Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the incidence status,influencing factors and short-term prognosis characteristics of early onset coronary heart disease in women in Wansheng District of Chongqing,and to provide scientific basis for formulating regional prevention and treatment strategies.Methods:This study was a single-center retrospective study,100 coronary heart disease in women from January 2022 to December 2023 at Chongqing Wansheng Economic and Technological Development Zone People's Hospital were prospective selected,and they were divided into early onset group of 40 cases(≤ 65 years old)and late onset group of 60 cases(＞65 years old)based on their age of onset.Another 60 healthy women who underwent physical examinations during the same period to exclude coronary heart disease were selected as the control group.Univariate factor and multiple factor logistic regression analysis were used to identify independent risk factors for early onset coronary heart disease in women.Draw receiver operating characteristic(ROC)curve for the subjects,the efficacy of risk factors in predicting early onset coronary heart disease based on the area under the curve(AUC)of ROC curve were evaluated.Patients were followed up for 1 year to observe the occurrence of major adverse cardiovascular events(MACE).Result:Among 100 fcoronary heart disease in women,the early onset group accounted for 40.00％(40/100).Univariate analysis showed that age,hyperlipidemia history,smoking history,hypertension history,family history,diabetes history,total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C)were related to the early onset coronary heart disease.Multivariate analysis showed that,hyperlipidemia history(OR=4.124,95％CI:2.343-7.217),smoking history(OR=3.564),hypertension(OR=3.253),family history(OR=2.981),diabetes history(OR=2.873)were independent risk factors.ROC curve analysis results showed that joint evaluation had the best predictive value,with AUC of 0.829,which was higher than the AUC of individual evaluation for each factor.The incidence of MACE in the early onset group(45.00％)was significantly higher than that in the late onset group(P＜0.05).Conclusion:Early onset coronary heart disease in women in Wansheng District of Chongqing is related to the hyperlipidemia history,smoking,hypertension history,family history and diabetes history.The incidence of MACE in early-onset patients followed up for 1 year is higher than that in late-onset patients.

Objective:To explore the incidence status,influencing factors and short-term prognosis characteristics of early onset coronary heart disease in women in Wansheng District of Chongqing,and to provide scientific basis for formulating regional prevention and treatment strategies.Methods:This study was a single-center retrospective study,100 coronary heart disease in women from January 2022 to December 2023 at Chongqing Wansheng Economic and Technological Development Zone People's Hospital were prospective selected,and they were divided into early onset group of 40 cases(≤ 65 years old)and late onset group of 60 cases(＞65 years old)based on their age of onset.Another 60 healthy women who underwent physical examinations during the same period to exclude coronary heart disease were selected as the control group.Univariate factor and multiple factor logistic regression analysis were used to identify independent risk factors for early onset coronary heart disease in women.Draw receiver operating characteristic(ROC)curve for the subjects,the efficacy of risk factors in predicting early onset coronary heart disease based on the area under the curve(AUC)of ROC curve were evaluated.Patients were followed up for 1 year to observe the occurrence of major adverse cardiovascular events(MACE).Result:Among 100 fcoronary heart disease in women,the early onset group accounted for 40.00％(40/100).Univariate analysis showed that age,hyperlipidemia history,smoking history,hypertension history,family history,diabetes history,total cholesterol(TC),low-density lipoprotein cholesterol(LDL-C)were related to the early onset coronary heart disease.Multivariate analysis showed that,hyperlipidemia history(OR=4.124,95％CI:2.343-7.217),smoking history(OR=3.564),hypertension(OR=3.253),family history(OR=2.981),diabetes history(OR=2.873)were independent risk factors.ROC curve analysis results showed that joint evaluation had the best predictive value,with AUC of 0.829,which was higher than the AUC of individual evaluation for each factor.The incidence of MACE in the early onset group(45.00％)was significantly higher than that in the late onset group(P＜0.05).Conclusion:Early onset coronary heart disease in women in Wansheng District of Chongqing is related to the hyperlipidemia history,smoking,hypertension history,family history and diabetes history.The incidence of MACE in early-onset patients followed up for 1 year is higher than that in late-onset patients.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective: To investigate dose-response associations between fluid overload (FO) and hospital mortality in patients with sepsis. Methods: The current cohort study was prospective and multicenter. Data were derived from the China Critical Care Sepsis Trial, which was conducted from January 2013 to August 2014. Patients aged≥18 years who were admitted to intensive care units (ICUs) for at least 3 days were included. Fluid input/output, fluid balance, fluid overload (FO), and maximum FO (MFO) were calculated during the first 3 days of ICU admission. The patients were divided into three groups based on MFO values: MFO<5%L/kg, MFO 5%-10%L/kg, and MFO≥10% L/kg. Kaplan-Meier analysis was used to predict time to death in hospital in the three groups. Associations between MFO and in-hospital mortality were evaluated via multivariable Cox regression models with restricted cubic splines. Results: A total of 2 070 patients were included in the study, of which 1 339 were male and 731 were female, and the mean age was (62.6±17.9) years. Of 696 (33.6%) who died in hospital, 968 (46.8%) were in the MFO<5%L/kg group, 530 (25.6%) were in the MFO 5%-10%L/kg group, and 572 (27.6%) were in the MFO≥10%L/kg group. Deceased patients had significantly higher fluid input than surviving patients during the first 3 days [7 642.0 (2 874.3, 13 639.5) ml vs. 5 738.0 (1 489.0, 7 153.5)ml], and lower fluid output [4 086.0 (1 367.0, 6 354.5) ml vs. 6 130.0 (2 046.0, 11 762.0) ml]. The cumulative survival rates in the three groups gradually decreased with length of ICU stay, and they were 74.9% (725/968) in the MFO<5% L/kg group, 67.7% (359/530) in the MFO 5%-10%L/kg group, and 51.6% (295/572) in the MFO≥10%L/kg group. Compared with the MFO<5%L/kg group, the MFO≥10%L/kg group had a 49% increased risk of inhospital mortality (HR=1.49, 95%CI 1.28-1.73). For each 1% L/kg increase in MFO, the risk of in-hospital mortality increased by 7% (HR=1.07, 95% CI 1.05-1.09). There was a"J-shaped"non-linear association between MFO and in-hospital mortality with a nadir of 4.1% L/kg. Conclusion: Higher and lower optimum fluid balance levels were associated with an increased risk of in-hospital mortality, as reflected by the observed J-shaped non-linear association between fluid overload and inhospital mortality.
Humans ; Male ; Female ; Adult ; Middle Aged ; Aged ; Aged, 80 and over ; Hospital Mortality ; Cohort Studies ; Prospective Studies ; Water-Electrolyte Imbalance ; Sepsis ; Intensive Care Units ; Retrospective Studies

