With the aim of controlling nanoscale magnetism, we demonstrate an approach encompassing concepts of surface and exchange anisotropy while reflecting size, shape, and structural hybridization of nanoparticles. We visualize that cube has higher magnetization value than sphere with highest coercivity at 60 nm. Its hybridization into core-shell (CS) structure brings about a 14-fold increase in the coercivity with an exceptional energy conversion of magnetic field into thermal energy of 10600 W/g, the largest reported to date. Such capability of the CS-cube is highly effective for drug resistant cancer cell treatment.

Background There are many perspectives on the advantages of introducing blockchain in the medical field, but there are no published feasibility studies regarding the storage, propagation, and management of personal health records (PHRs) using blockchain technology. Objective The purpose of this study was to investigate the usefulness of blockchains in the medical field in relation to transactions with and propagation of PHRs in a private blockchain. Methods We constructed a private blockchain network using Ethereum version 1.8.4 and conducted verification using the de-identified PHRs of 300 patients. The private blockchain network consisted of one hospital node and 300 patient nodes. In order to verify the effectiveness of blockchain-based PHR management, PHRs at a time were loaded in a transaction between the hospital and patient nodes and propagated to the whole network. We obtained and analyzed the time and gas required for data transaction and propagation on the blockchain network. For reproducibility, these processes were repeated 100 times. Results Of 300 patient records, 74 (24.7%) were not loaded in the private blockchain due to the data block size of the transaction block. The remaining 226 individual health records were classified into groups A (80 patients with outpatient visit data less than 1 year old), B (84 patients with outpatient data from between 1 and 3 years before data collection), and C (62 patients with outpatient data 3 to 5 years old). With respect to mean transaction time in the blockchain, C (128.7 seconds) had the shortest time, followed by A (132.2 seconds) and then B (159.0 seconds). The mean propagation times for groups A, B, and C were 1494.2 seconds, 2138.9 seconds, and 4111.4 seconds, respectively; mean file sizes were 5.6 KB, 18.6 KB, and 45.38 KB, respectively. The mean gas consumption values were 1,900,767; 4,224,341; and 4,112,784 for groups A, B, and C, respectively. Conclusions This study confirms that it is possible to exchange PHR data in a private blockchain network. However, to develop a blockchain-based PHR platform that can be used in practice, many improvements are required, including reductions in data size, improved personal information protection, and reduced operating costs.

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For the first time, an inorganic-organic hybrid polymer binder was used for the coating of hybrid composites on separators to enhance thermal stability and to prevent formation of lithium dendrite in lithium metal batteries. The fabricated hybrid-composite-coated separators exhibited minimal thermal shrinkage compared with the previous composite separators (<5% change in dimension), maintenance of porosity (Gurley number ∼400 s/100 cm(3)), and high ionic conductivity (0.82 mS/cm). Lithium metal battery cell examinations with our hybrid-composite-coated separators revealed excellent C-rate and cyclability performance due to the prevention of lithium dendrite growth on the lithium anode even after 200 cycles under 0.2-5C (charge-discharge) conditions. The mechanism for lithium dendrite prevention was attributed to exceptional nanoscale surface mechanical properties of the hybrid composite coating layer compared with the lithium metal anode, as the elastic modulus of the hybrid-composite-coated separator far exceeded those of both the lithium metal anode and the required threshold for lithium metal dendrite prevention.

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Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.

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