens (Hemsl.) Hara and Rabdosia serra (Maxim.) Hara has become the most direct and effective measure to solve this problem in order to ensure the sustainable use of the resources of Rabdosia. This paper summarized a set of non- pollution cultivation system of R. rubescens and R. serra to achieve the"safe, effective, stable and controllable"target.

Objective:To explore the clinical application value of MRI in quantitative evaluation of anterior cruciate ligament mucoid degeneration (ACL-MD).Methods:From March to July 2020, 40 patients who were scheduled to undergo arthroscopic treatment were prospectively collected in the First Affiliated Hospital of Kunming Medical University.The anterior cruciate ligament tissue from the lateral edge of the tibial end was taken during the operation. Based on the pathologicalre sults, the patients were divided into the ACL-MD group ( n=19) and the normal group ( n=21). The sagittal plane three-dimensional steady-state rapid precession (3D-FIESTA), T 1 mapping, T 2 mapping, and T 2* mapping were performed before the knee joint surgery, and the scanned images were post-processed and analyzed to measure the T 1, T 2, and T 2* values of the tibial end of the anterior cruciate ligament.The relaxation time of the ACL-MD group and the normal group was compared using independent sample t test. The ROC curve was drawn using each parameter and the areas under the curve (AUC) for the diagnosis of ACL-MD were obtained.DeLong test was used to compare the differences of AUCs. Results:The T 1 [(1 291.9±273.4) ms], T 2 [(54.8±10.6) ms], and T 2* values [(30.6±6.4) ms] of anterior cruciate ligaments in the ACL-MD group were significantly higher than those in the normal group [ (1 087.0±121.0), (44.8±7.1), (20.4±4.8) ms; t=3.011, 3.473, 5.658, all P<0.001]. The AUCs of T 1, T 2, T 2* were 0.747, 0.764, 0.912, sensitivity of 63.2%, 63.2%, 100%, and the specificity of 100%, 95.2%, 76.2% in diagnosing ACL-MD. The AUC of the T 2* value was higher than those of the T 1 and T 2 values, and the differences were statistically significant ( Z=1.734, 2.162, P=0.043, 0.031). Conclusion:T 1, T 2, T 2*values measured by MRI quantitative imaging have high performance in assessing knee joint ACL-MD, and T 2* value has the largest AUC and the highest diagnostic efficiency.

BACKGROUND:Rapid developments in the field of bioinformatics have provided new methods for the diagnosis of osteoarthritis.Artificial neural networks have powerful data computing and classification capabilities,which have shown better performance in disease diagnosis. OBJECTIVE:To establish a new diagnostic predictive model of osteoarthritis based on artificial neural network and to verify the diagnostic value of the model in osteoarthritis with an external dataset. METHODS:The eligible osteoarthritis-related data sets were downloaded through GEO database search and divided into Train group and Test group.The gene expression matrix of the Train group was analyzed to screen the differentially expressed genes.GO and KEGG enrichment analyses were performed on the differentially expressed genes.Through Lasso regression model,support vector machine model and random forest tree model,the key genes of osteoarthritis were further identified from the differentially expressed genes.The R software"Neuralnet"package was then used to construct the osteoarthritis diagnosis model based on artificial neural network,and the model performance was evaluated by the five-fold cross-validation.Two independent data sets in the Test group were used to verify their diagnostic results. RESULTS AND CONCLUSION:A total of 90 differentially expressed genes related to osteoarthritis were obtained by differential analysis,of which 33 were down-regulated and 57 were up-regulated.GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes,including leukocyte-mediated immunity,leukocyte migration in bone marrow and chemokine production.KEGG enrichment analysis showed that these genes were mainly enriched in rheumatoid arthritis,interleukin-17 signaling pathway and osteoclast differentiation pathway.Five key genes for the diagnosis of osteoarthritis,HMGB2,GADD45A,SLC19A2,TPPP3 and FOLR2,were identified by three machine learning methods.The artificial neural network model of five key genes in the Train group showed that the accuracy was 96.36%and the area under the curve was 0.997.The five-fold cross validation of the neural network model showed that the average area under the curve was greater than 0.9 and the model was of robustness.Two independent data sets in the Test group showed its area under the curve was 0.814 and 0.788 respectively.Therefore,the establishment of an artificial neural network model for the diagnosis of osteoarthritis has a certain diagnostic value.

