Objective To study the Hainan Utstein templates used for cardiac arrest and resuscitation registries to evaluate the epidemiological characteristics and outcomes of the patients with CPR by multi-center study. Methodsccording to the Utstein templates for cardiac arrest and CPR set by International Liaison Committee on resuscitation in 2004, a Hainan Utstein CPR registry chart was designed and a prospective descriptive study was carried out to evaluate the epidemiological characteristics, impact factors and outcomes of the patients with resuscitation attempt in emergency departments of thirteen hospitals in Hainan Island between January 2007 and December 2010.Results Of 1125 patients with cardiac arrest, male accounted for 73. 8％ and female was 26. 2％. The mean ( ± S. D) age of the cardiac arrest patients was 53.9 ± 13. 1 years old.Coronary heart diseases and hypertension were the most common preexisting chronic diseases in the studied patients. The ROSC rate and discharge rates after survival in 1125 patients with CPR were 23. 8％ and 7.4％ respectively. The ROSC rate and discharge rates after survival were 36. 3％ and 11.6％ in the in-hospital cardiac arrest (IHCA) group, respectively whereas 11.5％ and 3. 3％ in out-hospital cardiac arrest (OHCA) group. Of 188 patients with ventricular fibrillation/Pulseless ventricular tachycardia, the ROSC rate and discharge rate after survival were 58.0％and 21.8％,respectively. Of them, 448 (39. 8％ ) of the cardiac arrest patients had underlying cardiac causes, and the ROSC rate and discharge rate after survival were 36. 3％ and 11.5％ respectively in IHCA group whereas 11.6％ and 3. 3％ in OHCA group. The ROSC rate and discharge rate after survival were 69. 8％ and 7. 4％respectively in the tertiary hospitals whereas 30. 2％ and 7. 3％ in the secondary hospitals. Conclusions Patients experienced cardiac arrest were predominantly male. Coronary heart disease and hypertension were the two most common preexisting chronic diseases. The ROSC rate and discharge rate of patients with IHCA were higher than those with OHCA. ROSC rate and discharge rate after survival were higher in the ventriculat fibrillation/Pulseless ventricular tachycardia group than the other cardiac rhythms first witnessed groups. The time delayed of starting CPR after onset of cardiac arrest had a critical impact on survival and discharge rate in both IHCA and OHCA groups.

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: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.

Objective:To investigate the feasibility of a three-dimensional no new U-Net (3D nnU-Net) deep learning (DL) network for the automatic segmentation of colorectal cancer (CRC) based on abdominal CT images.Methods:This was a cross-sectional study. From January 2018 to May 2023, a total of 2180 primary CRC patients, confirmed by pathology at the Guangdong Provincial Hospital of Traditional Chinese Medicine (center 1, n=777), Nanfang Hospital, Southern Medical University (center 2, n=732), and Sun Yat-sen Memorial Hospital (center 3, n=671), were enrolled in this retrospective study. The baseline abdominal CT examination of each patient was conducted using CT equipment from 7 different models across 4 vendors, at the 3 centers, encompassing both the arterial phase (AP) and venous phase (VP). Two radiologists manually delineated the volume of interest to circumscribe the entire tumors in dual-enhanced phase CT images. The CT data of CRC patients from center 1 and center 3 were merged and divided into a training set ( n=1 159) and a validation set ( n=289) using a weighted random method with a ratio of 4∶1. The patients from center 2 were used as an independent external test set ( n=732). The 3D nnU-Net segmentation model was trained and tested. Using manually annotated label data as the benchmark, segmentation performance of the model was evaluated based on different phases and tumor locations. The segmentation coverage rate (SCR), Dice similarity coefficient (DSC), recall (REC), precision (PRE), F1-score, and 95% Hausdorff distance (HD 95) were calculated. The mean manual segmentation time and the mean automatic time were compared using independent samples t-test. Results:In the independent external test set, the performance of the 3D nnU-Net model based on the AP CT images was superior to that based on the VP CT images. On the AP images, the SCR, DSC, REC, PRE, F1-score, and HD 95 were 0.865, 0.714, 0.716, 0.736, 0.714, and 27.228, respectively; on the VP images, they were 0.834, 0.679, 0.710, 0.675, 0.679, and 29.358, respectively. The model achieved the best performance on right-sided colon cancer, with SCR, DSC, REC, PRE, F1-score, and HD95 on the AP CT images at 0.901, 0.775, 0.780, 0.787, 0.775, and 21.793, respectively. Next were left-sided colon cancer and rectal cancer, while the segmentation performance for transverse colon cancer was the worst (SCR, DSC, REC, PRE, F1-score, and HD 95 were 0.731, 0.631, 0.641, 0.630, 0.631 and 38.721, respectively). The automatic segmentation time on a single phase was (1.0±0.3) min, while the manual segmentation time was (17.5±6.0) min ( t=128.24, P<0.001). Conclusions:After training and validating on a dataset from multiple centers with various CT scanner vendors, the 3D nnU-Net DL model demonstrates the capability to automatically segment CRC based on abdominal CT images, while also showcasing commendable robustness and generalization ability.