Postoperative hemorrhage after Le Fort I osteotomy hemostasis with angiographic embolization: report of two cases.

This study reported two cases of acute life-threatening hemorrhage after Le Fort I osteotomy. In both cases, computed tomography and angiography revealed damage to the descending palatine artery, which was successfully treated by angiographic embolization. Although massive hemorrhage after Le Fort I osteotomy is rare, acute hemorrhage from the postoperative area may occur. Angiographic embolization is useful in cases of such hemorrhage from the posterior nasal cavity where endoscopic hemostasis is not possible.

Synthesis of 6-Halo-Substituted Pericosine A and an Evaluation of Their Antitumor and Antiglycosidase Activities.

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.

Initial evaluation of a novel electrocardiography sensor-embedded fabric wear during a full marathon.

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.

Predicting anesthetic infusion events using machine learning.

Recently, research has been conducted to automatically control anesthesia using machine learning, with the aim of alleviating the shortage of anesthesiologists. In this study, we address the problem of predicting decisions made by anesthesiologists during surgery using machine learning; specifically, we formulate a decision making problem by increasing the flow rate at each time point in the continuous administration of analgesic remifentanil as a supervised binary classification problem. The experiments were conducted to evaluate the prediction performance using six machine learning models: logistic regression, support vector machine, random forest, LightGBM, artificial neural network, and long short-term memory (LSTM), using 210 case data collected during actual surgeries. The results demonstrated that when predicting the future increase in flow rate of remifentanil after 1 min, the model using LSTM was able to predict with scores of 0.659 for sensitivity, 0.732 for specificity, and 0.753 for ROC-AUC; this demonstrates the potential to predict the decisions made by anesthesiologists using machine learning. Furthermore, we examined the importance and contribution of the features of each model using Shapley additive explanations-a method for interpreting predictions made by machine learning models. The trends indicated by the results were partially consistent with known clinical findings.

The Data Use Ontology to streamline responsible access to human biomedical datasets.

Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset's allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers' discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide.

Simple Wearable Device to Reduce Stress When Delivering a Speech without Pre-training.

OBJECTIVE: There are many occasions in modern life when people must deliver presentations in front of audiences. Most people feel nervous before and while giving a speech. If there were a simple way to ease their stress, speakers would be able to perform better and their quality of life would improve. Consequently, this study aimed to alleviate the stress of speakers giving speeches by regulating breathing using a simple device. METHODS: To achieve this goal, a popular device, the Apple Watch, was chosen. Twenty-eight participants were divided into two groups: the Breathe app group and the non-Breathe app group. The Breathe app group regulated their breathing using the Breathe app installed on an Apple Watch before speech preparation. The non-Breathe app group listened to an explanation of the experiment so that they could not undertake their own stress-easing strategies. Participants prepared speeches about themselves and delivered them in front of the researcher. RESULTS: The Breathe app exercise eased stress during the exercise itself and the preparation phase of the speech task based on participants' cardiac activity. However, stress was not alleviated during speech delivery. CONCLUSIONS: Based on the experimental setting and results of this study, together with the findings of previous studies, introducing pre-training sessions and performing stress-easing tasks before and/or during a speech, such as sending vibrations to participants' wearable devices, might be an effective way to reduce stress when delivering speeches immediately after the breath-regulating task.

De-identifying free text of Japanese electronic health records.

