[Objective] Based on the resting state functional magnetic resonance imaging to investigate the abnormal features of the functional connectivity of resting brain neural network in the patients with primary insomnia,by using voxel-wise whole-brain finctional networks analysis of degree centrality (DC) for imaging evidence of neural mechanisms underlying primary insomnia.[Methods] The resting state fMRI were performed in 59 PI patients and 47 age,education,and sex-matched normal healthy subjects.Analysis of DC map changes between the two patient groups and the control group were performed by two sample t test.(threshold at P ＜ 0.05).[Results] Compared with the control group,the patients with PI showed significantly reduced DC value in bilateral medial frontal gyrus (MFG),bilateral anterior cingulate gyrus (ACG),and right insula;and increased DC value in right middle temporal gyrus (MTG),and left cuneus,(CUN),P ＜ 0.05.[Conclusion]Changes of DC value occurred in some region of brain in the P[patient groups when compared with the control group.It was indicated that DC,as a novel resting-state fMRI parameter in the voxel-wise whole-brain functional networks,might be an appealing alternative approach for further study on pathologic and neuropsychological states of PI.

[Objective]To investigate the micro-structural alterations within whole brain white matter(WM) in cough syrup abuse addiction patients containing codeine,and to explore the correlation between aberrant WH and abuse time of cough medicine abuse patients.[Methods]Thirty cough syrup abuse addiction patients containing codeine and 30 controls participated in the study. Structural changes in FA and(mean diffusivity)MD were examined in cough syrup abuse addiction patients containing codeine which derived from DTI tractography. Pearson correlation analysis was performed to compare the mean FA value and duration of cough syrup abuse addiction in patients.[Results]Cough syrup abuse addiction patients containing codeine had lower FA value in bilateral anterior limb of internal capsule(ALIC)and higher MD in the bilateral hippocampus and insula,right anterior cingulate cortex(ACC)and superior temporal gyrus,compared to the controls. Cough syrup abuse addiction group also had positive correlation between mean FAvalues and duration of cough syrup abuse addiction in patients.[Conclusion]Micro-structural alterations within whole brain white matter(WM)are found in cough syrup abuse addiction patients containing codeine. This disturbance progresses as duration increases of cough syrup abuse addiction in patients.

Objective to establish a combined diagnosis model of scleroderma related genes based on gene expression comprehensive database(GEO)and artificial neural network(ANN)and to evaluate its effect and to predict and analyze targeted traditional Chinese medicine.Methods two scleroderma chips GSE23741 and GSE95065 were obtained from the GEO database as the training group data set.Random forest and lasso regression algorithms were used to screen the key genes of scleroderma and construct the ANN model for the diagnosis of scleroderma.The validation data sets GSE76807,GSE32413 and GSE59785 were used to verify the model,and the area under curve(AUC)analysis was used to evaluate the clinical application value of ANN model.The relative expression of key gene mRNA was verified by RT-qPCR experiment.The CIBERSORT algorithm was used to estimate the bioinformatics association between scleroderma and the screened biomarkers.Finally,the key genes were used to screen the targeted traditional Chinese medicine.Results A total of 167 differential genes were obtained.Furthermore,the five most relevant key genes(SERPINE2,SFRP4,SUGCT,FBLN5,NRXN2)were screened by machine learning,and the artificial neural network diagnosis model was constructed.The model was used to draw the subject operating characteristic(ROC)curves diagnosed by the training group and the verification group,and the AUC value of the training group was 1.000.The AUC of verification group were 0.770,0.795 and 0.872 respectively.The result of RT-qPCR experiment is consistent with that of machine learning algorithm.Immune cell infiltration analysis showed that the relative content of memory CD4+T cells was significantly increased in scleroderma group,while the relative content of γ δ T cells in normal group was significantly increased.Key genes are associated with macrophage M1,T cells,memory activated CD4+T cells,resting mast cells,CD8+T cells and so on.According to the key genes,12 traditional Chinese medicines were screened.Most of the four qi and five flavors belong to warm,cold,flat,sweet,pungent and bitter,and most of them belong to the meridians of liver,spleen and lung.Conclusion the artificial neural network diagnosis model of key genes of scleroderma is constructed,which can be used in clinical diagnosis of scleroderma,and the potential targeted traditional Chinese medicine for the treatment of scleroderma is predicted,which provides a new perspective for exploring the pathogenesis of scleroderma.

