Objective To investigate the short and longterm effect of the treatment on acute coronary syndrome(ACS) with percutaneous coronary angioplasty and stenting.Methods 65 patients suffering from ACS were treated with angiography to illuminate the features of lesions and proceeded with percutaneous coronary angioplasty and stenting when the angina was not completed controlled with internal medicine.All patients were treated with a new anti-thrombotic regimen,including aspirin,ticlopidine or clopidogrel, and low-molecular-weight heparin.Results The rates of success for patients and lesions were 96.9% and 82.7% respectively.One patient died without myocardial infarction or emergency bypass surgery.56 patients had no symptom of angina after procedure,and 5 patients had non-typical chest pain which disappeared within 2~3 days.During the 3~54 month follow-up,one patient died,one patient developed myocardial infarction and 4 patients underwent target vessel revascularization(repeated angioplasty 2 and bypass surgery 2).Conclusions With the widespread use of aspirin-ticlopidine-low molecular heparin regimen,ACS patients can undergo PTCA and stenting with high success rate,excellent immediate efficacy and long-term efficacy.

Objective To observe the apoptosis or oncosis of pancreatic acinar cells of different severity of acute pancreatitis (AP) and the release level of enzymes in vitro, and to investigate the relationship between them. Methods Two-step enzymatic digestion method was used to separate pancreatic acinar cells into 4 groups. 0. 1 μg/ml of the caerulein was added in the AP group. Caerulein and LPS (bacterial lipopolysaccharide, 10 mg/L) were added in LPS group. Caerulein and OCT (octreotide, 100 ng/ml) were added in OCT group. Medium was added in the control group. AO (acridine orange) and EB (ethidium bromide) double staining method was used to detect the incidence of apoptosis or oncosis of acinar cell. The release of intracellular enzyme was detected by measuring the concentrations of amylase and LDH in cell culture media by colorimetry method. Results The apoptosis index was 2.2 + 0.4, 6.4 ± 0.6, 4.6 + 0.4, 11.2 +1.2 in the control group, AP group, LPS group, OCT group; while the oncosis index was 3.0 +0.4, 17.2 ±1.6, 23.0 ± 2.2, 12.8 ± 1.4 in the control group, AP group, LPS group, OCT group; the release of LDH was (2180 ±240), (8060 ±930), (9460 +920), (6860 ±740) U/dl, the level of amylase was (1750 ± 190),(3820 ±460), (4420 ±480), (2260 ±260)U/L. All the values in the experiment groups were significantly higher than that in control group ( P ＜ 0.05 ). The oncosis index, LDH, amylase in LPS group was significantly higher than that in AP group ( P ＜ 0.05 ), but the apoptosis index in LPS group was significantly lower than that in AP group ( P ＜ 0.05 ). The apoptosis index in OCT group was significantly higher than that in AP group ( P ＜ 0. 05 ), but the oncosis index, LDH, amylase was significantly lower than that in AP group ( P ＜ 0. 05 ).Conclusions Induction of apoptosis and reduction of oncosis in AP pancreatic acinar cells can reduce the release of enzyme in acinar cells.

Objective To study the value of three-dimensional (3D) visualization and virtual surgery system in piggyback liver transplantation. Methods Two patients who suffered from choledocholithiasis were scanned by 64-slice spiral CT and the data were collected. The segmentation of the hepatic CT images was carried out. The 3D model of the liver and the intrahepatic vessels was reconstructed, and was imported to the FreeForm Modeling System in STL format for smoothing and modifying. Piggyback liver transplantation was simulated with the force-feedback equipment (PHANToM). Results The reconstructed 3D model of the liver was vivid, and the process of the virtual piggyback liver transplantation was verisimilar. Conclusions The 3D model of the liver enables the simulation of piggyback liver transplantation. It can reduce the risk and complications of the surgery, and enhance the communication between doctor and patient through designing surgical plan and demonstrating visualized operation before surgery. Virtual liver transplantation is also helpful during the training of medical workers.

Objective. To study the role of endogenous carbon monoxide (CO) in endotoxin shock. Methods. The changes of CO levels and the effects of zinc protoporphyrin IX (ZnPP),an inhibitor of heme-oxygenase (HO), in endotoxin shock and the efficacy of hemin,an inducer of HO were investigated.Results. The plasma CO levels were found to be significantly increased during the course of endotoxin shock. Injection of ZnPP was shown to abrogate the endotoxin-induced hypotension and metabolic derangements markedly. Administration of hemin to healthy rabbits revealed the hypotension and metabolic derangements similar to the animals given endotoxin.Conclusion.CO is a newly found endogenously produced mediator which may play an important role in the pathogenesis of endotoxin shock.

