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 investigate the value of CT quantitative parameters of tumor and spleen in predicting the clinical stage of gastric cancer (Ⅰ/Ⅱ stage and Ⅲ/Ⅳ stage).Methods:This study was a case-control study. The data of 145 patients with gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from February 2019 to June 2021 were retrospectively collected, including 70 cases of Ⅰ/Ⅱ stage and 75 cases of Ⅲ/Ⅳ stage. On the baseline CT images, the tumor related parameters, including tumor thickness, length of tumor, CT attenuation of tumor unenhanced phase, CT attenuation of tumor arterial phase, CT attenuation of tumor venous phase were measured. The spleen related parameters, including splenic thickness, CT attenuation of splenic unenhanced phase, CT attenuation of splenic arterial phase, CT attenuation of splenic venous phase, and standard deviation of CT attenuation (CTsd) in splenic unenhanced phase were also measured. The independent sample t test or Mann-Whitney U test was used to compare the parameters between the Ⅰ/Ⅱ stage and Ⅲ/Ⅳ stage patients. The multi-factor logistic regression analysis was used to find the independent predictors of gastric cancer clinical stage, and establish the combined parameters. The efficiency to the diagnosis of gastric cancer stage of single and combined parameters was evaluated using the operating characteristic curve, and the DeLong test was used to compare the differences of area under the curve (AUC). Results:There were significant differences in tumor thickness, length of tumor, CT attenuation of tumor venous phase, CT attenuation of splenic unenhanced phase, CT attenuation of splenic venous phase, CTsd in splenic unenhanced phase between the Ⅰ/Ⅱ stage and Ⅲ/Ⅳ stage of gastric cancer ( P<0.05). Multivariate analysis showed that tumor thickness ( OR=1.073, 95% CI 1.026-1.123, P=0.002), CT attenuation of splenic venous phase ( OR=1.040, 95% CI 1.011-1.070, P=0.006) and CTsd in splenic unenhanced phase ( OR=1.625, 95% CI 1.330-1.987, P<0.001) were independent risk factors for the clinical stage of gastric cancer and the combined parameters were established. The AUC values of tumor thickness, CT attenuation of splenic venous phase, CTsd in splenic unenhanced phase and combined parameters were 0.655, 0.614, 0.749 and 0.806, respectively. The AUC of combined parameters was higher than those of tumor thickness and CT attenuation of splenic venous phase, and the differences were statistically significant ( Z=3.37, 3.82, both P<0.001). Conclusion:Tumor thickness, CT attenuation of splenic venous phase and CTsd in splenic unenhanced phase are independent risk factors for the clinical stage of gastric cancer, and combined parameters can improve the diagnostic efficiency.

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:Standardized sample resources and high-quality clinical big data are important resources for medical research, only through resource sharing can maximize its utilization.Which can be utilized to the max only through resource sharing.Methods:This paper attempts to explore the sharing mechanism of the resource sharing platform and proposes some aspects such as the platform construction background, management regulations, legal ethical system, data sharing principles, benefit distribution, etc.This article attempts to explore the sharing mechanism based on the resource sharing platform of the respiratory disease biobank, proposes the contents that should be included in the sharing mode.Detailed information including the platform construction background, management procedures, legal and ethical system, data sharing principles and benefit distribution should take into consideration in the operating mechanism of the platform.Results:Establishing a resource sharing platform matches the development of clinical research in China.The tailored sharing model which is suitable for the field of respiratory diseases will also guide the rapid development of clinical research.Conclusions:The construction of a respiratory disease biobank sharing platform is conducive to promoting the opening and sharing of biological samples and information resources in the context of big data.

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.

