A Fully-Human Antibody Specifically Targeting a Membrane-Bound Fragment of CADM1 Potentiates the T Cell-Mediated Death of Human Small-Cell Lung Cancer Cells

Small-cell lung cancer (SCLC) is the most aggressive form of lung cancer and the leading cause of global cancer-related mortality. Despite the earlier identification of membrane-proximal cleavage of cell adhesion molecule 1 (CADM1) in cancers, the role of the membrane-bound fragment of CAMD1 (MF-CADM1) is yet to be clearly identified. In this study, we first isolated MF-CADM1-specific fully human single-chain variable fragments (scFvs) from the human synthetic scFv antibody library using the phage display technology. Following the selected scFv conversion to human immunoglobulin G1 (IgG1) scFv-Fc antibodies (K103.1–4), multiple characterization studies, including antibody cross-species reactivity, purity, production yield, and binding affinity, were verified. Finally, via intensive in vitro efficacy and toxicity evaluation studies, we identified K103.3 as a lead antibody that potently promotes the death of human SCLC cell lines, including NCI-H69, NCI-H146, and NCI-H187, by activated Jurkat T cells without severe endothelial toxicity. Taken together, these findings suggest that antibody-based targeting of MF-CADM1 may be an effective strategy to potentiate T cell-mediated SCLC death, and MF-CADM1 may be a novel potential therapeutic target in SCLC for antibody therapy.     

Development of Deep-Learning-Based Single-Molecule Localization Image Analysis

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging.     

HDAC8-Selective Inhibition by PCI-34051 Enhances the Anticancer Effects of ACY-241 in Ovarian Cancer Cells

HDAC6 is overexpressed in ovarian cancer and is known to be correlated with tumorigenesis. Accordingly, ACY-241, a selective HDAC6 inhibitor, is currently under clinical trial and has been tested in combination with various drugs. HDAC8, another member of the HDAC family, has recently gained attention as a novel target for cancer therapy. Here, we evaluated the synergistic anticancer effects of PCI-34051 and ACY-241 in ovarian cancer. Among various ovarian cancer cells, PCI-34051 effectively suppresses cell proliferation in wild-type p53 ovarian cancer cells compared with mutant p53 ovarian cancer cells. In ovarian cancer cells harboring wild-type p53, PCI-34051 in combination with ACY-241 synergistically represses cell proliferation, enhances apoptosis, and suppresses cell migration. The expression of pro-apoptotic proteins is synergistically upregulated, whereas the expressions of anti-apoptotic proteins and metastasis-associated proteins are significantly downregulated in combination treatment. Furthermore, the level of acetyl-p53 at K381 is synergistically upregulated upon combination treatment. Overall, co-inhibition of HDAC6 and HDAC8 through selective inhibitors synergistically suppresses cancer cell proliferation and metastasis in p53 wild-type ovarian cancer cells. These results suggest a novel approach to treating ovarian cancer patients and the therapeutic potential in developing HDAC6/8 dual inhibitors.     

Endometrial Epithelial ARID1A Is Required for Uterine Immune Homeostasis during Early Pregnancy

A growing body of work suggests epigenetic dysregulation contributes to endometriosis pathophysiology and female infertility. The chromatin remodeling complex subunit AT-rich interaction domain 1A (ARID1A) must be properly expressed to maintain normal uterine function. Endometrial epithelial ARID1A is indispensable for pregnancy establishment in mice through regulation of endometrial gland function; however, ARID1A expression is decreased in infertile women with endometriosis. We hypothesized that ARID1A performs critical operations in the endometrial epithelium necessary for fertility besides maintaining gland function. To identify alterations in uterine gene expression resulting from loss of epithelial ARID1A, we performed RNA-sequencing analysis on pre-implantation uteri from Ltf^iCre/+Arid1a^f/f and control mice. Differential expression analysis identified 4181 differentially expressed genes enriched for immune-related ingenuity canonical pathways including agranulocyte adhesion and diapedesis and natural killer cell signaling. RT-qPCR confirmed an increase in pro-inflammatory cytokine and macrophage-related gene expression but a decrease in natural killer cell signaling. Immunostaining confirmed a uterus-specific increase in macrophage infiltration. Flow cytometry delineated an increase in inflammatory macrophages and a decrease in uterine dendritic cells in Ltf^iCre/+Arid1a^f/f uteri. These findings demonstrate a role for endometrial epithelial ARID1A in suppressing inflammation and maintaining uterine immune homeostasis, which are required for successful pregnancy and gynecological health.     