BACKGROUND:Rheumatoid arthritis is a chronic systemic autoimmune disease.It is important to study the immunological changes involved in it for diagnosis and treatment. OBJECTIVE:To identify immune-related biomarkers associated with rheumatoid arthritis utilizing bioinformatics techniques and examine alterations in immune cell infiltration as well as the relationship between immune cells and biomarkers. METHODS:Differential expression analysis was used to identify the immune-related genes that were up-regulated in rheumatoid arthritis based on the GEO and Immport databases.Kyoto encyclopedia of genes and genomes(KEGG)and gene ontology(GO)enrichment analyses were used to investigate the possible function of these elevated genes.The immunological characteristic genes associated with rheumatoid arthritis were screened using least absolute shrinkage and selection operator(Lasso)and support vector machine recursive feature elimination(SVM-RFE).Independent datasets were used for difference validation,and the diagnostic performance was evaluated by plotting receiver operating characteristic curves for feature genes.Immune cell infiltration was used to analyze the differential profile of immune cells in rheumatoid arthritis and the correlation between the characterized genes and immune cells.In order to ascertain the causal relationship between monocytes and rheumatoid arthritis in immune cells,Mendelian randomization analysis was ultimately employed. RESULTS AND CONCLUSION:There were 39 upregulated differentially expressed genes in rheumatoid arthritis.The genes were primarily enriched in chemotaxis,cytokine activity,and immune receptor activity,according to GO enrichment analysis,while kEGG enrichment analysis revealed that the genes were considerably enriched in the tumor necrosis factor signaling pathway and peripheral leukocyte migration.Lasso and SVM-RFE identified five feature genes:CXCL13,SDC1,IGLC1,PLXNC1,and SLC29A3.Independent dataset validation of the feature genes found them to be similarly highly expressed in rheumatoid arthritis samples,with area under the curve values greater than 0.8 for all five feature genes in both datasets.Immune cell infiltration indicated that most immune cells,including natural killer cells and monocytes,exhibited increased levels of infiltration in rheumatoid arthritis samples.The correlation analysis revealed a significant positive correlation between memory B cells and immature B cells and these five feature genes.Correlation analysis showed that the five feature genes were positively correlated with memory B cells and immature B cells.The inverse variance weighting method revealed that monocytes were associated with the risk of developing rheumatoid arthritis.

BACKGROUND: Vaccination has been shown effective in controlling the global coronavirus disease 2019 (COVID-19) pandemic and reducing severe cases. This study was to assess the flare and change in disease activity after COVID-19 vaccination in patients with stable rheumatoid arthritis (RA). METHODS: A prospective cohort of RA patients in remission or with low disease activity was divided into a vaccination group and a non-vaccination group based on their COVID-19 vaccination status. Each of them was examined every 3 to 6 months. In the vaccination group, disease activity was compared before and after vaccination. The rates of flare defined as disease activity scores based on 28-joint count (DAS28) >3.2 with ΔDAS28 ≥0.6 were compared between vaccination and non-vaccination groups. RESULTS: A total of 202 eligible RA patients were enrolled. Of these, 98 patients received no vaccine shot (non-vaccination group), and 104 patients received two doses of vaccine (vaccination group). The median time interval from pre-vaccination visit to the first immunization and from the second dose of vaccine to post-vaccination visit was 67 days and 83 days, respectively. The disease activity scores at pre-vaccination and post-vaccination visits in the vaccination group patients were similar. At enrollment, gender, RA disease course, seropositivity, and disease activity were comparable across the two groups. Flare was observed in five (4.8%) of the vaccination group patients and nine (9.2%) of the non-vaccination group patients at post-vaccination assessment ( P  = 0.221). In terms of safety, 29 (27.9%) patients experienced adverse events (AEs) after vaccination. No serious AEs occurred. CONCLUSIONS COVID-19 vaccinations had no significant effect on disease activity or risk of flare in RA patients in remission or with low disease activity. Patients with stable RA should be encouraged to receive the COVID-19 vaccination.
Humans ; Arthritis, Rheumatoid ; Cohort Studies ; COVID-19/prevention & control* ; COVID-19 Vaccines/adverse effects* ; East Asian People ; Prospective Studies ; Vaccination/adverse effects*