OBJECTIVE: To investigate the evaluation value of preoperative peripheral blood lymphocyte-to-monocyte ratio (LMR) on the prognosis of patients with stage III colon cancer undergoing radical resection and postoperative adjuvant chemotherapy. METHODS: Electronic medical record were retrospectively retrived for stage III colon cancer patients who underwent radical surgery at Sun Yat-sen University Cancer Center from December 2007 to December 2013. Inclusion criteria were pathologically comfirmed colon adenocarcinoma, complete clinicopathological data, and postoperative XELOX (oxaliplatin + capecitabine) chemotherapy with follow-up of at least 3 months. Patients with neoadjuvant anti-tumor therapy, infectious disease, other malignant tumors and death of non-tumor causes within 3 months after operation were excluded. A total of 258 patients were included in this retrospective cohort study, including 146 males and 112 females with median age of 55 (22 to 85) years. Tumors of 100(38.8%) patients were located in the right hemicolon, and of 158 (61.2%) in the left hemicolon. Tumors of 194(75.2%) patients were highly and moderately differentiated, and of 64 (24.8%) were poorly differentiated. According to the TNM tumor pathological stage of AJCC 7th edition, 196 (76.0%) patients were stage IIIA to IIIB, and 62(24.0%) patients were stage IIIC. The median preoperative CEA was 3.8 (0.3 to 287.5) μg /L and the median cycle of the adjuvant chemotherapy was 6 (1 to 8). The cut-off value of preoperative LMR in prediction of 3-year overall survival (OS) outcome was determined by receiver operating characteristic (ROC) curve analysis. All patients were divided into low LMR group and high LMR group according to the critical value. Clinicopathological characteristics between the two groups were compared by using chi-square test or Fisher's exact test as appropriate. The 3-year disease-free survival and overall survival rate were estimated with the Kaplan-Meier method, and differences between two groups were assessed with the log-rank test. Univariate and multivariate analyses were performed through Cox regression model. RESULTS: ROC curve showed that the cut-off value of preoperative LMR in predicting 3-year overall survival was 4.29. Then 143 patients were divided into low LMR group (LMR<4.29) and 115 patients into high LMR group (LMR ≥ 4.29). Compared with high LMR group, the low LMR group presented higher proportions of male [62.2%(89/143) vs. 50.4%(58/115), χ²=4.167, P=0.041], right hemicolon cancer [44.8% (64/143) vs. 31.3% (36/115), χ²=4.858, P=0.028], and the largest tumor diameter>4 cm [60.1% (86/143) vs. 33.0% (38/115), χ²=18.748, P<0.001]. During a median follow-up of 46.0 (range, 3.0 to 74.0) months, 3-year disease-free survival rate was 83.8% in high LMR group and 78.9% in low LMR group, which was not significantly different (P=0.210). While 3-year overall survival rate in low LMR group was significant lower than that in high LMR group (86.6% vs. 97.2%, P=0.018). Univariate analysis revealed that preoperative low LMR (HR=2.841, 95%CI: 1.146 to 7.043, P=0.024), right hemicolon cancer (HR=2.865, 95%CI: 1.312 to 6.258, P=0.008) and postoperative adjuvant chemotherapy≥6 cycles (HR=0.420, 95%CI: 0.188 to 0.935, P=0.034) were the risk factors for poor overall survival. Multivariate analysis identified that preoperative low LMR (HR=2.550, 95%CI: 1.024 to 6.347, P=0.004) and right hemicolon cancer (HR=2.611, 95%CI: 1.191 to 5.723, P=0.017) were the independent risk factors for overall survival. CONCLUSIONS Preoperative peripheral blood LMR level represents an effective prognostic predictor for patients with stage III colon cancer receiving radical therapy. Low LMR indicates the poor prognosis and such patients require aggressive postoperative treatment strategy.
Adenocarcinoma ; blood ; drug therapy ; surgery ; Adult ; Aged ; Aged, 80 and over ; Antineoplastic Combined Chemotherapy Protocols ; administration & dosage ; Chemotherapy, Adjuvant ; Colonic Neoplasms ; blood ; drug therapy ; surgery ; therapy ; Female ; Humans ; Kaplan-Meier Estimate ; Leukocyte Count ; methods ; Lymphocytes ; Male ; Middle Aged ; Monocytes ; Preoperative Care ; Prognosis ; Retrospective Studies ; Young Adult

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 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.