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The quantile regression technique is introduced into the Lotka–Volterra ecosystem analysis framework. The quantile grey Lotka–Volterra model is established to reveal the dynamic trade relationship between China and the United States. An optimisation model is constructed to solve optimum quantile parameters. The empirical results show that the quantile grey Lotka–Volterra model shows higher fitting accuracy and reveals the trade relationships at different quantiles based on quarterly data on China–US trade from 1999 to 2019. The long-term China–US trade relationship presents a prominent predator–prey relationship because exports from China to the US inhibited China’s imports from the United States. Moreover, we divide samples into five stages according to four key events, China’s accession to the WTO, the 2008 global financial crisis, the weak global economic recovery in 2015, and the 2018 China–US trade war, recognising various characteristics at different stages.

Background and Objective: Tacrolimus, a calcineurin inhibitor widely used as a potent immunosuppressant to prevent graft rejection, exhibits nonlinear kinetics in patients with kidney transplantation and nephrotic syndrome. However, whether nonlinear drug metabolism occurs in adult patients undergoing liver transplantation remains unclear, as do the main underlying mechanisms. Therefore, here we aimed to further confirm the characteristics of nonlinearity through a large sample size, and determine the potential influence of nonlinearity and its possible mechanisms.Methods: In total, 906 trough concentrations from 176 adult patients (150 men/26 women; average age: 50.68 ± 9.71 years, average weight: 64.54 ± 11.85 kg after first liver transplantation) were included in this study. Population pharmacokinetic analysis was performed using NONMEM®. Two modeling strategies, theory-based linear compartmental and nonlinear Michaelis–Menten (MM) models, were evaluated and compared. Potential covariates were screened using a stepwise approach. Bootstrap, prediction-, and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine model stability and predictive performance. Finally, Monte Carlo simulations based on the superior model were conducted to design dosing regimens.Results: Postoperative days (POD), Aspartate aminotransferase (AST), daily tacrolimus dose, triazole antifungal agent (TAF) co-therapy, and recipient CYP3A5*3 genotype constituted the main factors in the theory-based compartmental final model, whereas POD, Total serum bilirubin (TBIL), Haematocrit (HCT), TAF co-therapy, and recipient CYP3A5*3 genotype were important in the nonlinear MM model. The theory-based final model exhibited 234 L h−1 apparent plasma clearance and 11,000 L plasma distribution volume. The maximum dose rate (Vmax) of the nonlinear MM model was 6.62 mg day−1; the average concentration at steady state at half-Vmax (Km) was 6.46 ng ml−1. The nonlinear MM final model was superior to the theory-based final model and used to propose dosing regimens based on simulations.Conclusion: Our findings demonstrate that saturated tacrolimus concentration-dependent binding to erythrocytes and the influence of daily tacrolimus dose on metabolism may partly contribute to nonlinearity. Further investigation is needed is need to explore the causes of nonlinear pharmacokinetic of tacrolimus. The nonlinear MM model can provide reliable support for tacrolimus dosing optimization and adjustment in adult patients undergoing liver transplantation.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has a high recurrence rate. Accurate prediction of recurrence risk is urgently required for tailoring personalized treatment programs for individual HCC patients in advance. In this study, we analyzed a gene expression dataset from an HCC cohort with 247 samples and identified five genes including ENY2, GPAA1, NDUFA4L2, NEDD9, and NRP1 as the variables for the prediction of HCC recurrence, especially the early recurrence. The Cox model and risks score were validated in two public HCC cohorts (GSE76427 and The Cancer Genome Atlas (TCGA)) and one cohort from Huashan Hospital, which included a total of 641 samples. Moreover, the multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor in the prediction of HCC recurrence. In addition, we found that ENY2, GPAA1, and NDUFA4L2 were significantly upregulated in HCC of the two validation cohorts, and ENY2 had significantly higher expression levels than another four genes in malignant cells, suggesting that ENY2 might play key roles in malignant cells. The cell line analysis revealed that ENY2 could promote cell cycle progression, cell proliferation, migration, and invasion. The functional analysis of the genes correlated with ENY2 revealed that ENY2 might be involved in telomere maintenance, one of the fundamental hallmarks of cancer. In conclusion, our data indicate that ENY2 may regulate the malignant phenotypes of HCC via activating telomere maintenance.