Objective To explore the predictive value and efficacy evaluation of plasma microRNA(miR)-132,miR-134 combined with miR-124 in patients with depression.Methods A total of 75 patients diagnosed with depression in the psychiatric department of the hospital from June 2021 to July 2023 were selected as the study group,and 75 healthy subjects who underwent physical examination in the hospital during the same pe-riod were selected as the control group.The relative expression levels of miR-132,miR-134 and miR-124 in plasma of the two groups were detected.The levels of miR-132,miR-124,miR-134,brain-derived neurotrophic factor(BDNF).inflammatory factors[interleukin(IL)-6,IL-18,tumor necrosis factor-a(TNF-a)]were com-pared between the two groups before and after 8 weeks of treatment.Multiple linear regression analysis was used to construct a joint prediction model.The application value of plasma miR-132,miR-134,miR-124 and combined prediction in the diagnosis of depression was evaluated by receiver operating characteristic(ROC)curve.Results The relative expression levels of miR-132 and miR-124 in plasma of the study group were sig-nificantly higher than those of the control group,with statistical significance(P＜0.05).The relative expres-sion level of miR-134 in the study group was significantly lower than that in the control group,and the differ-ence was statistically significant(P＜0.05).ROC curve analysis showed that the area under the curve of plas-ma miR-132,miR-134,miR-124 and combined prediction of depression were 0.858 8,0.851 9,0.763 1 and 0.971 4,respectively.Compared with 8 weeks before treatment,plasma miR-132,miR-124,IL-6,IL-18 and TNF-a levels were significantly down-regulated,while plasma miR-134 and BDNF levels were significantly up-regulated after 8 weeks of treatment.Conclusion miR-132,miR-134 and miR-124 are closely related to the oc-currence of depression,and the combination of the three can be used to predict and early diagnose depression patients and evaluate the drug efficacy of depression patients.

BACKGROUND:Knee osteoarthritis(KOA)is a common chronic inflammatory disease that causes damage to joint cartilage and surrounding tissues.Immune cells play an important role in the immune-inflammatory response in knee osteoarthritis,but the specific mechanisms involved are still not fully understood. OBJECTIVE:To evaluate the potential causal relationship between 731 immune cell phenotypes and the risk of knee osteoarthritis using Mendelian randomization. METHODS:Summary statistics of genome-wide association studies(GWAS)for 731 immune cell phenotypes(from GCST0001391 to GCST0002121)obtained from the GWAS catalog and GWAS data for knee osteoarthritis from the IEUGWAS database(ebi-a-GCST007090)were used.Inverse variance-weighted method,MR-Egger regression,weighted median method,weighted mode method,and simple mode method were employed to investigate the causal relationship between immune cells and knee osteoarthritis.Sensitivity analyses were conducted to assess the reliability of the Mendelian randomization results.Reverse Mendelian randomization analysis was also performed using the same methods. RESULTS AND CONCLUSION:The forward MR analysis indicated significant causal relationships(FDR<0.20)between knee osteoarthritis and four immune cell phenotypes,namely CD27 on CD24+CD27+in B cells(OR=1.026,P=0.000 26,Pfdr=0.18),CD33 on CD33dim HLA DR-in myeloid cells(OR=1.014,P=0.000 50,Pfdr=0.18),and CD45RA+CD28-CD8br%CD8br in Treg cells(OR=1.001,P=0.000 78,Pfdr=0.18),and PDL-1 on monocytes in mononuclear cells(OR=0.952,P=0.000 98,Pfdr=0.18).These immune cell phenotypes showed direct positive or negative causal associations with the risk of knee osteoarthritis.Reverse Mendelian randomization analysis revealed no significant causal relationships(FDR<0.20)between knee osteoarthritis as exposure and any of the 731 immune cell phenotypes.The results of sensitivity analysis show that the P-values of the Cochran's Q test and the MR-Egger regression method for bidirectional Mendelian randomization were both greater than 0.05,indicating that there is no significant heterogeneity and pleiotropy in the causal effect analysis between immune cell phenotypes and knee osteoarthritis.To conclude,there may be four potential causal relationships between immune cell phenotypes,such as CD27 on CD24+CD27+cells,CD33 on CD33dim HLA DR-cells,CD45RA+CD28-CD8br%CD8br cells,and PDL-1 on monocytes,and knee osteoarthritis.These findings provide valuable clues for studying the biological mechanisms of knee osteoarthritis and exploring early prevention and treatment strategies.They also offer new directions for the development of intervention drugs.