Platelet activation is involved in cardiovascular diseases, such as atherosclerosis and ischemic stroke. Licochalcone A (LA), an active ingredient of licorice, exhibits multiple biological activities such as anti-oxidation and anti-inflammation. However, its role in platelet activation remains unclear. Therefore, the study investigated the antiplatelet mechanism of LA. Our data revealed that LA (2–10 μM) concentration dependently inhibited platelet aggregation induced by collagen, but not thrombin and U46619. LA markedly attenuated collagen-stimulated ATP release, P-selectin secretion, calcium mobilization, and GPIIbIIIa activation, but did not interfere with the collagen binding to platelets. Moreover, LA significantly reduced the activation of PLCγ2, PKC, Akt and MAPKs. Thus, LA attenuates platelet activation, possibly by inhibiting collagen receptor downstream signaling but not by blocking the collagen receptors. In addition, LA prevented adenosine diphosphate (ADP)-induced acute pulmonary thrombosis, fluorescein sodium-induced platelet thrombus formation, and middle cerebral artery occlusion/reperfusion-induced brain injury in mice, but did not affect normal hemostasis. This study demonstrated that LA effectively reduced platelet activation and thrombus formation, in part, through the inhibition of PLCγ2–PKC, Akt, and MAPK pathways, without the side effect of bleeding. These findings also indicate that LA may provide a safe and alternative therapeutic approach for preventing thromboembolic disorders such as stroke.

Metastasis is the most dangerous risk faced by patients with hereditary non-polyposis colon cancer (HNPCC). The expression of matrix metalloproteinases (MMPs) has been observed in several types of human cancers and regulates the efficacy of many therapies. Here, we show that treatment with various concentrations of prostaglandin E2 (PGE2; 0, 1, 5 or 10 μM) promotes the migration ability of the human LoVo colon cancer cell line. As demonstrated by mRNA and protein expression analyses, EP2 and EP4 are the major PGE2 receptors expressed on the LoVo cell membrane. The Phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/Akt cell survival pathway was upregulated by EP2 and EP4 activation. Following the activation of the PI3K/Akt pathway, β-catenin translocated into the nucleus and triggered COX2 transcription via LEF-1 and TCF-4 and its subsequent translation. COX2 expression correlated with the elevation in the migration ability of LoVo cells. The experimental evidence shows a possible mechanism by which PGE2 induces cancer cell migration and further suggests PGE2 to be a potential therapeutic target in colon cancer metastasis. On inhibition of PGE2, in order to determine the downstream pathway, the levels of PI3K/Akt pathway were suppressed and the β-catenin expression was also modulated. Inhibition of EP2 and EP4 shows that PGE2 induces protein expression of COX-2 through EP2 and EP4 receptors in LoVo colon cancer cells.

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.

Obesity is one of the major risk factors for nonalcoholic fatty liver disease (NAFLD), and NAFLD is highly associated with an increased risk of cardiovascular disease (CVD). Scholars have suggested that certain probiotics may significantly impact cardiovascular health, particularly certain Lactobacillus species, such as Lactobacillus reuteri GMNL-263 (Lr263) probiotics, which have been shown to reduce obesity and arteriosclerosis in vivo. In the present study, we examined the potential of heat-killed bacteria to attenuate high fat diet (HFD)-induced hepatic and cardiac damages and the possible underlying mechanism of the positive effects of heat-killed Lr263 oral supplements. Heat-killed Lr263 treatments (625 and 3125 mg/kg-hamster/day) were provided as a daily supplement by oral gavage to HFD-fed hamsters for eight weeks. The results show that heat-killed Lr263 treatments reduce fatty liver syndrome. Moreover, heat-killed Lactobacillus reuteri GMNL-263 supplementation in HFD hamsters also reduced fibrosis in the liver and heart by reducing transforming growth factor β (TGF-β) expression levels. In conclusion, heat-killed Lr263 can reduce lipid metabolic stress in HFD hamsters and decrease the risk of fatty liver and cardiovascular disease.

Aging, a natural biological/physiological phenomenon, is accelerated by reactive oxygen species (ROS) accumulation and identified by a progressive decrease in physiological function. Several studies have shown a positive relationship between aging and chronic heart failure (HF). Cardiac apoptosis was found in age-related diseases. We used a traditional Chinese medicine, Alpinate Oxyphyllae Fructus (AOF), to evaluate its effect on cardiac anti-apoptosis and pro-survival. Male eight-week-old Sprague–Dawley (SD) rats were segregated into five groups: normal control group (NC), d-Galactose-Induced aging group (Aging), and AOF of 50 (AL (AOF low)), 100 (AM (AOF medium)), 150 (AH (AOF high)) mg/kg/day. After eight weeks, hearts were measured by an Hematoxylin–Eosin (H&E) stain, Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-assays and Western blotting. The experimental results show that the cardiomyocyte apoptotic pathway protein expression increased in the d-Galactose-Induced aging groups, with dose-dependent inhibition in the AOF treatment group (AL, AM, and AH). Moreover, the expression of the pro-survival p-Akt (protein kinase B (Akt)), Bcl-2 (B-cell lymphoma 2), anti-apoptotic protein (Bcl-xL) protein decreased significantly in the d-Galactose-induced aging group, with increased performance in the AOF treatment group with levels of p-IGFIR and p-PI3K (Phosphatidylinositol-3′ kinase (PI3K)) to increase by dosage and compensatory performance. On the other hand, the protein of the Sirtuin 1 (SIRT1) pathway expression decreased in the aging groups and showed improvement in the AOF treatment group. Our results suggest that AOF strongly works against ROS-induced aging heart problems.

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.