AIM: To investigate the relationship between osteopontin plasma concentrations and the severity of portal hypertension and to assess osteopontin prognostic value.METHODS: A cohort of 154 patients with confirmed liver ...AIM: To investigate the relationship between osteopontin plasma concentrations and the severity of portal hypertension and to assess osteopontin prognostic value.METHODS: A cohort of 154 patients with confirmed liver cirrhosis (112 ethylic, 108 men, age 34-72 years) were enrolled in the study. Hepatic venous pressure gradient (HVPG) measurement and laboratory and ultrasound examinations were carried out for all patients. HVPG was measured using a standard catheterization method with the balloon wedge technique. Osteopontin was measured using the enzyme-linked immunosorbent assay (ELISA) method in plasma. Patients were followed up with a specific focus on mortality. The control group consisted of 137 healthy age- and sex- matched individuals.RESULTS: The mean value of HVPG was 16.18 ± 5.6 mmHg. Compared to controls, the plasma levels of osteopontin in cirrhotic patients were significantly higher (P < 0.001). The plasma levels of osteopontin were positively related to HVPG (P = 0.0022, r = 0.25) and differed among the individual Child-Pugh groups of patients. The cut-off value of 80 ng/mL osteopontin distinguished patients with significant portal hypertension (HVPG above 10 mmHg) at 75% sensitivity and 63% specificity. The mean follow-up of patients was 3.7 ± 2.6 years. The probability of cumulative survival was 39% for patients with HVPG > 10 mmHg and 65% for those with HVPG ≤ 10 mmHg (P = 0.0086, odds ratio (OR), 2.92, 95% confidence interval (CI): 1.09-7.76). Osteopontin showed a similar prognostic value to HVPG. Patients with osteopontin values above 80 ng/mL had significantly lower cumulative survival compared to those with osteopontin ≤ 80 ng/mL (37% vs 56%, P = 0.00035; OR = 2.23, 95%CI: 1.06-4.68).CONCLUSION: Osteopontin is a non-invasive parameter of portal hypertension that distinguishes patients with clinically significant portal hypertension. It is a strong prognostic factor for survival.展开更多
The complex process of soil freezing which relates to moisture field, temperature and stress field usually accompanies water migration and crystallization. The mechanism of water migration in thefrozen fringe is blurr...The complex process of soil freezing which relates to moisture field, temperature and stress field usually accompanies water migration and crystallization. The mechanism of water migration in thefrozen fringe is blurry though there have rather mature theory analyzing water migration in the unfrozen zone and fully-frozen zone. It is a visualized and easy method to calculate the potential gradient of frozen fringe by frost heave amount, the duration of the steady state of frost heaving and the coefficientof permeability based on the Darcy penetration theory, not directly considering water driving force, ice segregation temperature and the thickness of frozen fringe. The method isfeasible by comparing the calculated amount of frost-heaving with the test data.展开更多
To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in c...To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.展开更多
基金Supported by The Internal Grant Agency of the Czech Ministry of Health(http://iga.mzcr.cz/public Web/),No.NT 12290/4the Charles University in Prague(http://www.cuni.cz/UKEN-1.html),No.SVV 260156/2015the Czech Ministry of Health(http://mzcr.cz),No.MZCR-RVO VFN64165
文摘AIM: To investigate the relationship between osteopontin plasma concentrations and the severity of portal hypertension and to assess osteopontin prognostic value.METHODS: A cohort of 154 patients with confirmed liver cirrhosis (112 ethylic, 108 men, age 34-72 years) were enrolled in the study. Hepatic venous pressure gradient (HVPG) measurement and laboratory and ultrasound examinations were carried out for all patients. HVPG was measured using a standard catheterization method with the balloon wedge technique. Osteopontin was measured using the enzyme-linked immunosorbent assay (ELISA) method in plasma. Patients were followed up with a specific focus on mortality. The control group consisted of 137 healthy age- and sex- matched individuals.RESULTS: The mean value of HVPG was 16.18 ± 5.6 mmHg. Compared to controls, the plasma levels of osteopontin in cirrhotic patients were significantly higher (P < 0.001). The plasma levels of osteopontin were positively related to HVPG (P = 0.0022, r = 0.25) and differed among the individual Child-Pugh groups of patients. The cut-off value of 80 ng/mL osteopontin distinguished patients with significant portal hypertension (HVPG above 10 mmHg) at 75% sensitivity and 63% specificity. The mean follow-up of patients was 3.7 ± 2.6 years. The probability of cumulative survival was 39% for patients with HVPG > 10 mmHg and 65% for those with HVPG ≤ 10 mmHg (P = 0.0086, odds ratio (OR), 2.92, 95% confidence interval (CI): 1.09-7.76). Osteopontin showed a similar prognostic value to HVPG. Patients with osteopontin values above 80 ng/mL had significantly lower cumulative survival compared to those with osteopontin ≤ 80 ng/mL (37% vs 56%, P = 0.00035; OR = 2.23, 95%CI: 1.06-4.68).CONCLUSION: Osteopontin is a non-invasive parameter of portal hypertension that distinguishes patients with clinically significant portal hypertension. It is a strong prognostic factor for survival.
文摘The complex process of soil freezing which relates to moisture field, temperature and stress field usually accompanies water migration and crystallization. The mechanism of water migration in thefrozen fringe is blurry though there have rather mature theory analyzing water migration in the unfrozen zone and fully-frozen zone. It is a visualized and easy method to calculate the potential gradient of frozen fringe by frost heave amount, the duration of the steady state of frost heaving and the coefficientof permeability based on the Darcy penetration theory, not directly considering water driving force, ice segregation temperature and the thickness of frozen fringe. The method isfeasible by comparing the calculated amount of frost-heaving with the test data.
基金The National Natural Science Foundation of China(No.51478114,51778136)
文摘To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.
