The alteration of drug metabolism enzymes and pharmacokinetic parameters in nonalcoholic fatty liver disease: current animal models and clinical practice.

Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disease. The whole concept of NAFLD has now moved into metabolic dysfunction-associated fatty liver disease (MAFLD) to emphasize the strong metabolic derangement as the basis of the disease. Several studies have suggested that hepatic gene expression was altered in NAFLD and NAFLD-related metabolic comorbidities, particularly mRNA and protein expression of phase I and II drug metabolism enzymes (DMEs). NAFLD may affect the pharmacokinetic parameters. However, there were a limited number of pharmacokinetic studies on NAFLD at present. Determining the pharmacokinetic variation in patients with NAFLD remains challenging. Common modalities for modeling NAFLD included: dietary induction, chemical induction, or genetic models. The altered expression of DMEs has been found in rodent and human samples with NAFLD and NAFLD-related metabolic comorbidities. We summarized the pharmacokinetic changes of clozapine (CYP1A2 substrate), caffeine (CYP1A2 substrate), omeprazole (Cyp2c29/CYP2C19 substrate), chlorzoxazone (CYP2E1 substrate), midazolam (Cyp3a11/CYP3A4 substrate) in NAFLD. These results led us to wonder whether current drug dosage recommendations may need to be reevaluated. More objective and rigorous studies are required to confirm these pharmacokinetic changes. We have also summarized the substrates of the DMEs aforementioned. In conclusion, DMEs play an important role in the metabolism of drugs. We hope that future investigations should focus on the effect and alteration of DMEs and pharmacokinetic parameters in this special patient population with NAFLD.

A block variational procedure for the iterative diagonalization of non-Hermitian random-phase approximation matrices.

We present a technique for the iterative diagonalization of random-phase approximation (RPA) matrices, which are encountered in the framework of time-dependent density-functional theory (TDDFT) and the Bethe-Salpeter equation. The non-Hermitian character of these matrices does not permit a straightforward application of standard iterative techniques used, i.e., for the diagonalization of ground state Hamiltonians. We first introduce a new block variational principle for RPA matrices. We then develop an algorithm for the simultaneous calculation of multiple eigenvalues and eigenvectors, with convergence and stability properties similar to techniques used to iteratively diagonalize Hermitian matrices. The algorithm is validated for simple systems (Na(2) and Na(4)) and then used to compute multiple low-lying TDDFT excitation energies of the benzene molecule.

A Self-Consistent-Field Iteration for Orthogonal Canonical Correlation Analysis.

We propose an efficient algorithm for solving orthogonal canonical correlation analysis (OCCA) in the form of trace-fractional structure and orthogonal linear projections. Even though orthogonality has been widely used and proved to be a useful criterion for visualization, pattern recognition and feature extraction, existing methods for solving OCCA problem are either numerically unstable by relying on a deflation scheme, or less efficient by directly using generic optimization methods. In this paper, we propose an alternating numerical scheme whose core is the sub-maximization problem in the trace-fractional form with an orthogonality constraint. A customized self-consistent-field (SCF) iteration for this sub-maximization problem is devised. It is proved that the SCF iteration is globally convergent to a KKT point and that the alternating numerical scheme always converges. We further formulate a new trace-fractional maximization problem for orthogonal multiset CCA and propose an efficient algorithm with an either Jacobi-style or Gauss-Seidel-style updating scheme based on the SCF iteration. Extensive experiments are conducted to evaluate the proposed algorithms against existing methods, including real-world applications of multi-label classification and multi-view feature extraction. Experimental results show that our methods not only perform competitively to or better than the existing methods but also are more efficient.

Error control of iterative linear solvers for integrated groundwater models.

An open problem that arises when using modern iterative linear solvers, such as the preconditioned conjugate gradient method or Generalized Minimum RESidual (GMRES) method, is how to choose the residual tolerance in the linear solver to be consistent with the tolerance on the solution error. This problem is especially acute for integrated groundwater models, which are implicitly coupled to another model, such as surface water models, and resolve both multiple scales of flow and temporal interaction terms, giving rise to linear systems with variable scaling. This article uses the theory of "forward error bound estimation" to explain the correspondence between the residual error in the preconditioned linear system and the solution error. Using examples of linear systems from models developed by the US Geological Survey and the California State Department of Water Resources, we observe that this error bound guides the choice of a practical measure for controlling the error in linear systems. We implemented a preconditioned GMRES algorithm and benchmarked it against the Successive Over-Relaxation (SOR) method, the most widely known iterative solver for nonsymmetric coefficient matrices. With forward error control, GMRES can easily replace the SOR method in legacy groundwater modeling packages, resulting in the overall simulation speedups as large as 7.74×. This research is expected to broadly impact groundwater modelers through the demonstration of a practical and general approach for setting the residual tolerance in line with the solution error tolerance and presentation of GMRES performance benchmarking results.