Hong-Joong Kim

OBJECTIVE: To investigate the anti-cancer efficacy of ENB101-LNP, an ionizable lipid nanoparticles (LNPs) encapsulating siRNA against E6/E7 of HPV 16, in combination therapy with cisplatin in cervical cancer in vitro and in vivo. METHODS: CaSki cells were treated with ENB101-LNP, cisplatin, or combination. Cell viability assessed the cytotoxicity of the treatment. HPV16 E6/E7 gene knockdown was verified with RT-PCR both in vitro and in vivo. HLA class I and PD-L1 were checked by flow cytometry.

This study deals with one of the most important issues for understanding financial markets, future asset fluctuations. Predicting the direction of asset fluctuations accurately is very difficult due to the uncertainty of the stock market, the influence of various economic indicators, and the sentiment of investors, etc. In this study, we present a new method to improve the effectiveness of machine learning by selecting appropriate training data using an adaptive method. The application to variou

Product obsolescence occurs in every production line in the industry as better-performance or cost-effective products become available. A proactive strategy for obsolescence allows firms to prepare for such events and reduces the manufacturing loss, which eventually leads to positive customer satisfaction. We propose a machine learning-based algorithm to forecast the obsolescence date of electronic diodes, which has a limitation on the amount of data available. The proposed algorithm overcomes t

Fulvestrant-3-boronic acid (ZB716), an oral selective estrogen receptor degrader (SERD) under clinical development, has been investigated in ADME studies to characterize its absorption, metabolism, and pharmacokinetics. ZB716 was found to have high plasma protein binding in human and animal plasma, and low intestinal mucosal permeability. ZB716 had high clearance in hepatocytes of all species tested. ZB716 was metabolized primarily by CYP2D6 and CYP3A. In human liver microsomes, ZB716 demonstrat

We develop a multi-dimensional local average lattice method in order to compute efficiently and accurately the price of multivariate contingent claims. The proposed method improves the accuracy of the standard lattice method by considering the local averages of option prices around each node at the final time, rather than the prices at the nodes. The average value smooths the oscillatory behavior of the lattice method, which leads to fast convergence of the option values. Numerical computations

Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single m

The paper is concerned with efficient computation of numerical solutions to Burger′s equation with random initial conditions. When the Lax‐Wendroff scheme (LW) is expanded using the Wiener chaos expansion (WCE), random and deterministic effects can be separated and we obtain a system of deterministic equations with respect to Hermite‐Fourier coefficients. One important property of the system is that all the statistical moments of the solution to the Burger′s equation can be computed using the so