Albumin Binding Function: The Potential Earliest Indicator for Liver Function Damage

Background. Currently there is no indicator that can evaluate actual liver lesion for early stages of viral hepatitis, nonalcoholic fatty liver disease (NAFLD), and cirrhosis. Aim of this study was to investigate if albumin binding function could better reflect liver function in these liver diseases. Methods. An observational study was performed on 193 patients with early NAFLD, viral hepatitis, and cirrhosis. Cirrhosis patients were separated according to Child-Pugh score into A, B, and C subgroup. Albumin metal ion binding capacity (Ischemia-modified albumin transformed, IMAT) and fatty acid binding capacity (total binding sites, TBS) were detected. Results. Both IMAT and TBS were significantly decreased in patients with NAFLD and early hepatitis. In hepatitis group, they declined prior to changes of liver enzymes. IMAT was significantly higher in cirrhosis Child-Pugh class A group than hepatitis patients and decreased in Child-Pugh class B and class C patients. Both IMAT/albumin and TBS/albumin decreased significantly in hepatitis and NAFLD group patients. Conclusions. This is the first study to discover changes of albumin metal ion and fatty acid binding capacities prior to conventional biomarkers for liver damage in early stage of liver diseases. They may become potential earliest sensitive indicators for liver function evaluation

Minimization of Entropy Generation Rate in Hydrogen Iodide Decomposition Reactor Heated by High-Temperature Helium

The thermochemical sulfur-iodine cycle is a potential method for hydrogen production, and the hydrogen iodide (HI) decomposition is the key step to determine the efficiency of hydrogen production in the cycle. To further reduce the irreversibility of various transmission processes in the HI decomposition reaction, a one-dimensional plug flow model of HI decomposition tubular reactor is established, and performance optimization with entropy generate rate minimization (EGRM) in the decomposition reaction system as an optimization goal based on finite-time thermodynamics is carried out. The reference reactor is heated counter-currently by high-temperature helium gas, the optimal reactor and the modified reactor are designed based on the reference reactor design parameters. With the EGRM as the optimization goal, the optimal control method is used to solve the optimal configuration of the reactor under the condition that both the reactant inlet state and hydrogen production rate are fixed, and the optimal value of total EGR in the reactor is reduced by 13.3% compared with the reference value. The reference reactor is improved on the basis of the total EGR in the optimal reactor, two modified reactors with increased length are designed under the condition of changing the helium inlet state. The total EGR of the two modified reactors are the same as that of the optimal reactor, which are realized by decreasing the helium inlet temperature and helium inlet flow rate, respectively. The results show that the EGR of heat transfer accounts for a large proportion, and the decrease of total EGR is mainly caused by reducing heat transfer irreversibility. The local total EGR of the optimal reactor distribution is more uniform, which approximately confirms the principle of equipartition of entropy production. The EGR distributions of the modified reactors are similar to that of the reference reactor, but the reactor length increases significantly, bringing a relatively large pressure drop. The research results have certain guiding significance to the optimum design of HI decomposition reactors

SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.

One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model