Topical Formulations for the Transdermal Delivery of Niacin and Methods of Treating Hyperlipidemia

Niacin and niacin prodrugs are topically administered as suitable formulations to device for impoving the lipid profiles of subjects, preferably humans

Topical Micronutrient Delivery System and Uses of Thereof

The invention involves methods and compositions useful in delivering micronutrients to cells. By formulating the micronutrient in the form of an ester that is convertible to the active form of the micronutrient, one can combine it with a co-ester that inhibits esterases, so that the micronutrient can reach the targeted cells prior to degradation. Both methods and compositions are described

Methods and Compositions Useful in Enhancing Oxygen Delivery to Cells

The invention discloses compositions and methods which are useful in improving delivery of oxygen to cells. The compositions require at least one derivative of a compound. The derivatives are chosen to have log P values below about 6.0

IoT-Based Intelligent Monitoring System Applying RNN

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources

IoT-Based Intelligent Monitoring System Applying RNN

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources

An IoT Platform with Monitoring Robot Applying CNN-Based Context-Aware Learning

Internet of Things (IoT) technology has been attracted lots of interests over the recent years, due to its applicability across the various domains. In particular, an IoT-based robot with artificial intelligence may be utilized in various fields of surveillance. In this paper, we propose an IoT platform with an intelligent surveillance robot using machine learning in order to overcome the limitations of the existing closed-circuit television (CCTV) which is installed fixed type. The IoT platform with a surveillance robot provides the smart monitoring as a role of active CCTV. The intelligent surveillance robot, which has been built with its own IoT server, and can carry out line tracing and acquire contextual information through the sensors to detect abnormal status in an environment. In addition, photos taken by its camera can be compared with stored images of normal state. If an abnormal status is detected, the manager receives an alarm via a smart phone. For user convenience, the client is provided with an app to control the robot remotely. In the case of image context processing it is useful to apply convolutional neural network (CNN)-based machine learning (ML), which is introduced for the precise detection and recognition of images or patterns, and from which can be expected a high performance of recognition. We designed the CNN model to support contextually-aware services of the IoT platform and to perform experiments for learning accuracy of the designed CNN model using dataset of images acquired from the robot. Experimental results showed that the accuracy of learning is over 0.98, which means that we achieved enhanced learning in image context recognition. The contribution of this paper is not only to implement an IoT platform with active CCTV robot but also to construct a CNN model for image-and-context-aware learning and intelligence enhancement of the proposed IoT platform. The proposed IoT platform, with an intelligent surveillance robot using machine learning, can be used to detect abnormal status in various industrial fields such as factory, smart farms, logistics warehouses, and public places

Methodologies for investigating natural medicines for the treatment of nonalcoholic fatty liver disease (NAFLD)

Non-alcoholic fatty liver disease (NAFLD) is emerging as a prominent condition in Western countries. In this review we describe the characteristics and current treatments of NAFLD and discuss opportunities for developing new therapeutic management approaches, with a particular emphasis on development of animal studies and in vitro assays for identification of components of natural product medicines. The main manifestation of NAFLD is hepatic lipid accumulation in the form of lipid droplets (LDs), known as hepatic steatosis (fatty liver). Current treatments for NAFLD generally aim to reduce triglyceride (TG) accumulation, often utilizing thiazolidinedines (TZDs) and fibrates, which are known to lower TG levels in hyperlipidemia, diabetes and metabolic syndrome. Both of these compounds act through activation of nuclear receptors of the Peroxisome Proliferator-Activated Receptor (PPAR) family, thereby activating genes involved in triglyceride metabolism. Thus treatment using natural PPARα and PPARγ ligands, such as polyunsaturated fatty acids (PUFA), has also been considered. Alternatively, natural medicines for the treatment of NAFLD have a long and successful history of controlling disease without prominent side effects. However, active compounds in natural medicine responsible for lowering hepatic TG levels are yet poorly characterized. This points to the need for medium-high throughput screening assays to identify active components within natural herbs. As outlined in this review, the quantification of the size and number of lipid droplets could provide an opportunity to screen compound libraries derived from natural medicine for their potential to reduce NAFLD

Regulation of low-density lipoprotein receptor and 3-hydroxy-3-methylglutaryl coenzyme A reductase expression by Zingiber officinale in the liver of high-fat diet-fed rats

Zingiber officinale has been used to control lipid disorders and reported to possess remarkable cholesterol-lowering activity in experimental hyperlipidaemia. In the present study, the effect of a characterized and standardized extract of Zingiber officinale on the hepatic lipid levels as well as on the hepatic mRNA and protein expression of low-density lipoprotein (LDL) receptor and 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase was investigated in a high-fat diet-fed rat model. Rats were treated with an ethanol extract of Zingiber officinale (400 mg/kg) extract along with a high-fat diet for 6 weeks. The extract of Zingiber officinale significantly decreased hepatic triglyceride and tended to decrease hepatic cholesterol levels when administered over 6 weeks to the rats fed a high-fat diet. We found that in parallel, the extract up-regulated both LDL receptor mRNA and protein level and down-regulated HMG-CoA reductase protein expression in the liver of these rats. The metabolic control of body lipid homeostasis is in part due to enhanced cholesterol biosynthesis and reduced expression of LDL receptor sites following long-term consumption of high-fat diets. The present results show restoration of transcriptional and post-transcriptional changes in low-density lipoprotein and HMG CoA reductase by Zingiber officinale administration with a high-fat diet and provide a rational explanation for the effect of ginger in the treatment of hyperlipidaemia