Background: More than 79.2 million confirmed COVID-19 cases and 1.7 million deaths were caused by SARS-CoV-2; the disease was named COVID-19 by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of the COVID-19 pandemic together with each country’s policy measures. Objective: We aimed to develop an online artificial intelligence (AI) system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heat map visualization of policy measures in 171 countries. Methods: The COVID-19 Pandemic AI System (CPAIS) integrated two data sets: the data set from the Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government, which is maintained by the University of Oxford, and the data set from the COVID-19 Data Repository, which was established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting: autoregressive integrated moving average (ARIMA), feedforward neural network (FNN), multilayer perceptron (MLP) neural network, and long short-term memory (LSTM). With regard to 1-year records (ie, whole time series data), records from the last 14 days served as the validation set to evaluate the performance of the forecast, whereas earlier records served as the training set. Results: A total of 171 countries that featured in both databases were included in the online system. The CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July 2020 and another peak of 6,368,591 in December 2020. A dynamic heat map with policy measures depicts changes in COVID-19 measures for each country. A total of 19 measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting; the performances of ARIMA, FNN, and the MLP neural network were not stable because their forecast accuracy was only better than LSTM for a few countries. LSTM demonstrated the best forecast accuracy for Canada, as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA (RMSE=317.53169; MAPE=0.4641688) and FNN (RMSE=181.29894; MAPE=0.2708482) demonstrated better performance for South Korea. Conclusions: The CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning–based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

Background: Medical referral is the transfer of a patient’s care from one physician to another upon request. This process involves multiple steps that require provider-to-provider and provider-to-patient communication. In Taiwan, the National Health Insurance Administration (NHIA) has implemented a national medical referral (NMR) system, which encourages physicians to refer their patients to different health care facilities to reduce unnecessary hospital visits and the financial stress on the national health insurance. However, the NHIA’s NMR system is a government-based electronic medical referral service, and its referral data access and exchange are limited to authorized clinical professionals using their national health smart cards over the NHIA virtual private network. Therefore, this system lacks scalability and flexibility and cannot establish trusting relationships among patients, family doctors, and specialists. Objective: To eliminate the existing restrictions of the NHIA’s NMR system, this study developed a scalable, flexible, and blockchain-enabled framework that leverages the NHIA’s NMR referral data to build an alliance-based medical referral service connecting health care facilities. Methods: We developed a blockchain-enabled framework that can integrate patient referral data from the NHIA’s NMR system with electronic medical record (EMR) and electronic health record (EHR) data of hospitals and community-based clinics to establish an alliance-based medical referral service serving patients, clinics, and hospitals and improve the trust in relationships and transaction security. We also developed a blockchain-enabled personal health record decentralized app (DApp) based on our blockchain-enabled framework for patients to acquire their EMR and EHR data; DApp access logs were collected to assess patients’ behavior and investigate the acceptance of our personal authorization-controlled framework. Results: The constructed iWellChain Framework was installed in an affiliated teaching hospital and four collaborative clinics. The framework renders all medical referral processes automatic and paperless and facilitates efficient NHIA reimbursements. In addition, the blockchain-enabled iWellChain DApp was distributed for patients to access and control their EMR and EHR data. Analysis of 3 months (September to December 2018) of access logs revealed that patients were highly interested in acquiring health data, especially those of laboratory test reports. Conclusions: This study is a pioneer of blockchain applications for medical referral services, and the constructed framework and DApp have been applied practically in clinical settings. The iWellChain Framework has the scalability to deploy a blockchain environment effectively for health care facilities; the iWellChain DApp has potential for use with more patient-centered applications to collaborate with the industry and facilitate its adoption.

Previously, we showed that chitosan could augment the biocidal efficacy mediated by photodynamic treatment against Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans. In this study, we showed that the antimicrobial action of chitosan in augmenting photodynamic inactivation (PDI) is related to the increase in cell surface destruction. The microbial cell surfaces exhibit severe irregular shapes after PDI in the presence of chitosan as demonstrated by transmitted electron microscopy. Furthermore, increases in the concentration or incubation time of chitosan significantly reduced the amounts of photosensitizer toluidine blue O required, indicating that chitosan could be an augmenting agent used in conjunction with PDI against S. aureus, P. aeruginosa, and C. albicans. A prolonged lag phase was found in microbial cells that survived to PDI, in which chitosan acted to completely eradicate the cells. Once the exponential log stage and cell rebuild began, their cellular functions from PDI-induced damage returned and the increased cytotoxic effect of chitosan disappeared. Together, our results suggest that chitosan can prevent the rehabilitation of PDI-surviving microbial cells, leading to increased biocidal efficacy.

