Beom-Jin Lee

Pharmacokinetics and tissue distribution of methotrexate after intravenous injection of differently charged liposome-entrapped methotrexate to rats

Abstract Liposome-entrapped [ 3 H]MTX was prepared by modification of reverse-phase evaporation vesicle (REV) methods. Neutral liposomes were prepared with a mixture of phosphatidylcholine (PC), cholesterol (CH) and α-tocopherol (α-T) (8:4:0.1, molar ratio). Positively and negatively charged liposomes were also prepared by incorporation of stearylamine (SA) (8:4:0.1:1, molar ratio) and dicetyl phosphate (DCP) (8:4:0.1:1, molar ratio) into neutral liposomes, respectively. The release profiles of [ 3 H]MTX from liposomes in the presence of rat plasma were the same shape regardless of surface charges, showing initial fast release followed by much slower release. The release profiles of [ 3 H]MTX from liposomes were dependent on the surface charge of liposomes. Negatively charged liposomes showed much greater [ 3 H]MTX release compared to positively charged and neutral liposomes. The enhanced in vitro release of [ 3 H]MTX from negatively charged liposomes in the presence of rat plasma may be due to the interaction of incorporated lipid compositions of liposomes with plasma components, resulting in a change in drug release through the lipid bilayers by destabilization of the liposomal membranes. After intravenous (i.v.) injection of free and liposome-entrapped [ 3 H]MTX to rats, the elimination of [ 3 H]MTX from the blood stream showed biphasic patterns indicating rapid declining disposition up to 30 min followed by a slower elimination phase. Liposome-entrapped [ 3 H]MTX maintained a higher and longer plasma concentration, and mainly intact form in plasma compared to free [ 3 H]MTX for 2 h. Liposome-entrapped [ 3 H]MTX enhanced bioavailability in plasma due to the retardation of drug release and protection of drug clearance by lipid bilayers of liposomes. Negatively charged liposome-entrapped drug was cleared more rapidly from the blood, resulting from the rapid uptake by liver to a greater extent, possibly via the reticuloendothelial system (RES), since liposome-entrapped drug cannot be eliminated by the kidney, the main eliminating organ for MTX. The tissue distribution of liposome-entrapped [ 3 H]MTX was widely different when compared to free [ 3 H]MTX. There was a markedly increased uptake of drug in spleen from neutral and negatively charged liposomes. Negatively charged liposomes also increased localization of drug in liver, lung and lymph nodes compared to neutral and positively charged liposomes at 2 h after i.v. injection to rats. From these findings, liposomes containing anticancer drugs would appear to be an effective carrier system for targeting to these sites. Although liposome-entrapped [ 3 H]MTX was widely distributed in tissues without the exact role of the liposomal surface charge being known, it was evident that the surface charge of liposomes altered the biodistribution and membrane permeation of drug.

Preparation and evaluation of temperature sensitive liposomes containing adriamycin and cytarabine

Temperature sensitive liposomes (TSL) containing adriamycin (ADM) and cytarabine (Ara-C) were prepared. ADM and Ara-C were selected as model compounds of amphiphilic and hydrophilic drug, respectively. Encapsulation efficiency of ADM entrapped into TSL was about twice greater than that of Ara-C. It might be due to different polarity of the drugs. Lipid compositions of TSL had no effect on the encapsulation efficiency of drugs. Thermal behavior of TSL using a differential scanning calorimetry (DSC) was also investigated. Phase transition temperature (Tc) of TSL was dependent on the lipid compositions of TSL.ADM broadened thermogram of TSL but Ara-C did not. However, Tc of TSL was not changed by any drug. Release rate of drugs was highly dependent on temperature. The release profile of ADM was similar to that of Ara-C. The maximum release rate of drugs from TSL was occurred at the near Tc and observed at 39–41°C for DPPC (Dipalmitoylphosphatidylcholine) only, 52–54°C for DSPC (Distearoylphosphatidylcholine) only, 41–43°C for DPPC and DSPC (3∶1), and 43–45°C for DPPC and DSPC (1∶1), respectively. Effect of human serum albumin (HSA) on the release rate of ADM was investigated. HSA had no significant effect on the release of ADM below Tc. However, ADM release from TSL was increased at the near and above Tc. The HSA-induced leakage of drug may result from the interaction of liposomal constituents with HSA structure at the near Tc. From the fact that the release profiles of ADM from freshly prepared TSL and stored TSL for 1 week at 4°C was not changed, the TSL was considered to be stable for at least 1 week at 4°C. Based on these findings, TSL may be useful to deliver drugs to preheated target sites due to its thermal behaviors.

Effect of salts on the entrapment of calf thymus DNA into liposomes

To correlate the conformational changes of DNA (Calf Thymus) with entrapment of DNA into lipsomes, the effect of ions(Na+, Mg++) on the entrapment of calf thymus DNA into liposomes was investigated. The effect of divalent ion(Mg++) on the structural changes of DNA indicated by decrease of observed ellipticity at 274 nm and nonspecific binding of DNA to lipid bilayers was greater than monovalent ion(Na+). But the efficiency of DNA encapsulated was not altered. These results show that entrapment of DNA into liposomes is not due to nonspecific binding and structural changes because of electrostatic forces but to mechanical capture of DNA by the internal aqueous space of liposomes although divalent ion contributes large structural changes and more nonspecific association of DNA with liposomes due to strong charges.