Objective: To investigate the prognostic factors of children with congenital heart disease (CHD) who had undergone cardiopulmonary resuscitation (CPR) in pediatric intensive care unit (PICU) in China. Methods: From November 2017 to October 2018, this retrospective multi-center study was conducted in 11 hospitals in China. It contained data from 281 cases who had undergone CPR and all of the subjects were divided into CHD group and non-CHD group. The general condition, duration of CPR, epinephrine doses during resuscitation, recovery of spontaneous circulation (ROSC), discharge survival rate and pediatric cerebral performance category in viable children at discharge were compared. According to whether malignant arrhythmia is the direct cause of cardiopulmonary arrest or not, children in CHD and non-CHD groups were divided into 2 subgroups: arrhythmia and non-arrhythmia, and the ROSC and survival rate to discharge were compared. Data in both groups were analyzed by t-test, chi-square analysis or ANOVA, and logistic regression were used to analyze the prognostic factors for ROSC and survival to discharge after cardiac arrest (CA). Results: The incidence of CA in PICU was 3.2% (372/11 588), and the implementation rate of CPR was 75.5% (281/372). There were 144 males and 137 females with median age of 32.8 (5.6, 42.7) months in all 281 CPA cases who received CPR. CHD group had 56 cases while non-CHD had 225 cases, with the percentage of 19.9% (56/281) and 80.1% (225/281) respectively. The proportion of female in CHD group was 60.7% (34/56) which was higher than that in non-CHD group (45.8%, 103/225) (χ²=4.00, P=0.045). There were no differences in ROSC and rate of survival to discharge between the two groups (P>0.05). The ROSC rate of children with arthythmid in CHD group was 70.0% (28/40), higher than 6/16 for non-arrhythmic children (χ²=5.06, P=0.024). At discharge, the pediatric cerebral performance category scores (1-3 scores) of CHD and non-CHD child were 50.9% (26/51) and 44.9% (92/205) respectively. Logistic regression analysis indicated that the independent prognostic factors of ROSC and survival to discharge in children with CHD were CPR duration (odds ratio (OR)=0.95, 0.97; 95%CI: 0.92~0.97, 0.95~0.99; both P<0.05) and epinephrine dosage (OR=0.87 and 0.79, 95%CI: 0.76-1.00 and 0.69-0.89, respectively; both P<0.05). Conclusions: There is no difference between CHD and non-CHD children in ROSC and survival rate of survival to discharge was low. The epinephrine dosage and the duration of CPR are related to the ROSC and survival to discharge of children with CHD.
Cardiopulmonary Resuscitation ; Child ; Child, Preschool ; Female ; Heart Arrest/therapy* ; Heart Defects, Congenital/therapy* ; Humans ; Intensive Care Units, Pediatric ; Male ; Retrospective Studies

Immuno-fluorescence technique can qualitatively determine certain nuclear translocation, of which NF-κB/ p65 implicates the activation of NF-κB signal pathways. Immuno-fluorescence analysis software with independent property rights is able to quantitatively analyze dynamic location of NF-κB/p65 by computing relative fluorescence units in nuclei and cytoplasm. We verified the quantitative analysis by Western Blot. When we applied the software to analysis of nuclear translocation in lipopolysaccharide (LPS) induced (0. 5 h, 1 h, 2 h, 4 h) primary human umbilical vein endothelial cells (HUVECs) , we found that nuclear translocation peak showed up at 2h as with calculated Western blot verification results, indicating that the inventive immuno-fluorescence analysis software can be applied to the quantitative analysis of immuno-fluorescence.
Active Transport, Cell Nucleus ; Cell Nucleus ; metabolism ; Cytoplasm ; metabolism ; Fluorescent Antibody Technique ; Human Umbilical Vein Endothelial Cells ; Humans ; NF-kappa B p50 Subunit ; metabolism ; Software