Objective To investigate the status of quality of life and mental health among nurses in ClassⅢ Grade A hospitals in Beijing and to explore the correlation between them and analyze its main influencing factors.Methods From March to August 2017, the MOS Item Short From Health Survey-36 (SF-36) and Symptom Checklist-90 (SCL-90) were used to investigate 480 nurses from four ClassⅢ Grade A hospitals in Beijing. The Pearson correlation analysis and canonical correlation analysis were used to analyze the correlation between the two variables from multiple perspectives to determine the major factors influencing the correlation between them.Results Among 472 nurses of ClassⅢ Grade A hospitals in Beijing recruited in this research, the mean of total score of SF-36 was (66.02±14.26) with (56.76±21.01) for the lowest score in general health. The mean of total score of SCL-90 was (1.69±0.69) with 46.40％ for the positive rate of psychological problems. Pearson correlation analysis showed that the total score of SF-36 was negatively correlated with the total score of SCL-90 (r=-0.712,P<0.01), and canonical correlation analysis revealed that its major influencing factors included the dimensions in general health, mental health and depression. Conclusions Nurses of ClassⅢ Grade A hospitals of Beijing are with a low level of life quality and poor psychologic status with a negative correlation between them. Related functional departments should improve nurses' life quality and mental health from two aspects in physiology and psychology.

OBJECTIVETo report on a Chinese family from Wenzhou with genetically confirmed Kennedy disease and describe its clinical and genetic features.

METHODSThe clinical phenotype and the level of relevant biochemical markers were assessed. To determine the number of CAG repeats in the exon 1 of androgen receptor (AR) gene, genomic DNA was extracted from peripheral blood samples of the family members, amplified by PCR and identified by DNA sequencing.

RESULTSThe proband showed predominantly proximal limb weakness, fasciculation, muscle atrophy, gynecomastia, sexual dysfunction and increased serum creatine kinase. Myopathy and neuropathy were identified by electromyography. Two other affected males and 2 affected female carriers were identified to carry an expanded CAG repeat in the AR gene. The numbers of CAG repeats were found to be 43 in the proband, 43 and 42 in the other two affected males, one of which had similar clinical symptoms to the proband.

CONCLUSIONThe family was diagnosed with Kennedy disease by analysis of the AR gene.

Adolescent ; Adult ; Base Sequence ; Bulbo-Spinal Atrophy, X-Linked ; blood ; diagnosis ; genetics ; Creatine Kinase ; blood ; Female ; Humans ; Male ; Middle Aged ; Molecular Sequence Data ; Pedigree ; Receptors, Androgen ; genetics ; Trinucleotide Repeat Expansion ; Young Adult

Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.

Objective To observe the value of clinical and CT radiomics features for predicting microsatellite instability-high(MSI-H)status of gastric cancer.Methods Totally 150 gastric cancer patients including 30 cases of MSI-H positive and 120 cases of MSI-H negative were enrolled and divided into training set(n=105)or validation set(n=45)at the ratio of 7∶3.Based on abdominal vein phase enhanced CT images,lesions radiomics features were extracted and screened,and radiomics scores(Radscore)was calculated.Clinical data and Radscores were compared between MSI-H positive and negative patients in training set and validation set.Based on clinical factors and Radscores being significant different between MSI-H positive and negative ones,clinical model,CT radiomics model and clinical-CT radiomics combination model were constructed,and their predictive value for MSI-H status of gastric cancer were observed.Results Significant differences of tumor location and Radscore were found between MSI-H positive and negative patients in both training and validation sets(all P＜0.05).The area under the curve(AUC)of clinical model,CT radiomics model and combination model for evaluating MSI-H status of gastric cancer in training set was 0.760,0.799 and 0.864,respectively,of that in validation set was 0.735,0.812 and 0.849,respectively.AUC of clinical-CT radiomics combination model was greater than that of the other 2 single models(all P＜0.05).Conclusion Clinical-CT radiomics combination model based on tumor location and Radscore could effectively predict MSI-H status of gastric cancer.