BACKGROUND: 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. RESULTS: Using three datasets, we implemented de-identification systems for Japanese EHRs and compared the de-identification performances found for rule-based, Conditional Random Fields (CRF), and Long-Short Term Memory (LSTM)-based methods. Gold standard tags for de-identification are annotated manually for age, hospital, person, sex, and time. We used different combinations of our datasets to train and evaluate our three methods. Our best F1-scores were 84.23, 68.19, and 81.67 points, respectively, for evaluations of the MedNLP dataset, a dummy EHR dataset that was virtually written by a medical doctor, and a Pathology Report dataset. Our LSTM-based method was the best performing, except for the MedNLP dataset. The rule-based method was best for the MedNLP dataset. The LSTM-based method achieved a good score of 83.07 points for this MedNLP dataset, which differs by 1.16 points from the best score obtained using the rule-based method. Results suggest that LSTM adapted well to different characteristics of our datasets. Our LSTM-based method performed better than our CRF-based method, yielding a 7.41 point F1-score, when applied to our Pathology Report dataset. This report is the first of study applying this LSTM-based method to any de-identification task of a Japanese EHR. CONCLUSIONS: Our LSTM-based machine learning method was able to extract named entities to be de-identified with better performance, in general, than that of our rule-based methods. However, machine learning methods are inadequate for processing expressions with low occurrence. Our future work will specifically examine the combination of LSTM and rule-based methods to achieve better performance. Our currently achieved level of performance is sufficiently higher than that of publicly available Japanese de-identification tools. Therefore, our system will be applied to actual de-identification tasks in hospitals.

[Training Medical Staff with Basic Skills for Data Science in Genomic Medicine].

The development of specialized training programs for medical personnel, particularly nurses, clinical laboratory technicians, and pharmacists, is considered critical for the promotion of genomic medicine throughout Japan. Specifically, medical personnel skilled at analyzing and understanding high-throughput genomic data are in high demand. In this symposium, we will introduce the basic knowledge and skills necessary for processing genomic data.

DV200 Index for Assessing RNA Integrity in Next-Generation Sequencing.

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.

Search for Therapeutic Agents for Cardiac Arrest Using a Drug Discovery Tool and Large-Scale Medical Information Database.

The survival rate of cardiac arrest patients is less than 10%; therefore, development of a therapeutic strategy that improves their prognosis is necessary. Herein, we searched data collected from medical facilities throughout Japan for drugs that improve the survival rate of cardiac arrest patients. Candidate drugs, which could improve the prognosis of cardiac arrest patients, were extracted using "TargetMine," a drug discovery tool. We investigated whether the candidate drugs were among the drugs administered within 1 month after cardiac arrest in data of cardiac arrest cases obtained from the Japan Medical Data Center. Logistic regression analysis was performed, with the explanatory variables being the presence or absence of the administration of those candidate drugs that were administered to ≥10 patients and the objective variable being the "survival discharge." Adjusted odds ratios for survival discharge were calculated using propensity scores for drugs that significantly improved the proportion of survival discharge; the influence of covariates, such as patient background, medical history, and treatment factors, was excluded by the inverse probability-of-treatment weighted method. Using the search strategy, we extracted 165 drugs with vasodilator activity as candidate drugs. Drugs not approved in Japan, oral medicines, and external medicines were excluded. Then, we investigated whether the candidate drugs were administered to the 2,227 cardiac arrest patients included in this study. The results of the logistic regression analysis showed that three (isosorbide dinitrate, nitroglycerin, and nicardipine) of seven drugs that were administered to ≥10 patients showed significant association with improvement in the proportion of survival discharge. Further analyses using propensity scores revealed that the adjusted odds ratios for survival discharge for patients administered isosorbide dinitrate, nitroglycerin, and nicardipine were 3.35, 5.44, and 4.58, respectively. Thus, it can be suggested that isosorbide dinitrate, nitroglycerin, and nicardipine could be novel therapeutic agents for improving the prognosis of cardiac arrest patients.

A new approach to prevent critical cardiac accidents in athletes by real-time electrocardiographic tele-monitoring system: Initial trial in full marathon.

The majority of marathon deaths are caused by sudden cardiac arrest (SCA), which occur in approximately 1 in 57,000 runners. Such deaths are more common among older males and usually occur in the last 4 miles of the racecourse. Although prompt resuscitation, including early use of an automated external defibrillator (AED), improves survival, the deployment of enough trained medical staff and AEDs is difficult due to increased cost. Moreover, most victims of exercise-related SCA have no premonitory symptoms. Therefore, we tried to use a novel approach to prevent sudden cardiac deaths (SCD) related to SCA using real-time electrocardiographic tele-monitoring system, as an initial trial to assess operative possibility in a full marathon. As a result, 3 out of 5 runners had reasonable measurement results and sufficient tele-monitoring without complications related to this trial was possible. However, many investigations and improvements, such as improving cost-effectiveness, reducing noise, and automating the monitoring system, are needed for practical application of these devices for athletes. As a next step, we would establish a novel strategy to reduce SCDs in athletes using next-generation devices, which include an alarm system associated with early application of AED. .

