By using a 27-MHz piezoelectric quartz oscillator connected with a vector network analyzer, we obtained resonance frequency decreases (-DeltaFwater) and energy dissipation increases (DeltaDwater) during binding of biotinylated bovine serum albumin, biotinylated ssDNA, biotinylated dsDNA, and biotinylated pullulan to a NeutrAvidin-immobilized 27-MHz quartz crystal microbalance (QCM) plate in aqueous solution, as well as in the wet air phase (98% humidity, -DeltaFwet and DeltaDwet) and in the dry air phase (-DeltaFair and DeltaDair). -DeltaFwater indicates the total mass of the molecule, bound water, and vibrated water in aqueous solutions. -DeltaFwet indicates the total mass of the molecule and bound water. -DeltaFair simply shows the real mass of the molecule on the QCM. In terms of results, (-DeltaFwet)/(-DeltaFair) values indicated the bound water ratios per unit biomolecular mass were on the order of pullulan (2.1-2.2) > DNAs = proteins (1.4-1.6) > polystyrene (1.0). The (-DeltaFwater)/(-DeltaFair) values indicated the hydrodynamic water (bound and vibrated water) ratios per unit biomolecular mass were on the order of dsDNA (6.5) > ssDNA = pullulan (3.5-4.4) > proteins (2.4-2.5) > polystyrene (1.0). Energy dissipation parameters per unit mass in water (DeltaDwater/(-DeltaFair)) were on the order of pullulan > dsDNA > ssDNA > proteins > polystyrene. Energy dissipation in the wet and dry air phases (DeltaDwet and DeltaDair) were negligibly small, which indicates even these biomolecules act as elastic membranes in the air phase (without aqueous solution). We obtained a good linear relationship between [(-DeltaFwater)/(-DeltaFair) - 1], which is indicative of hydration and DeltaDwater/(-DeltaFair) of proteins. The aforementioned values suggest that the energy dissipation of proteins was mainly caused by hydration and that proteins themselves are elastic molecules without energy dissipation in aqueous solutions. On the contrary, plots in cases of denatured proteins, DNAs, and pullulans were relatively deviant toward the large hydration and energy dissipation from the theoretical line as perfect elastic materials, meaning that the large energy dissipation occurs because of viscoelastic properties of denatured proteins, linear DNAs, and pullulans in the water phase, in addition to energy dissipation due to the hydration of molecules. These two parameters could characterize various biomolecules with structural properties in aqueous solutions.

Human epidermal growth factor receptor 2 (HER2) is a member of the HER family of proteins containing four receptor tyrosine kinases. It plays an important role in the pathogenesis of certain human cancers. In non‐small‐cell lung cancer (NSCLC), HER2 amplification or mutations have been reported. However, little is known about the benefit of HER2‐targeted therapy for NSCLCs harboring HER2 alterations. In this study, we investigated the antitumor effect of afatinib, an irreversible epidermal growth factor receptor (EGFR)–HER2 dual inhibitor, in lung cancers harboring HER2 oncogene alterations, including novel HER2 mutations in the transmembrane domain, which we recently identified. Normal bronchial epithelial cells, BEAS‐2B, ectopically overexpressing wild‐type HER2 or mutants (A775insYVMA, G776VC, G776LC, P780insGSP, V659E, and G660D) showed constitutive autophosphorylation of HER2 and activation of downstream signaling. They were sensitive to afatinib, but insensitive to gefitinib. Furthermore, we examined the antitumor activity of afatinib and gefitinib in several NSCLC cell lines, and investigated the association between their genetic alterations and sensitivity to afatinib treatment. In HER2‐altered NSCLC cells (H2170, Calu‐3, and H1781), afatinib downregulated the phosphorylation of HER2 and EGFR as well as their downstream signaling, and induced an antiproliferative effect through G1 arrest and apoptotic cell death. In contrast, HER2‐ or EGFR‐non‐dependent NSCLC cells were insensitive to afatinib. In addition, these effects were confirmed in vivo by using a xenograft mouse model of HER2‐altered lung cancer cells. Our results suggest that afatinib is a therapeutic option as a HER2‐targeted therapy for NSCLC harboring HER2 amplification or mutations.

We present Toxygates, a user-friendly integrated analysis platform for this database. Toxygates combines a large microarray dataset with the ability to fetch semantic linked data, such as pathways, compound-protein interactions and orthologs, on demand. It can also perform pattern-based compound ranking with respect to the expression values of a set of relevant candidate genes. By using Toxygates, users can freely interrogate the transcriptome's response to particular compounds and conditions, which enables deep exploration of toxicity mechanisms.

Poor quality of biological samples will result in an inaccurate analysis of next-generation sequencing (NGS). Therefore, methods to accurately evaluate sample integrity are needed. Among methods for evaluating RNA quality, the RNA integrity number equivalent (RINe) is widely used, whereas the DV200, which evaluates the percentage of fragments of >200 nucleotides, is also used as a quality assessment standard. In this study, we compared the RINe and DV200 RNA quality indexes to determine the most suitable RNA index for the NGS analysis. Seventy-one RNA samples were extracted from formalin-fixed paraffin-embedded tissue samples (n = 30), fresh-frozen samples (n = 25), or cell lines (n = 16). After assessing RNA quality using the RINe and DV200, we prepared two kinds of stranded mRNA sequencing libraries. Finally, we calculated the correlation between each RNA quality index and the amount of library product (1 st PCR product per input RNA). The DV200 measure showed stronger correlation with the amount of library product than the RINe (R 2 = 0:8208 for the DV200 versus 0.6927 for the RINe). Receiver operating characteristic curve analyses revealed that the DV200 was the better marker for predicting efficient library production than the RINe using a threshold of >10 ng/ng for the amount of the 1 st PCR product per input RNA (cutoff value for the RINe and DV200, 2.3 and 66.1%; area under the curve, 0.99 and 0.91; sensitivity, 82% and 92%; and specificity, 93% and 100%, respectively). Our results indicate that NGS libraries prepared using RNA samples with the DV200 value > 66:1% exhibit greater sensitivity and specificity than those prepared with the RINe values > 2:3. These findings suggest that the DV200 is superior to the RINe, especially for low-quality RNA, because it is a more consistent assessment of the amount of the 1 st NGS library product per input.