Objective To retrospectively analyze the epidemiology, clinical characteristics and causes of misdiagnosis of Juvenile myoclonic epilepsy (JME) in Xinjiang Uygur Autonomous Region, so as to provide basis for improving the diagnosis and treatment of JME. Methods 979 patients with epilepsy in Xinjiang Uygur Autonomous Region were analyzed retrospectively. There.were515males and 464females,average.age(18.66+8.31)years,.The epidemiological characteristics of JME were analyzed. The clinical characteristics, EEG, treatment effect and prognosis of patients diagnosed with JME were analyzed. The causes of misdiagnosis, missed diagnosis and delayed treatment were analyzed. Results The proportion of JME in 979 patients with epilepsy was 1.4%, a total of 14 cases. The median age of onset was (15+5.83) years, the median time from onset to treatment was 3 years, and the median time from onset to diagnosis was 6 years. All patients showed myoclonic seizures, 13 cases were complicated with generalized tonic clonic seizures, and 4 cases were accompanied by absence seizures. EEG findings include normal background activity, 3-6 Hz generalized spikes or frontal dominant multiple spikes at the beginning of arousal. seven patients were treated with levetiracetam, and the other seven patients were treated with lamotrigine and / or sodium valproate. Incomplete collection of medical history and failure to describe the medical history in detail are the main reasons for delaying diagnosis. Conclusion Juvenile myoclonic epilepsy is an treatable disease, but it is easy to be misdiagnosed. The rate of misdiagnosis and missed diagnosis of JME in Xinjiang is higher, and the delay of diagnosis and treatment is longer. The inquiry of more detailed and demonstrative medical history is of great significance to improve the diagnostic accuracy.
ObjectiveTo develop an immune-related genes (IRGs) based prognostic signature and evaluate the value in predicting prognosis in patients with colon cancer.MethodsGene chip data sets of 452 colon cancer patients were collected from the TCGA database, and 2 498 IRGs data sets were obtained from the ImmPort database. After taking the intersection, univariate and multivariate Cox proportional hazards regression analysis were used to screen and construct the IRGs gene model. To evaluate the prognostic value of genetic models, Cox proportional hazards regression was used to analyze the correlation between IRGs model/clinicopathological features with prognosis of colon cancer. The relationship between risk score and immune cell infiltration was analyzed too.ResultsA total of 206 differentially expressed IRGs were identified in colon cancer tissues, and 11 kinds of IRGs were identified by univariate and multivariate Cox proportional hazards regression analysis: solute carrier family 10 member 2 (SLC10A2), C-X-C motif chemokine ligand 5 (CXCL5), C-C motif chemokine ligand 28 (CCL28), immunoglobulin kappa variable 1D-42 (IGKV1D-42), chromogranin A (CHGA), endothelial cell specific molecule 1 (ESM1), gastrin releasing peptide (GRP), stanniocalcin 2 (STC2), urocortin (UCN), oxytocin receptor (OXTR) and immunoglobulin heavy constant gamma 1 (IGHG1). Colon cancer patients were divided into high risk group and low risk group according to the median value of risk value of IRGs risk markers. Patients in the high risk group had shorter overall survival (OS) than that in the low risk group (P<0.001). The area of the time-dependent ROC curve (AUC) was 0.754, suggesting that IRGs model had a good ability to predict the prognosis of colon cancer patients. The higher the risk value of IRGs, the later T stage of colon cancer (T3–T4), the more lymph node metastasis (N1–N2) and the later clinical stage of colon cancer (Ⅲ–Ⅳ), P<0.05. Except for neutrophils, the infiltration density of B cells, CD4+ T cells, CD8+ T cells, dendritic cells and macrophages were significantly increased with the increased of the risk value (P<0.05).ConclusionThe risk values of the 11 kinds of IRGs gene models screened in this study can be used to predict the prognosis of colon cancer patients, and can be used as biomarkers to evaluate the prognosis of colon cancer patients.