Objective:To investigate the effect of neoadjuvant chemotherapy on the prognosis of gastric neuroendocrine cancer.Methods:This study included 102 patients with gastric neuroendocrine cancer, the disease-free survival rate (DFS) and overall survival rate (OS) were compared between two groups according whether they were given neoadjuvant chemotherapy before radical resection.Results:Ninteen of the 102 patients received neoadjuvant chemotherapy combined with surgery, while the other 83 patients received upfront surgery . The 1-year survival rate of the direct operation group and the NAC group was 83.0% and 51.8%, respectively, and the 3-year survival rate was 63.0% and 33.3%, respectively ( χ2=9.182, P=0.002). The 1-year disease-free survival rate was 80.4% and 38.5%, respectively, and the 3-year disease-free survival rate was 59.8% and 25.7%, respectively ( χ2=11.142, P=0.001). Subgroup analysis showed that the difference between the two groups was mainly significant between MANEC patients ( χ2=10.742, P=0.001). Multivariate analysis showed that neoadjuvant therapy was an independent risk factor affecting the overall survival rate (all P<0.05). Univariate analysis shows that only adjuvant chemotherapy is the risk factor affecting disease-free survival ( P<0.05). When the neoadjuvant chemotherapy and the direct surgery were matched 1∶1, the OS and DFS of the direct surgery group were better than those of the NAC patients ( χ2=4.014, 3.954; P=0.045, 0.047). Conclusion:Neoadjuvant chemotherapy failed to improve the prognosis of patients with gastric neuroendocrine cancer/MANEC.

Objective:To study the effects of miR-128-3p on the migration and invasion of the gastric cancer cells.Methods:qRT-PCR was used to detect the expression of miR-128-3p in 126 gastric cancer tissues and adjacent tissues from Jan 2014 to Jan 2016 at He'nan Cancer Hospital. The effect of miR-128-3p on the invasion and migration of gastric cancer cell line was detected.The expression of miR-128-3p related proteins was detected by Western blotting, miRNA on-line target prediction tool for the prediction of miR-128-3p directly regulated downstream target genes.Results:the expression of miR-128-3p in gastric cancer was significantly higher than that in adjacent non-tumor tissues ( P<0.05). The expression of miR-128-3p was correlated with the vascular tumor thrombus, pN staging and pTNM staging, the prognosis of patients with high expression of miR-128-3p was poor (all P<0.05). MiR-128-3p expression was significantly higher in gastric cancer cell lines ( P<0.05). Online target prediction tool and double luciferase reporter gene showed that CLDN18 was a downstream target gene directly regulated by mir-128-3p. Conclusion:The high expression of miR-128-3p is related to the poor prognosis of gastric cancer patients.

Objective:To establish a nomogram to predict overall survival of patients with stage Ⅰ-Ⅲ gastric cancer (GC) based on preoperative inflammatory markers.Methods:Clinicopathological and follow-up data of 1 035 patients with stage Ⅰ-Ⅲ gastric cancer operated at He'nan Cancer Hospital between May 2015 and Oct 2016 were retrospectively collected. A nomogram was established based on prognostic factors. Harrell's concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were used to verify the performance of the model according to differentiation, calibration and clinical utility.Results:A total of 1 035 patients were enrolled . The median follow-up time was 41.9 months; According to the optimal cutoff value, 170 were with elevated neutrophil-to-lymphocyte ratio (NLR) and 865 with a reduced　ratio; 562 in elevated platelet-to-lymphocyte ratio (PLR) vs. 473 in the reduced group; fibrinogen/albumin ratio (FAR) elevated in 108 group vs. 972 in the reduced group; 180 in the prognostic nutritional index score (PNI) elevated group and 855 in the reduced group. Two hundred and sixty-seven patients were categorized at stage Ⅰ, 334 at stage Ⅱ ,434 at stage Ⅲ. Multivariate regression analysis showed tumor location, vascular tumor thrombus, pTNM stage, FAR, PNI and NLR were independent prognostic factors (all P<0.05). The C-index of the nomogram was 0.723 (95% CI: 0.710 -0.736) and had better clinical utility than the American Joint Committee on Cancer (AJCC) 8th TNM staging system 0.693 (95% CI, 0.681 -0.705). The calibration curve of the nomogram showed that the predicted survival rate was consistent with the actual survival rate in GC patients. Compared to AJCC 8th pTNM staging system, the DCA curve indicate that the nomogram has a higher net income. Conclusion:The nomogram predicting overall survival of patients with stage Ⅰ-Ⅲ gastric cancer is established and verified , which provides better individual prediction than TNM staging system.