Intraoperative navigation is a tool that provides surgeons with real-time guidance based on patients' preoperative imaging studies. The application of intraoperative navigation to neurosurgery and otolaryngology has been well documented; however, only isolated reports have analyzed its potential in the field of craniomaxillofacial surgery.From November 2010 to July 2014, 15 patients were operated on for complex craniomaxillofacial surgery with assistance by 3 different navigation systems, which used either infrared or electromagnetic technologies. We imported fine-cut (0.625-mm) computed tomographic scan images of the patients to the navigation systems whose software processed them into multisurface 3-dimentional models used as guiding material for the surgical navigation. We also developed a simple "2-plane reference system" to ensure that the final results were symmetric to the normal half of the face. Appearance outcome was evaluated by questionnaire.Of these 15 cases, 3 cases were performed with infrared-based navigation, and the remaining 12 cases were accomplished by electromagnetic technology. Most of these cases resulted in satisfactory outcomes after tumor resection, posttraumatic reconstruction, and postablative reconstruction.Navigation systems offer highly valuable intraoperative assistance in complex craniomaxillofacial surgery. Not only can these systems pinpoint deep-seated lesions as neurosurgeons or otolaryngologists do, but they can also use a simple 2-plane reference system for accurate bone alignment. Moreover, advancements in multisurface 3-D models provide us more reliable intuitive image guidance. The application of electromagnetic technology, with its smaller reference obviation of the line-of-sight problem, makes the manipulation of craniomaxillofacial surgery more comfortable.

Breast cancer patients in Asia show considerable disparities from Caucasian patients, such as younger age of onset and lower rates of smoking, obesity, and diabetes. Findings of prior studies regarding risk factors associated with complications in tissue expander may not hold for Asian populations, since most of these studies involved Caucasian patients. In this study, we surveyed risk factors in the Taiwanese population, providing additional evidence about the important differences and discuss the implications for clinical practice. Patients who underwent immediate, two-stage, tissue expander breast reconstruction from December 2008 to August 2014 in the National Taiwan University Hospital, Taipei, Taiwan were included. Follow-up observations of all patients were conducted until December 2014. Complications occurring during the tissue expander stage were evaluated. Multivariate regression modeling was used to identify risk factors for complications. A total of 246 consecutive, immediate, smooth round tissue expander placements were performed for breast reconstruction. The most common complication was skin necrosis (4.9%), followed by wound dehiscence (4.1%). In the multivariate model, body mass index (BMI) ≥ 24 kg/m2 was the only risk factor that reached statistical significance (odds ratio: 2.41, 95% confidence interval: 1.17-4.96). We provided evidence that racial disparities have an impact on the risk factors for complications associated with tissue expander breast reconstruction. BMI≥24 kg/m2 was the only risk factor significantly associated with complications. Clinically, BMI≥24 kg/m2, rather than the standard definition of obesity (BMI > 30 kg/m2), may be a more suitable cutoff point for risk in patients of Asian ethnicity.

Internal tandem duplication of FLT3 juxtamembrane domain (FLT3-ITD)-positive acute myeloid leukemia (AML) leads to poor clinical outcomes after chemotherapy. We aimed to establish a cytarabine-resistant line from FLT3-ITD-positive MV4-11 (MV4-11-P) cells and examine the development of resistance. The FLT3-ITD mutation was retained in MV4-11-R; however, the protein was underglycosylated and less phosphorylated in these cells. Moreover, the phosphorylation of ERK1/2, Akt, MEK1/2 and p53 increased in MV4-11-R. The levels of Mcl-1 and p53 proteins were also elevated in MV4-11-R. A p53 D281G mutant emerged in MV4-11-R, in addition to the pre-existing R248W mutation. MV4-11-P and MV4-11-R showed similar sensitivity to cabozantinib, sorafenib, and MK2206, whereas MV4-11-R showed resistance to CI-1040 and idarubicin. MV4-11-R resistance may be associated with inhibition of Akt phosphorylation, but not ERK phosphorylation, after exposure to these drugs. The multi-kinase inhibitor cabozantinib inhibited FLT3-ITD signaling in MV4-11-R cells and MV4-11-R-derived tumors in mice. Cabozantinib effectively inhibited tumor growth and prolonged survival time in mice bearing MV4-11-R-derived tumors. Together, our findings suggest that Mcl-1 and Akt phosphorylation are potential therapeutic targets for p53 mutants and that cabozantinib is an effective treatment in cytarabine-resistant FLT3-ITD-positive AML.