Tweet Classification Toward Twitter-Based Disease Surveillance: New Data, Methods, and Evaluations.

BACKGROUND: 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. OBJECTIVE: This study aimed to present the results of groups participating in a Japanese subtask, English subtask, and Chinese subtask along with discussions, to clarify the issues that need to be resolved in the field of medical NLP. METHODS: In summary, 8 groups (19 systems) participated in the Japanese subtask, 4 groups (12 systems) participated in the English subtask, and 2 groups (6 systems) participated in the Chinese subtask. In total, 2 baseline systems were constructed for each subtask. The performance of the participant and baseline systems was assessed using the exact match accuracy, F-measure based on precision and recall, and Hamming loss. RESULTS: The best system achieved exactly 0.880 match accuracy, 0.920 F-measure, and 0.019 Hamming loss. The averages of match accuracy, F-measure, and Hamming loss for the Japanese subtask were 0.720, 0.820, and 0.051; those for the English subtask were 0.770, 0.850, and 0.037; and those for the Chinese subtask were 0.810, 0.880, and 0.032, respectively. CONCLUSIONS: This paper presented and discussed the performance of systems participating in the NTCIR-13 MedWeb task. As the MedWeb task settings can be formalized as the factualization of text, the achievement of this task could be directly applied to practical clinical applications.

Medical Attitudes Survey for Female Dystrophinopathy Carriers in Japan.

Objective This study attempted to clarify the current status of female dystrophinopathy carriers, including the numbers of patients, the status of genetic screening, the status of counseling, physicians' understanding, and barriers to registration. Methods We sent out questionnaires to 402 physicians registered in the Remudy dystrophinopathy registry. The total number of responses received was 130 (response rate: 32%). Result In total, 1,212 cases of Duchenne muscular dystrophy, 365 cases of Becker muscular dystrophy, and 132 cases of female dystrophinopathy with a confirmed genetic mutation were encountered, and genetic testing was performed in the mother in 137, 23, and 12 cases, respectively. With respect to the risk of the onset of health problems, 25% of physicians always explained, 29% usually explained, 29% sometimes explained, and 13% never explained the risk to the mothers and female siblings of dystrophinopathy patients. The most common reason for not explaining the risk was a lack of knowledge/information. Thirty-five percent were familiar with the guidelines for testing the heart function of carriers. Conclusion Fewer mothers of dystrophinopathy patients have undergone genetic testing in Japan than in other countries. A significant portion of doctors did not explain the risks of health problems due to a lack of knowledge. We hope this survey will lead to an increased discussion of female dystrophinopathy patients.

Antitumor effect of afatinib, as a human epidermal growth factor receptor 2-targeted therapy, in lung cancers harboring HER2 oncogene alterations.

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.

Implementation of linked data in the life sciences at BioHackathon 2011.

BACKGROUND: 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. RESULTS: This manuscript describes the experiences and discussions shared among participants of the week-long BioHackathon 2011 who went through the development of RDF representations of their own data and developed specific RDF and SPARQL use cases. Advice regarding considerations to take when developing RDF representations of their data are provided for bioinformaticians considering making data available and interoperable. CONCLUSIONS: Participants of the BioHackathon 2011 were able to produce RDF representations of their data and gain a better understanding of the requirements for producing such data in a period of just five days. We summarize the work accomplished with the hope that it will be useful for researchers involved in developing laboratory databases or data analysis, and those who are considering such technologies as RDF and Linked Data.

Toxygates: interactive toxicity analysis on a hybrid microarray and linked data platform.