Objective To investigate the toxicity of 2-dodecenoic acid on human hepatocarcinoma cells.Methods The present study is to in-vestigate the toxic effects of 2 -dodecenoic acid on human hepatoma tumor cells by using trypan blue staining , MTT assay , and flow cytometry analysis.Results After cancer cells were treated with concentrations of 0.1 , 0.3 ,0.9 mg· L-1 2-dodecenoic acid at 6 h, 12 h, 18 h, 24 h, and 48 h, respectively.Compared to the control group , 48 h after treat-ment, the inhibition rates of the hepatoma cells proliferation in treatment group were increased by 45.46%, 67.16%, 79.18%at the above three concentrations , respectively ( P<0.01 ).Flow cytometry analysis showed that the apoptosis rates after the 2-dodecenoic acid treatment were in-creased by 14.8%, 20.2% and 24.2% over the control group.Conclusion These results indicated that the 2-dodecenoic acid has a significant toxic effect on human hepatoma cells.

Objective To evaluate the prognostic value of the model for end-stage liver disease (MELD) and △MELD in liver failure patients infected with hepatitis B virus.Methods Based on prospective study design,98 hospitalized cases were studied and followed up for 24 weeks.The clinical data were recorded.We calculated the score of MELD and △MELD,and also compare the score between the survival group and death group.Using ROC curve plotting obtained the better decisive threshold.The case fatality rate were compared at different time points which the patients were classified by the best critical value of MELD and △MELD.We draw the Kaplan-Meier survival curve of different group and analyse the change of survival rate by log-rank analysis.Results 52 of 97 patients died and 46 survive during 24 weeks of followup.There was significant difference between the two groups for MELD and △MELD (P ＜ 0.01 ).The case fatality rate in group which MELD ≥ 23 was obviously higher than in that MELD ＜ 23. The rate in group which △MELD ＞4.5 was obviously higher than in that △MELD ＜ 4.5 (P ＜ 0.001 ).The area under curve (AUC) for the twelfth and 24th week' s prognosis judgment of △MELD(0.823,0.815) was larger than that of MELD ( 0.680,0.684 ) ( P ＜ 0.05 ).Survival analyses (Kaplan-Meier) indicated that there were significant differences in cumulative survival rates among the groups which were grouped by optimization critical value(P =0.000).Conclusions The scoring system of MELD also applied to the forecasting of prognosis for severe hepatitis B patients in China.The accuracy of △MELD to predict the prognosis was higher than that of MELD.The combination of MELD and △MELD showed good clinical practical value.

OBJECTIVESInvestigate the clinical efficacy of cryotherapy ablation treatment for advanced hepatocellular carcinoma. analyse the predictive factors of cryotherapy ablation treatment.

METHODSThere were 190 cases of hepatitis B-related HCC patients with advanced HCC from 2005 to 2008 in our hospital. By using clinical cohort method, they included cryoablation group (147 cases) and control group (43 cases), The median survival time and time to disease progression were compared. Evaluate clinical significance of age, gender, location of portal vein tumor thrombus, HBeAg, tumor histological grade, Child-Pugh classification, end-stage liver disease (MELD) score, advanced liver cancer prediction system (ALCPS) score and the Eastern Cooperative Oncology Group performance status (ECOG PS) score for predicting the efficacy of cryoablation. Group rates were compared with the x2 test, survival analysis by using Kaplan-Meier method, survival rates were compared by Log-rank analysis; multiple factor survival analysis by using Cox regression model.

RESULTSMedian survival time of cryoablation group and Control group was 7.5 (4.2 to 14.6) months and 3.2 (1.2 to 8.6) months, median TTP was 3.5 (2.5 to 4.5) months and 1.5 (1.0 to 3.5 months), the differences were statistically significant ( P less than 0.05 ). Median OS and TTP of advanced HCC patients who had Well-differentiated tumor, Child-pugh A-class and low score of MELD score, ALCPS score, ECOG PS score were significantly longer than the poorly differentiate, Child-Pugh B-class and the high those scores ( P less than 0.05). ECOG PS ( P less than 0.05, 95% CI 1.074 to 2.143) and ALCPS (P less than 0.05, 95% CI 1.005 to 2.121) were independent predictors for OS of advanced HCC.

CONCLUSIONCryoablation treatment can prolong median OS and TTP of advanced HCC; ECOG PS and ALCPS are important predictors for survival time of advanced HCC.

Carcinoma, Hepatocellular ; surgery ; Cryosurgery ; Female ; Humans ; Liver Neoplasms ; surgery ; Male ; Middle Aged ; Prognosis ; Proportional Hazards Models ; Survival Rate ; Treatment Outcome