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In this short article, we determine the bigness of the tangent bundle $T_X$ of the projective bundle $X=\mathbb{P}_C(E)$ associated to a vector bundle $E$ on a smooth projective curve $C$.

Methaqualone, a quinazolinone marketed commercially as Quaalude, is a central nervous system depressant that was used clinically as a sedative-hypnotic, then became a notorious recreational drug in the 1960s-80s. Due to its high abuse potential, medical use of methaqualone was eventually prohibited, yet it persists as a globally abused substance. Methaqualone principally targets GABAA receptors, which are the major inhibitory neurotransmitter-gated ion channels in the brain. The restricted status and limited accessibility of methaqualone have contributed to its pharmacology being understudied. Here, we use cryo-EM to localize the GABAA receptor binding sites of methaqualone and its more potent derivative, PPTQ, to the same intersubunit transmembrane sites targeted by the general anesthetics propofol and etomidate. Both methaqualone and PPTQ insert more deeply into subunit interfaces than the previously-characterized modulators. Binding of quinazolinones to this site results in widening of the extracellular half of the ion-conducting pore, following a trend among positive allosteric modulators in destabilizing the hydrophobic activation gate in the pore as a mechanism for receptor potentiation. These insights shed light on the underexplored pharmacology of quinazolinones and further elucidate the molecular mechanisms of allosteric GABAA receptor modulation through transmembrane binding sites.

BackgroundPrioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. ObjectiveThe purpose of this study was to develop a machine learning model that predicts the need for intensive care for patients with COVID-19 using easily obtainable characteristics—baseline demographics, comorbidities, and symptoms. MethodsA retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25, 2020, to June 3, 2020, were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20, 2020, were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21, 2020. The machine learning model with the best discrimination performance was selected and compared against the CURB-65 (confusion, urea, respiratory rate, blood pressure, and 65 years of age or older) score using the area under the receiver operating characteristic curve (AUC). ResultsA total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively. ConclusionsWe developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.

BackgroundTo implement standardized machine-processable clinical sequencing reports in an electronic health record (EHR) system, the International Organization for Standardization Technical Specification (ISO/TS) 20428 international standard was proposed for a structured template. However, there are no standard implementation guidelines for data items from the proposed standard at the clinical site and no guidelines or references for implementing gene sequencing data results for clinical use. This is a significant challenge for implementation and application of these standards at individual sites. ObjectiveThis study examines the field utilization of genetic test reports by designing the Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) for genomic data elements based on the ISO/TS 20428 standard published as the standard for genomic test reports. The goal of this pilot is to facilitate the reporting and viewing of genomic data for clinical applications. FHIR Genomics resources predominantly focus on transmitting or representing sequencing data, which is of less clinical value. MethodsIn this study, we describe the practical implementation of ISO/TS 20428 using HL7 FHIR Genomics implementation guidance to efficiently deliver the required genomic sequencing results to clinicians through an EHR system. ResultsWe successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. ConclusionsTo share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard-based information system in a practical setting. This prototyping work shows that reporting data from clinical genomics sequencing can be effectively implemented into an EHR system using the existing ISO/TS 20428 standard and FHIR resources.

CMOS-RRAM integration holds great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise, leading to computational accuracy loss, increased energy consumption, and overhead by expensive program and verify schemes. We developed a filament-free, bulk switching RRAM technology to address these challenges. We systematically engineered a trilayer metal-oxide stack and investigated the switching characteristics of RRAM with varying thicknesses and oxygen vacancy distributions to achieve reliable bulk switching without any filament formation. We demonstrated bulk switching at megaohm regime with high current nonlinearity, up to 100 levels without compliance current. We developed a neuromorphic compute-in-memory platform and showcased edge computing by implementing a spiking neural network for an autonomous navigation/racing task. Our work addresses challenges posed by existing RRAM technologies and paves the way for neuromorphic computing at the edge under strict size, weight, and power constraints.