MOTIVATION: In early stage drug development, it is desirable to assess the toxicity of compounds as quickly as possible. Biomarker genes can help predict whether a candidate drug will adversely affect a given individual, but they are often difficult to discover. In addition, the mechanism of toxicity of many drugs and common compounds is not yet well understood. The Japanese Toxicogenomics Project provides a large database of systematically collected microarray samples from rats (liver, kidney and primary hepatocytes) and human cells (primary hepatocytes) after exposure to 170 different compounds in different dosages and at different time intervals. However, until now, no intuitive user interface has been publically available, making it time consuming and difficult for individual researchers to explore the data. RESULTS: 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.

Sagace: a web-based search engine for biomedical databases in Japan.

BACKGROUND: In the big data era, biomedical research continues to generate a large amount of data, and the generated information is often stored in a database and made publicly available. Although combining data from multiple databases should accelerate further studies, the current number of life sciences databases is too large to grasp features and contents of each database. FINDINGS: We have developed Sagace, a web-based search engine that enables users to retrieve information from a range of biological databases (such as gene expression profiles and proteomics data) and biological resource banks (such as mouse models of disease and cell lines). With Sagace, users can search more than 300 databases in Japan. Sagace offers features tailored to biomedical research, including manually tuned ranking, a faceted navigation to refine search results, and rich snippets constructed with retrieved metadata for each database entry. CONCLUSIONS: Sagace will be valuable for experts who are involved in biomedical research and drug development in both academia and industry. Sagace is freely available at http://sagace.nibio.go.jp/en/.

Lipid recognition propensities of amino acids in membrane proteins from atomic resolution data.

BACKGROUND: 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. RESULTS: To gain an insight into the molecular mechanisms of the recognition of lipid molecules by membrane proteins, we investigated amino acid propensities in membrane proteins for interacting with the head and tail groups of lipid molecules. We observed a common pattern of lipid tail-amino acid interactions in two different data sources, crystal structures and molecular dynamics simulations. These interactions are largely explained by general lipophilicity, whereas the preferences for lipid head groups vary among individual proteins. We also found that membrane and water-soluble proteins utilize essentially an identical set of amino acids for interacting with lipid head and tail groups. CONCLUSIONS: We showed that the lipophilicity of amino acid residues determines the amino acid preferences for lipid tail groups in both membrane and water-soluble proteins, suggesting that tightly-bound lipid molecules and lipids in the annular shell interact with membrane proteins in a similar manner. In contrast, interactions between lipid head groups and amino acids showed a more variable pattern, apparently constrained by each protein's specific molecular function.

BUDDY-system: A web site for constructing a dataset of protein pairs between ligand-bound and unbound states.

BACKGROUND: Elucidating molecular recognition by proteins, such as in enzyme-substrate and receptor-ligand interactions, is a key to understanding biological phenomena. To delineate these protein interactions, it is important to perform structural bioinformatics studies relevant to molecular recognition. Such studies require a dataset of protein structure pairs between ligand-bound and unbound states. In many studies, the same well-designed and high-quality dataset has been used repeatedly, which has spurred the development of subsequent relevant research. Using previously constructed datasets, researchers are able to fairly compare obtained results with those of other studies; in addition, much effort and time is saved. Therefore, it is important to construct a refined dataset that will appeal to many researchers. However, constructing such datasets is not a trivial task. FINDINGS: We have developed the BUDDY-system, a web site designed to support the building of a dataset comprising pairs of protein structures between ligand-bound and unbound states, which are widely used in various areas associated with molecular recognition. In addition to constructing a dataset, the BUDDY-system also allows the user to search for ligand-bound protein structures by its unbound state or by its ligand; and to search for ligands by a particular receptor protein. CONCLUSIONS: The BUDDY-system receives input from the user as a single entry or a dataset consisting of a list of ligand-bound state protein structures, unbound state protein structures, or ligands and returns to the user a list of protein structure pairs between the ligand-bound and the corresponding unbound states. This web site is designed for researchers who are involved not only in structural bioinformatics but also in experimental studies. The BUDDY-system is freely available on the web.