Objective To investigate the changes of vital signs and the damage of important organs in dog progressive circulatory failures induced by soman.Method Seven male dogs,weighing(12～15)kg,were injected intramuscularly 1/3 LD sornan(1 LD=10μg/kg)per ten minutes.The moan blood pressure decreased to (40～45)mmHg was defined as circulatory failure.The changes of heart rate,blood pressure.and hemodynamic parameters were evaluated by an eight-channel direct-witing oscillograph,blood gas,pH value,electrolyte,and the damage of important organs were observed before and after sornan injection.Statistical analysis of the data was performed using the self control t test with the SAS 6.12 Software Program.Results In anesthetized dogs intoxicated with sornan,the circulatory failure was characterized by the significant decreases in blood pressure,heart rate and hemodymrnic parameters(P<0.05).Partial pressure of oxygen was less than 60 mmHg,saturation of oxygen Was less than 90% and partial pressure of carbon dioxide was greater than 50 mmHg in arterial blood of the dog model.These results showed mix respiratory failure occurred during intermittent positive pressure.Significant metabolic acidosis was induced by soman[pH(7.345±0.064)vs.(6.956±0.022),P<0.01].The concentralion of sodium ion and chloride ion in blood were changed gently.The concentrations of GTP,GOT,Cr,BUN,CK-MB and LDH were increased significantly(P<0.05),which showed multiple important organs including liver,kidney and heart were damaged by sornan.Conclusions The severe progressive circulatory failure induced by cholinesterase inhibitor sornan leads to the darnage of vital signs and important organs significantly.

Objective:To explore the value of mammography-based radiomics for preoperative prediction of axillary lymph node metastasis in breast carcinoma.Methods:The clinical and X-ray data of female patients with pathologically confirmed breast cancer in Henan People′s Hospital from June 2013 to July 2017 were analyzed retrospectively. A total of 214 patients, aged 30-85 (53±11) years, were randomly divided into training set ( n=153) and verification set ( n=61) according to the ratio of 3∶1. According to pathological findings of the axillary lymph node metastasis, 99 cases were divided into positive group and 115 cases into negative group. The lesions were segmented and extracted in X-ray images of mediolateral oblique (MLO) and cranial caudal (CC). Three, nine and seven axillary lymph node metastasis related histologic features were selected from the high dimensional features of CC, MLO and CC combined MLO images by lasso regression model. According to the characteristics of imaging and clinical characteristics, the prediction model was constructed. The prediction ability of the model was verified by 10% cross validation. Results:The lymph node in positive group was larger than negative groups, the difference was statistically significant ( t=2.611, P<0.05). In the validation set, the area under curve (AUC) values of CC, MLO, CC combined with MLO images, clinical features and clinical features combined with CC and MLO images were 0.680, 0.723, 0.740, 0.558 and 0.714, respectively. Among them, CC combined with MLO images had the highest prediction efficiency, and AUC values were higher than CC alone, MLO images and CC combined with MLO images. Conclusions:Quantitative radiomics features of breast tumor extracted from digital mammograms are helpful for preoperatively predicting axillary lymph node metastasis. Future larger studies are needed to further evaluate these findings.

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: = 125; validation dataset, = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
Carcinoma, Hepatocellular ; diagnostic imaging ; Humans ; Liver Neoplasms ; diagnostic imaging ; Magnetic Resonance Imaging ; Neoplasm Grading ; methods ; ROC Curve