BackgroundPatient-generated health data (PGHD), especially lifelog data, are important for managing chronic diseases. Additionally, personal health records (PHRs) have been considered an effective tool to engage patients more actively in the management of their chronic diseases. However, no PHRs currently integrate PGHD directly from Samsung S-Health and Apple Health apps. ObjectiveThe purposes of this study were (1) to demonstrate the development of an electronic medical record (EMR)–tethered PHR system (Health4U) that integrates lifelog data from Samsung S-Health and Apple Health apps and (2) to explore the factors associated with the use rate of the functions. MethodsTo upgrade conventional EMR-tethered PHRs, a task-force team (TFT) defined the functions necessary for users. After implementing a new system, we enrolled adults aged 19 years and older with prior experience of accessing Health4U in the 7-month period after November 2017, when the service was upgraded. ResultsOf the 17,624 users, 215 (1.22%) integrated daily steps data, 175 (0.99%) integrated weight data, 51 (0.29%) integrated blood sugar data, and 90 (0.51%) integrated blood pressure data. Overall, 61.95% (10,919/17,624) had one or more chronic diseases. For integration of daily steps data, 48.3% (104/215) of patients used the Apple Health app, 43.3% (93/215) used the S-Health app, and 8.4% (18/215) entered data manually. To retrieve medical documentation, 324 (1.84%) users downloaded PDF files and 31 (0.18%) users integrated their medical records into the Samsung S-Health app via the Consolidated-Clinical Document Architecture download function. We found a consistent increase in the odds ratios for PDF downloads among patients with a higher number of chronic diseases. The age groups of ≥60 years and ≥80 years tended to use the download function less frequently than the others. ConclusionsThis is the first study to examine the factors related to integration of lifelog data from Samsung S-Health and Apple Health apps into EMR-tethered PHRs and factors related to the retrieval of medical documents from PHRs. Our findings on the lifelog data integration can be used to design PHRs as a platform to integrate lifelog data in the future.

BackgroundAlthough using the technologies for a variety of chronic health conditions such as personal health record (PHR) is reported to be acceptable and useful, there is a lack of evidence on the associations between the use of the technologies and the change of health outcome and patients’ response to a digital health app. ObjectiveThis study aimed to examine the impact of the use of PHR and wearables on health outcome improvement and sustained use of the health app that can be associated with patient engagement. MethodsWe developed an Android-based mobile phone app and used a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across 4 weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to 3 subgroups including 2 intervention groups: (1) mobile app and wearable device users (n=20), (2) mobile app–only users (n=20), and (3) controls (n=20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA–related respiratory parameters such as respiratory disturbance, apnea-hypopnea, and oxygenation desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant. ResultsWe collected the individual data for each group during the trial, specifically anthropometric measurement and laboratory test results for health outcomes, and the app usage logs for patient response were collected and analyzed. The body weight showed a significant reduction in the 2 intervention groups after intervention, and the mobile app–only group showed more weight loss compared with the controls (P=.01). There were no significant changes in sleep-related health outcomes. From a patient response point of view, the average daily step counts (8165 steps) from the app plus wearable group were significantly higher than those (6034 steps) from the app-only group because they collected step count data from different devices (P=.02). The average rate of data collection was not different in physical activity (P=.99), food intake (P=.98), sleep (P=.95), stress (P=.70), and weight (P=.90) in the app plus wearable and app-only groups, respectively. ConclusionsWe tried to integrate PHR data that allow clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that a PHR–based intervention may be successful in losing body weight and improvement in lifestyle behavior. Trial RegistrationClinicalTrials.gov NCT03200223; https://clinicaltrials.gov/ct2/show/NCT03200223 (Archived by WebCite at http://www.webcitation.org/74baZmnCX).