Objective:To explore the prognostic value of combined fibrinogen/albumin ratio (FAR) and CA724 in patients with stage Ⅱ/Ⅲ gastric cancer after radical resection.Methods:A total of 932 patients were enrolled in the study, and the best cut-off values of CEA, FAR, NLR and other variables were obtained through ROC curve analysis. According to the FAR-CA724 score, patients were divided into 3 groups: FAR-CA724=0 (CA724<3.43 ng/ml and FAR<0.083), FAR-CA724=1 (CA724≥3.43 ng/ml and FAR≥0.083) and FAR-CA724=2 (CEA≥3.43 ng/ml and FAR≥0.083).Results:After FAR-CA724 grouping, the patient's age (χ 2=12.02, P=0.002), gender (χ 2=15.91, P<0.001), tumor size (χ 2=18.22, P<0.001), hypertension (χ 2=6.35, P=0.042), tumor location (χ 2=26.09, P<0.001), degree of differentiation (χ 2=12.46, P=0.002) and pTNM staging (χ 2=6.68, P=0.035) are significantly different. Survival analysis showed that there were significant differences in OS between the three groups of patients (FAR-CA724=0, 1, and 2: 88.2%, 64.3% and 37.8%, respectively, P<0.001). By multivariate analysis FAR-CA724 is an independent risk factor affecting OS in patients with stage Ⅱ/Ⅲ gastric cancer after radical surgery. Conclusions:Preoperative FAR-CA724 may be a potential blood marker for predicting the prognosis of GC patients.

Objective The aim of this study is to understand the impairment and compensation mechanism of brain function in untreated primary insomnia (PI) patients.The approach of fractional amplitude of low frequency fluctuation (fALFF) is used to analyze raw data between the PI patients and the normal control group in resting state using functional magnetic resonance imaging (fMRI).Methods Fifty-nine PI patients,admitted to our hospital from November 2015 to November 2016,and 47 age-,education-,and gender-matched normal healthy subjects were chosen in our study.Pittsburg sleep quality index (PSQI),insomnia severity index (ISI) were employed to evaluate the sleep quality.Self-rating anxiety scale (SAS) and self-rating depression scale (SDS) were employed to evaluate the emotion.Resting state fMRI and fALFF analyses were used to compare the functional regional activities.The correlations of fALFF data with PSQI,SAS and SDS scores were analyzed.Results In PI patients,2 had mild to moderate insomnia,41 had moderate insomnia,and 16 had serious insomnia.ISI scores in the normal healthy subjects were less than 7.The PSQI,SAS,SDS and ISI scores in the PI patients were significantly higher than those in the normal healthy subjects (P＜0.05).As compared with the control group,the PI group had significantly increased fALFF value in the right hippocampus (HIP),right parahippocampa gyms,right amygdala,and bilateral thalamus.The fALFF value was positively correlated PSQI,SASandSDSscores (r=0.582,P=0.000;r=0.617,P=0.000;r=0.653,P=0.000).Conclusion Some brain regions in the PI patients are abnormal in the resting state,which can reflex functional regional activities of PI patients.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.