Objective:To explore and establish an artificial neural network (ANN) model for predicting the efficacy of first-line FOLFOX chemotherapy for metastatic colorectal cancer.Methods:A set of FOLFOX chemotherapy data from a group of patients with metastatic colorectal cancer (mCRC) (GSE104645) was downloaded from the GEO database as a training set. According to the FOLFOX protocol, the efficacy was divided into two groups: the chemo-sensitive group (including complete response and partial response) and the chemo-resistant group (including stable disease and progressive disease), including 31 cases in the sensitive group and 23 in the resistant group. Then, chip data (accessible number: GSE69657) from Fujian Medical University Union Hospital were chosen as a test set. A total of 30 patients were enrolled in the study, including 13 in the sensitive group and 17 in the resistant group. The batch effect correction was performed on the expression values of the two sets of matrices using the R 3.5.1 software Combat package. The gene expression difference of sensitive and resistant group in GSE104645 was analyzed by the GEO2R platform. P<0.05 and the absolute value of log 2FC>0.33 (FC abbreviation of fold change) were used as the threshold value to screen the drug resistance and sensitive genes of the FOLFOX regimen. An ANN was constructed using the multi-layer perceptron (MLP) to perform the FOLFOX regimen on the GSE104645 dataset. The GSE69657 expression matrix and clinical efficacy parameters were then used for retrospective verification. Receiver operating characteristic(ROC) curves were used to evaluate the test results and predictive power. Results:A total of 2, 076 differentially expressed genes in GSE104645 were selected, of which 822 genes were up-regulated and 1, 254 genes were down-regulated in the chemo-resistance group. The down-regulated genes were sensitive genes. GO analysis of the biological processes in which the differentially expressed genes were involved, revealed that they were mainly involved in the regulation of substance metabolism. A total of 39 genes were included in the final model construction. This was a neural network model with two hidden layers. The accuracy of predicting training samples and test samples was 75.7% and 76.5%, respectively, and the area under the ROC curve was 0.875. The chip data set of our department (GSE69657) was set as the test set, and the area under the ROC curve was 0.778.Conclusions:In this study, an artificial neural network model is successfully constructed to predict the efficacy of first-line FOLFOX regimen for metastatic colorectal cancer based on the microarray, and an independent external verification is also conducted. The model has good stability and well prediction efficiency. Besides, the results of this study suggest that the gene functions related to oxaliplatin resistance are mainly enriched in the regulation process of substance metabolism.

Objective:To explore and establish an artificial neural network (ANN) model for predicting the efficacy of first-line FOLFOX chemotherapy for metastatic colorectal cancer.Methods:A set of FOLFOX chemotherapy data from a group of patients with metastatic colorectal cancer (mCRC) (GSE104645) was downloaded from the GEO database as a training set. According to the FOLFOX protocol, the efficacy was divided into two groups: the chemo-sensitive group (including complete response and partial response) and the chemo-resistant group (including stable disease and progressive disease), including 31 cases in the sensitive group and 23 in the resistant group. Then, chip data (accessible number: GSE69657) from Fujian Medical University Union Hospital were chosen as a test set. A total of 30 patients were enrolled in the study, including 13 in the sensitive group and 17 in the resistant group. The batch effect correction was performed on the expression values of the two sets of matrices using the R 3.5.1 software Combat package. The gene expression difference of sensitive and resistant group in GSE104645 was analyzed by the GEO2R platform. P<0.05 and the absolute value of log 2FC>0.33 (FC abbreviation of fold change) were used as the threshold value to screen the drug resistance and sensitive genes of the FOLFOX regimen. An ANN was constructed using the multi-layer perceptron (MLP) to perform the FOLFOX regimen on the GSE104645 dataset. The GSE69657 expression matrix and clinical efficacy parameters were then used for retrospective verification. Receiver operating characteristic(ROC) curves were used to evaluate the test results and predictive power. Results:A total of 2, 076 differentially expressed genes in GSE104645 were selected, of which 822 genes were up-regulated and 1, 254 genes were down-regulated in the chemo-resistance group. The down-regulated genes were sensitive genes. GO analysis of the biological processes in which the differentially expressed genes were involved, revealed that they were mainly involved in the regulation of substance metabolism. A total of 39 genes were included in the final model construction. This was a neural network model with two hidden layers. The accuracy of predicting training samples and test samples was 75.7% and 76.5%, respectively, and the area under the ROC curve was 0.875. The chip data set of our department (GSE69657) was set as the test set, and the area under the ROC curve was 0.778.Conclusions:In this study, an artificial neural network model is successfully constructed to predict the efficacy of first-line FOLFOX regimen for metastatic colorectal cancer based on the microarray, and an independent external verification is also conducted. The model has good stability and well prediction efficiency. Besides, the results of this study suggest that the gene functions related to oxaliplatin resistance are mainly enriched in the regulation process of substance metabolism.