Bias-error reduction in nanometer resolution microscopic motion tracking vision systems

Microscopic Motion Tracking Systems utilize sub-pixel algorithms to achieve close to nanometer precision motion measurement. This paper characterizes the bias-error of template matching image registration techniques which are commonly used to achieve this level of sub-pixel accuracy. An accurate functional expression for such bias error is derived, which shows how the error depends on image content. Furthermore, this expression is directly utilized in a bias reduction scheme that is shown to significantly reduce the bias error. The newly derived expressions and reduction schemes for bias are applied to a Microscopic Vision System that measures micro-object motion with close to nanometer resolution.     

A study on the optimization of KCI-based index (Kor-Factor) in evaluating Korean journals

This study describes the development process of Kor-Factor, which is a novel composite evaluation index that was developed to promote Korean domestic academic journals. As more data accumulate, the Kor-Factor’s optimization process was modified in an attempt to address possible drawbacks of the original form; the result is presented in this study. This study compares Kor-Factor with the Impact Factor, which is the most well-known single element evaluation index. We found that Kor-Factor demonstrates a better power of differentiation and a greater capacity to reflect the reputability of key journals. The modified Kor-Factor, which has been developed through an optimization process, reveals a greater power of differentiation than the original Kor-Factor; however, the modified version has less capacity to reflect reputability. The evaluation elements of the modified Kor-Factor are better and are more evenly reflected on the index value than those of the original version. Finally, we propose the establishment of an appropriate data measurement period for the actual application of the index.     

Codes based on BCK-algebras

The notion of a BCK-valued function on a set is introduced, and related properties are investigated. Codes generated by BCK-valued functions are established.     

Fabrication of Al matrix composite reinforced with submicrometer-sized Al<Subscript>2</Subscript>O<Subscript>3</Subscript> particles formed by combustion reaction between HEMM Al and V<Subscript>2</Subscript>O<Subscript>5</Subscript> composite particles during sintering

To fabricate an Al-V matrix composite reinforced with submicron-sized Al₂O₃ and Al_xV_y (Al₃V, Al₁₀V) phases, high energy mechanical milling (HEMM) and sintering were employed. By increasing the milling time, the size of mechanically milled powder was significantly reduced. In this study, the average powder size of 59 μm for Al, and 178 μm for V₂O₅ decreased with the formation of a new product, Al-Al₂O₃-Al_xV_y, with a size range from 1.3 μm to 2.6 μm formed by the in-situ combustion reaction during sintering of HEM milled Al and V₂O₅ composite powders. The in-situ reaction between Al and V₂O₅ during the HEMM and sintering transformed the Al₂O₃ and Al_xV_y (Al₃V, Al₁₀V) phases. Most of the reduced V reacted with excess the Al to form Al_xV_y (Al₃V, Al₁₀V) with very little V dissolved into Al matrix. By increasing the milling time and weight percentage of V₂O₅, the hardness of the Al-Al₂O₃-Al_xV_y composite sintered at 1173 K increased. The composite fabricated with the HEMM Al-20wt.%V₂O₅ composite powder and sintering at 1173 K for 2 h had the highest hardness.     

The pH effect for rare earth metals Nd, Pr and Y in aqueous solution dissolved Mg, Zn and Al

The pH variations of Mg, Zn and Al solutions to which had been added the rare earth metals Nd, Pr and Y were observed in 3.5 wt.% NaCl with respect to high energy mechanical ball milling effects. Mg was directly dissolved and exhibited a pH value of 10.5. On the other hand, Zn and Al needed to be saturated for a certain amount of time. The addition of rare earth metals played a role in increasing the pH with low reduction potentials. Additionally, mechanical ball milling provided high energy to Mg + x wt.% Zn + 0.5 wt.% Nd mixture by fracturing fragmentation of metals, which led to an increase in the pH when the mixture was immersed in 3.5 wt.% NaCl. The addition of Zn to Mg + 0.5 wt.% Nd caused a higher pH than when Mg + 0.5 wt.% Nd alone was added.