Ethylene-propylene-diene monomer (EPDM) rubber has widespread uses in various applications due to its ozone and oxidation stability, weatherability, electrical insulation, and elasticity. However, its further use is limited by low flame retardancy and low thermal stability. This study incorporated metal hydroxide flame retardant to enhance flame retardancy and electron beam cross-linking to simultaneously enhance mechanical properties, thermal stability, and flame retardancy. The utilization of electron beam irradiation resulted in an increase in gel content and a decrease in swelling ratio with increasing absorbed doses, indicating the successful formation of cross-linked network structures. The hot-set elongation was significantly reduced to 0.6%-0.8%, while the decomposition temperature increased with increasing absorbed doses, indicating improved thermal stability. The results from limiting oxygen index measurements and burning tests demonstrate that the flame retardancy of EPDM was improved by the combined effect of metal hydroxide flame retardant and electron beam cross-linking. Highlights * EPDM/metal hydroxide composites were prepared via electron beam irradiation. * Metal hydroxide flame retardants were incorporated to enhance flame retardancy. * EBI simultaneously enhanced mechanical property and thermal stability. KEYWORDS cross-linking, electron beam irradiation, ethylene-propylene-diene monomer rubber, gel content, limiting oxygen index, mechanical property, thermal stability, 1 | INTRODUCTION Ethylene-propylene-diene-monomer (EPDM) rubber is a versatile synthetic rubber composed of ethylene, propylene, and a small amount of unsaturated dienes, such as ethylidene norbornene (ENB), dicyclopentadiene (DCPD), and [...]

Hydrophilicity/hydrophobicity-or wettability-is a key surface characterization metric for titanium used in dental and orthopedic implants. However, the effects of hydrophilicity/hydrophobicity on biological capability remain uncertain, and the relationships between surface wettability and other surface parameters, such as topography and chemistry, are poorly understood. The objective of this study was to identify determinants of surface wettability of titanium and establish the reliability and validity of the assessment. Wettability was evaluated as the contact angle of ddH2O. The age of titanium specimens significantly affected the contact angle, with acid-etched, microrough titanium surfaces becoming superhydrophilic immediately after surface processing, hydrophobic after 7 days, and hydrorepellent after 90 days. Similar age-related loss of hydrophilicity was also confirmed on sandblasted supra-micron rough surfaces so, regardless of surface topography, titanium surfaces eventually become hydrophobic or hydrorepellent with time. On age-standardized titanium, surface roughness increased the contact angle and hydrophobicity. UV treatment of titanium regenerated the superhydrophilicity regardless of age or surface roughness, with rougher surfaces becoming more superhydrophilic than machined surfaces after UV treatment. Conditioning titanium surfaces by autoclaving increased the hydrophobicity of already-hydrophobic surfaces, whereas conditioning with 70% alcohol and hydrating with water or saline attenuated pre-existing hydrophobicity. Conversely, when titanium surfaces were superhydrophilic like UV-treated ones, autoclaving and alcohol cleaning turned the surfaces hydrorepellent and hydrophobic, respectively. UV treatment recovered hydrophilicity without exception. In conclusion, surface roughness accentuates existing wettability and can either increase or decrease the contact angle. Titanium must be age-standardized when evaluating surface wettability. Surface conditioning techniques significantly but unpredictably affect existing wettability. These implied that titanium wettability is significantly influenced by the hydrocarbon pellicle and other contaminants inevitably accumulated. UV treatment may be an effective strategy to standardize wettability by making all titanium surfaces superhydrophilic, thereby allowing the characterization of individual surface topography and chemistry parameters in future studies.

BackgroundRecently, many hospitals have introduced mobile electronic medical records (mEMRs). Although numerous studies have been published on the usability or usage patterns of mEMRs through user surveys, investigations based on the real data usage are lacking. ObjectiveAsan Medical Center, a tertiary hospital in Seoul, Korea, implemented an mEMR program in 2010. On the basis of the mEMR usage log data collected over a period of 4.5 years, we aimed to identify a usage pattern and trends in accordance with user occupation and to disseminate the factors that make the mEMR more effective and efficient. MethodsThe mEMR log data were collected from March 2012 to August 2016. Descriptive analyses were completed according to user occupation, access time, services, and wireless network type. Specifically, analyses targeted were as follows: (1) the status of the mEMR usage and distribution of users, (2) trends in the number of users and usage amount, (3) 24-hour usage patterns, and (4) trends in service usage based on user occupations. Linear regressions were performed to model the relationship between the time, access frequency, and the number of users. The differences between the user occupations were examined using Student t tests for categorical variables. ResultsApproximately two-thirds of the doctors and nurses used the mEMR. The number of logs studied was 7,144,459. Among 3859 users, 2333 (60.46%) users were nurses and 1102 (28.56%) users were doctors. On average, the mEMR was used 1044 times by 438 users per day. The number of users and amount of access logs have significantly increased since 2012 (P