Abstract Background Aggressive angiomyxoma (AA) is a rare tumor that typically occurs in the pelvis and perineum, most commonly in women of reproductive age. However, no para-ureteral AA has been reported according to the literature. Case presentation We herein describe the first case of para-ureteral AA. A 62-year-old male presented to our institute in March 2017 with a para-ureteral mass that was 15 mm in diameter incidentally. No symptom was observed and laboratory analysis was unremarkable. Magnetic resonance and computed tomography imaging showed a non-enhancing mass abutting the left ureter without causing obstruction. Laparoscopic resection of the mass was performed without injury to the ureter. Pathologic and immunohistochemical results were consistent with AA. Till now, no recurrence was noticed. Conclusions We reported a rare case of para-ureteral AA, along with a literature review. Early diagnosis, proper surgical plan and long-term close follow-up is recommended for its high risk of recurrence and malignant potential.

As an important indicator for measuring the quality of business environment of different countries, ease of doing business (EDB) issued by the World Bank (WB) provides an important reference for investors in making decisions on transnational investment. The calculation method for EDB issued by the WB is improved using a technique for order preference by similarity to an ideal solution (TOPSIS) method based on Mahalanobis distance. Based on various indicator data in 2019, business environments in 121 countries participating in “the Belt and Road Initiative (BRI)” were empirically analysed and compared through such models. The result showed that TOPSIS method based on Mahalanobis distance can more fully utilise information and take the effect of negative ideal points into account. Therefore, compared with ranking method by the WB, TOPSIS method based on Mahalanobis distance is more applicable for ranking BRI countries. The ranking results indicated significant geographical characteristics. The EDB rankings obtained through the WB overestimate the business environments of countries in Central and Eastern Europe while underestimate those in Southeast Asia, Africa, etc. First published online 22 September 2020

Objective: The expression of B-cell lymphoma 2 (Bcl-2) seems to be influenced by the endocrine environment. Numerous reports demonstrate the diverse expression of Bcl-2 family members under sex steroid regulation. With the exception of estrogen-related tumors, androgen-related tumors have shown their characteristics in Bcl-2 expression. In this study, the status of Bcl-2 expression in male hepatocellular carcinoma (HCC) patients was examined to verify the high incidence of HCC in males.Methods: Tumor tissue microarray was used to examine Bcl-2 expression levels in 374 HCC cases including 306 males and 68 females. Kaplan-Meier method, log-rank test, and Cox proportional hazards model were applied to investigate the predictive value of Bcl-2 in HCC patients.Results: Immunohistochemistry analysis showed that male patients with higher Bcl-2 levels had significantly longer median survival time and recurrence time than those with lower levels. However, no significant differences in outcomes were found between different Bcl-2 levels in female patients. When the male patients were stratified into several age points, the level of Bcl-2 expression showed poorer predictive efficiency in the 45–49 and 55–60 age groups in andropause-age patients compared with other age groups. Bcl-2 was an independent prognostic factor for both overall survival (P < 0.0001) and recurrence time (P = 0.0001) in male patients. After excluding male patients in the 45–60 age group, the predictive efficiency was enhanced (n = 147, OS, P = 0.0002, TTR, P < 0.0001).Conclusions: Bcl-2 expression is an independent predictor of survival and recurrence in male HCC. Bcl-2 levels may also be regulated by androgens or androgen receptors in male HCC patients. Bcl-2 levels change and exhibit poor predictive efficiency when androgen levels vary dramatically (andropause age).

The grey prediction model with convolution integral GMC (1, n) is a multiple grey model with exact solutions. To further improve prediction accuracy and describe better the relationship between cause and effect, we introduce nonlinear parameters into GMC (1, n) model and additionally apply a convolution integral to produce an improved forecasting model here designated as NGMC (1, n). The model solving process applied the least-squares method to evaluate the structure parameters of the model: convolution was used to obtain an exact solution with this improved grey model. The nonlinear optimisation took the parameters as the decision variables with the objective of minimising forecasting errors. The GMC (1, 2) and NGMC (1, 2) models were used to predict China’s industrial SO2 emissions from the basis of the economic output level as the influencing factor. Results indicated that NGMC (1, 2) can effectively describe the nonlinear relationship between China’s economic output and SO2 emissions with an improved accuracy over current GMC (1, 2) models.

Coal, oil and gas, and clean energy consumptions were considered to be three competitive populations in a closed system and their dynamic competitive relationships were investigated using a Lotka-Volterra model. The parameters of this model were identified through non-linear least squares estimation method; on this basis, this study analysed the equilibrium of the system and predicted its future development trend. Results show that both coal, and oil and gas, consumptions positively promoted clean energy consumption, while oil and gas exerted a greater influence than coal; moreover, clean energy consumption helps to promote oil and gas consumption, and this phenomenon forms competition for coal consumption. The energy consumption system of China is unstable and her energy supply structure still needs further adjustment in the short-term. Although future energy consumption is still dominated by coal, its market share presents a downward trend, and clean energy is expected to become more popular. [ABSTRACT FROM AUTHOR]