Evolutionary concept learning from cartoon videos by multimodal hypernetworks

Concepts have been widely used for categorizing and representing knowledge in artificial intelligence. Previous researches on concept learning have focused on unimodal data, usually on linguistic domains in a static environment. Concept learning from multimodal stream data, such as videos, remains a challenge due to their dynamic change and high-dimensionality. Here we propose an evolutionary method that simulates the process of human concept learning from multimodal video streams. Two key ideas on evolutionary concept learning are representing concepts in a large collection (population) of hyperedges or a hypergraph and to incrementally learning from video streams based on an evolutionary approach. The hypergraph is learned evolutionarily by repeating the generation and selection process of hyperedge concepts from the video data. The advantage of this evolutionary learning process is that the population-based distributed coding allows flexible and robust trace of the change of concept relations as the video story unfolds. We evaluate the proposed method on a suite of childrens cartoon videos for 517 minutes of total playing time. Experimental results show that the proposed method effectively represents visual-textual concept relations and our evolutionary concept learning method effectively models the conceptual change as an evolutionary process. We also investigate the structure properties of the constructed concept networks.

Place Recognition Using Ensemble Learning of Mobile Multimodal Sensory Information

Place awareness is an essential for location-based services that are widely provided to smartphone users. However, traditional GPS-based methods are only valid outdoors where the GPS signal is strong and also require symbolic place information of the physical location. In this paper, environmental sounds and images are used to recognize important aspects of each place. The proposed method extracts feature vectors from visual, auditory and location data recorded by a smartphone with built-in camera, microphone and GPS sensors modules. The heterogeneous feature vectors were then learned by an ensemble learning method that learns each group of feature vectors for each classifier respectively and votes to produce the highest weighted result. The proposed method is evaluated for place recognition using a data group of 3000 samples in six places and the experimental results show a remarkably improved recognition accuracy when using all kinds of sensory data comparing to results using data from a single sensor or audio-visual integrated data only.

Character-based Subtitle Generation by Learning of Multimodal Concept Hierarchy from Cartoon Videos

Previous multimodal learning methods focus on problem-solving aspects, such as image and video search and tagging, rather than on knowledge acquisition via content modeling. In this paper, we propose the Multimodal Concept Hierarchy (MuCH), which is a content modeling method that uses a cartoon video dataset and a character-based subtitle generation method from the learned model. The MuCH model has a multimodal hypernetwork layer, in which the patterns of the words and image patches are represented, and a concept layer, in which each concept variable is represented by a probability distribution of the words and the image patches. The model can learn the characteristics of the characters as concepts from the video subtitles and scene images by using a Bayesian learning method and can also generate character-based subtitles from the learned model if text queries are provided. As an experiment, the MuCH model learned concepts from Pororo cartoon videos with a total of 268 minutes in length and generated character-based subtitles. Finally, we compare the results with those of other multimodal learning models. The Experimental results indicate that given the same text query, our model generates more accurate and more character-specific subtitles than other models.

Text-to-image retrieval based on incremental association via multimodal hypernetworks

Text-to-image retrieval is to retrieve the images associated with the textual queries. A text-to-image retrieval model requires an incremental learning method for its practical use since the multimodal data grow up dramatically. Here we propose an incremental text-to-image retrieval method using a multimodal association model. The association model is based on a hypernetwork (HN) where a vertex corresponds to a textual word or a visual patch and a hyperedge represents a higher-order multimodal association. Using the HN incrementally learned by a sequential Bayesian sampling, in the multimodal hypernetwork-based text-to-image retrieval, a given text query is crossmodally expanded to the visual query and then similar images are retrieved to the expanded visual query. We evaluated the proposed method using 3,000 images with textual description from Flickr.com. The experimental results present that the proposed method achieves very competitive retrieval performances compared to a baseline method. Moreover, we demonstrate that our method provides robust text-to-image retrieval results for the increasing data.

Event Cognition-based Daily Activity Prediction Using Wearable Sensors

Learning from human behaviors in the real world is essential for human-aware intelligent systems such as smart assistants and autonomous robots. Most of research focuses on correlations between sensory patterns and a label for each activity. However, human activity is a combination of several event contexts and is a narrative story in and of itself. We propose a novel approach of human activity prediction based on event cognition. Egocentric multi-sensor data are collected from an individuals daily life by using a wearable device and smartphone. Event contexts about location, scene and activities are then recognized, and finally the users daily activities are predicted from a decision rule based on the event contexts. The proposed method has been evaluated on a wearable sensor data collected from the real world over 2 weeks by 2 people. Experimental results showed improved recognition accuracies when using the proposed method comparing to results directly using sensory features.

Smartphone-User Interactive based Self Developing Place-Time-Activity Coupled Prediction Method for Daily Routine Planning System

Over the past few years, user needs in the smartphone application market have been shifted from diversity toward intelligence. Here, we propose a novel cognitive agent that plans the daily routines of users using the lifelog data collected by the smart phones of individuals. The proposed method first employs DPGMM (Dirichlet Process Gaussian Mixture Model) to automatically extract the users POI (Point of Interest) from the lifelog data. After extraction, the POI and other meaningful features such as GPS, the users activity label extracted from the log data is then used to learn the patterns of the users daily routine by POMDP (Partially Observable Markov Decision Process). To determine the significant patterns within the users time dependent patterns, collaboration was made with the SNS application Foursquare to record the locations visited by the user and the activities that the user had performed. The method was evaluated by predicting the daily routine of seven users with 3300 feedback data. Experimental results showed that daily routine scheduling can be established after seven days of lifelogged data and feedback data have been collected, demonstrating the potential of the new method of place-time-activity coupled daily routine planning systems in the intelligence application market.