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.

Linked Data has gained some attention recently in the life sciences as an effective way to provide and share data. As a part of the Semantic Web, data are linked so that a person or machine can explore the web of data. Resource Description Framework (RDF) is the standard means of implementing Linked Data. In the process of generating RDF data, not only are data simply linked to one another, the links themselves are characterized by ontologies, thereby allowing the types of links to be distinguished. Although there is a high labor cost to define an ontology for data providers, the merit lies in the higher level of interoperability with data analysis and visualization software. This increase in interoperability facilitates the multi-faceted retrieval of data, and the appropriate data can be quickly extracted and visualized. Such retrieval is usually performed using the SPARQL (SPARQL Protocol and RDF Query Language) query language, which is used to query RDF data stores. For the database provider, such interoperability will surely lead to an increase in the number of users.

The enantiomers of 6-fluoro-, 6-bromo-, and 6-iodopericosine A were synthesized. An efficient synthesis of both enantiomers of pericoxide via 6-bromopericosine A was also developed. These 6-halo-substituted pericosine A derivatives were evaluated in terms of their antitumor activity against three types of tumor cells (p388, L1210, and HL-60) and glycosidase inhibitory activity. The bromo- and iodo-congeners exhibited moderate antitumor activity similar to pericosine A against the three types of tumor cell lines studied. The fluorinated compound was less active than the others, including pericosine A. In the antitumor assay, no significant difference in potency between the enantiomers was observed for any of the halogenated compounds. Meanwhile, the (-)-6-fluoro- and (-)-6-bromo-congeners inhibited α-glucosidase to a greater extent than those of their corresponding (+)-enantiomers, whereas (+)-iodopericosine A showed increased activity when compared to its (-)-enantiomer.

Recently, more electronic data sources are becoming available in the healthcare domain. Electronic health records (EHRs), with their vast amounts of potentially available data, can greatly improve healthcare. Although EHR de-identification is necessary to protect personal information, automatic de-identification of Japanese language EHRs has not been studied sufficiently. This study was conducted to raise de-identification performance for Japanese EHRs through classic machine learning, deep learning, and rule-based methods, depending on the dataset.

Sudden cardiac accident (SCA) during a marathon is a concern due to the popularity of the sport. Preventive strategies, such as cardiac screening and deployment of automated external defibrillators have controversial cost-effectiveness. We investigated the feasibility of use of a new electrocardiography (ECG) sensor-embedded fabric wear (SFW) during a marathon as a novel preventive strategy against SCA. Twenty healthy volunteers participated in a full marathon race. They were equipped with a SFW hitoe® with a transmitter connected via Bluetooth to a standard smartphone for continuous ECG recording. All data were stored in a smartphone and used to analyze the data acquisition rate. The adequate data acquisition rate was > 90% in 13, 30-90% in 3, and < 10% in 4 runners. All of 4 runners with poorly recorded data were female. Inadequate data acquisition was significantly associated with the early phase of the race compared with the mid phase (P = 0.007). Except for 3 runners with poor heart rate data, automated software calculation was significantly associated with manual analysis for both the mean (P < 0.001) and maximum (P = 0.014) heart rate. We tested the feasibility of continuously recording cardiac data during a marathon using a new ECG sensor-embedded wearable device. Although data from 65% of runners were adequately recorded, female runners and the early phase of the race tended to have poor data acquisition. Further improvements in device ergonomics and software are necessary to improve ability to detect abnormal ECGs that may precede SCA.

The amount of medical and clinical-related information on the Web is increasing. Among the different types of information available, social media-based data obtained directly from people are particularly valuable and are attracting significant attention. To encourage medical natural language processing (NLP) research exploiting social media data, the 13th NII Testbeds and Community for Information access Research (NTCIR-13) Medical natural language processing for Web document (MedWeb) provides pseudo-Twitter messages in a cross-language and multi-label corpus, covering 3 languages (Japanese, English, and Chinese) and annotated with 8 symptom labels (such as cold, fever, and flu). Then, participants classify each tweet into 1 of the 2 categories: those containing a patient's symptom and those that do not.

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 (1st 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 1st 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 1st NGS library product per input.

Protein-lipid interactions play essential roles in the conformational stability and biological functions of membrane proteins. However, few of the previous computational studies have taken into account the atomic details of protein-lipid interactions explicitly.