Grey resource model is the core model in the grey ecological resource exploitation theory for describing and forecasting development stages of resource efficiency. In order to define the application range of grey resource model, this study deduces the ranges of secondary parameter packages based on the coverage operations of grey numbers, and then deduces that of the development coefficient. Finally the admissible region of non-homogeneous class ratio for grey resource model is obtained in the form of both mathematical formula and numerical simulation of China's wind power industry and science & technology industry. [ABSTRACT FROM AUTHOR]

The grey prediction model with convolution integral GMC (1, n) is a multiple grey model with exact solutions. To further improve prediction accuracy and describe better the relationship between cause and effect, we introduce nonlinear parameters into GMC (1, n) model and additionally apply a convolution integral to produce an improved forecasting model here designated as NGMC (1, n). The model solving process applied the least-squares method to evaluate the structure parameters of the model: convolution was used to obtain an exact solution with this improved greymodel. The nonlinear optimisation took the parameters as the decision variables with the objective of minimising forecasting errors. TheGMC (1, 2) and NGMC (1, 2) models were used to predict China's industrial SO2 emissions from the basis of the economic output level as the influencing factor. Results indicated that NGMC (1, 2) can effectively describe the nonlinear relationship between China's economic output and SO2 emissions with an improved accuracy over current GMC (1, 2) models. [ABSTRACT FROM AUTHOR]

The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1, n), performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1, n), n interpolation coefficients (taken as unknown parameters) are introduced into the background values of the n variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1, n)model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1, n) model. The modelling results can assist the government in developing future policies regarding high-tech industry management. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate the utility of a preoperative model of end-stage liver disease (MELD) score and Child-Turcotte-Pugh (CTP) score in predicting the prognosis after othotopic liver transplantation (OLT) for chronic severe hepatitis B (CSHB) and explore the prognostic factors. The outcome of 137 patients who underwent OLT using donors after cardiac death (DCDs) for CSHB in our center was reviewed retrospectively. Survival analysis was performed using the Kaplan-Meier method; the log-rank test was used for univariate analysis; and the Cox proportional hazards regression model was used for prognostic factors screening. The overall mortality rate was 33.6% (46/137); and 1-month, 6-month, 1-year, and 5-year patient survival rates were 75.8, 72.0, 71.0, and 60.1%, respectively. Most patients (33/46) died during the first month after OLT. The area under the curve values generated by the receiver operating characteristics curves were 0.82 [95% confidence interval (CI) 0.72–0.92] and 0.68 (95% CI 0.58–0.79), respectively ( P < 0.01), for the MELD and CTP models in predicting 1-month mortality after OLT. Patients with a preoperative MELD score <33.8 or a CTP score <12.5 had significantly better prognosis than those with higher scores ( P < 0.05). Other mortality predictors include hepatic encephalopathy, preoperative infection, serum creatinine ≥1.5 mg/dl. The MELD score was more efficient than the CTP score for evaluating the short-term prognosis in patients with CSHB undergoing OLT using DCDs, which should be taken into consideration during graft allocation. [ABSTRACT FROM AUTHOR]

Aim: To establish a model to predict long-term survival of hepatocellular carcinoma (HCC) patients after liver transplantation (MHCAT)., Methods: Two hundred and twenty-three patients with HCC were followed for at least six years to identify independent risk factors for long-term survival after liver transplantation (LT). The criteria for HCC liver transplantation included the Milan, University of California San Francisco, Hangzhou and Shanghai Fudan criteria. The Cox regression model was used to build MHCAT specifying these criteria. A survival analysis was carried out for patients with high or low risk., Results: The one-, three- and five-year cumulative survival of HCC patients after LT was 78.9%, 53.2% and 46.4%, respectively. Of the HCC patients, the proportion meeting the Hangzhou and Fudan criteria was significantly higher than the proportion meeting the Milan criteria (64.6% vs 39.5%, 52.0% vs 39.5%, P < 0.05). Moreover, the proportion meeting the Hangzhou criteria was also significantly higher than the proportion meeting other criteria (P < 0.01). Pre-operative alfa-fetoprotein level, intraoperative blood loss and retransplantation were common significant predictors of long-term survival in HCC patients with reference to the Milan, University of California San Francisco and Fudan criteria, whereas in MHCAT based on the Hangzhou criteria, total bilirubin, intraoperative blood loss and retransplantation were independent predictors. The c-statistic for MHCAT was 0.773-0.824, with no statistical difference among these four criteria. According to the MHCAT scoring system, patients with low risk showed a higher five-year survival than those with high risk (P < 0.001)., Conclusion: MHCAT can effectively predict long-term survival for HCC patients, but needs to be verified by multi-center retrospective or randomized controlled trials.