Retinoic acid liposome-hydrogel: preparation, penetration through mouse skin and induction of F9 mouse teratocarcinoma stem cells differentiation

Retinoic acid (RA), a metabolite of retinol, is one of the most biologically active forms of retinoid and plays vital roles in embryonic development and in the regulation of cell proliferation and differentiation. Knowing that liposomes simulate cell membranes and that hydrogel is an ideal delivery vehicle for topical medicine, liposome-hydrogel is a novel preparation that has synergistic advantages over each component separately. Our objective was to investigate the characteristics of RA liposome-hydrogel. For quality control of the RA-loaded liposomes, we measured their morphology, particle size, Zeta-potential, and entrapment efficiency. Then we determined the viscosity of RA liposome-hydrogel. Next, the diffusion through mouse skin was explored, followed by investigation of the mRNA expression levels of Ker18, REX1, and α-FP using Q-PCR. The results showed that RA liposome-hydrogel penetrates the mouse skin effectively. The permeation rates were: Qn (%) of RA liposome-hydrogel < Qn(%) of RA-loaded liposome < Qn (%) of RA. The mRNA expression levels were dose-dependent and the effective dose decreased between vehicles due to their different release rates. F9 mouse teratocarcinoma stem cells were an ideal model to explore the mechanism of RA liposome-hydrogel in stem cell differentiation.O ácido retinóico (RA) é um metabolito de retinol. Ele também é uma das formas mais biologicamente ativas de retinóide. Desempenha papel vital no desenvolvimento embrionário e na regulação da proliferação e diferenciação celular. Sabendo-se que lipossomas simulam a membrana das células e que hidrogel é um sistema ideal para o medicamento tópico, o lipossoma-hidrogel é uma nova preparação, que apresenta vantagens sinérgicas em relação a cada um dos componentes separados. Nosso objetivo foi investigar as características de RA lipossoma-hidrogel. A fim de controlar a qualidade do lipossoma carregado com RA, medimos morfologia, tamanho das partículas, potencial zeta e eficiência de retenção. Em seguida, determinou-se a viscosidade de RA lipossoma-hidrogel. Em seguida, avaliou-se a sua difusão através da pele de camundongos, seguida da investigação dos níveis da expressão de mRNA de Ker18, REX e de α-FP, utilizando-se Q-PCR. Os resultados mostraram que RA lipossoma-hidrogel pode penetrar na pele do camundongo de forma eficaz. As taxas de permeação foram: Qn (%) de RA lipossoma-hidroge

Laparoscopic Transient Uterine Artery Occlusion and Myomectomy for Symptomatic Uterine Myoma as an Alternative to Hysterectomy

Objective: To compare the clinical outcomes of laparoscopic transient uterine artery ligation plus myomectomy (LTUAL) to simple laparoscopic myomectomy (LM) for symptomatic myomas

Flocculation performance of anionic starch in oil sand tailings

Abstract A series of carboxymethyl starches (CMSs), with various degrees of substitution from 0.1 to 0.79, were synthesized and selected as a model to study the feasibility of using natural polymers as flocculants for oil sand tailings treatment. The flocculation performance of modified CMS in kaolin clay suspensions and oil sand tailings was evaluated in terms of settling rate, solids content, capillary suction time, and specific resistance to filtration of the sediment phase. It was found that the synthesized CMS effectively accelerates settling of kaolin suspensions and oil sand fine tailings, thus demonstrating the feasibility of this application

DropMessage: Unifying Random Dropping for Graph Neural Networks

Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and non-robustness. Previous works indicate that these problems can be alleviated by random dropping methods, which integrate noises into models by randomly masking parts of the input. However, some open-ended problems of random dropping on GNNs remain to solve. First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models. Second, random noises introduced to GNNs cause the incomplete coverage of parameters and unstable training process. In this paper, we propose a novel random dropping method called DropMessage, which performs dropping operations directly on the message matrix and can be applied to any message-passing GNNs. Furthermore, we elaborate the superiority of DropMessage: it stabilizes the training process by reducing sample variance; it keeps information diversity from the perspective of information theory, which makes it a theoretical upper bound of other methods. Also, we unify existing random dropping methods into our framework and analyze their effects on GNNs. To evaluate our proposed method, we conduct experiments that aims for multiple tasks on five public datasets and two industrial datasets with various backbone models. The experimental results show that DropMessage has both advantages of effectiveness and generalization

Downlink and Uplink Cooperative Joint Communication and Sensing

Downlink (DL) and uplink (UL) joint communication and sensing (JCAS) technologies have been individually studied for realizing sensing using DL and UL communication signals, respectively. Since the spatial environment and JCAS channels in the consecutive DL and UL JCAS time slots are generally unchanged, DL and UL JCAS may be jointly designed to achieve better sensing performance. In this paper, we propose a novel DL and UL cooperative (DUC) JCAS scheme, including a unified multiple signal classification (MUSIC)-based JCAS sensing scheme for both DL and UL JCAS and a DUC JCAS fusion method. The unified MUSIC JCAS sensing scheme can accurately estimate AoA, range, and Doppler based on a unified MUSIC-based sensing module. The DUC JCAS fusion method can distinguish between the sensing results of the communication user and other dumb targets. Moreover, by exploiting the channel reciprocity, it can also improve the sensing and channel state information (CSI) estimation accuracy. Extensive simulation results validate the proposed DUC JCAS scheme. It is shown that the minimum location and velocity estimation mean square errors of the proposed DUC JCAS scheme are about 20 dB lower than those of the state-of-the-art separated DL and UL JCAS schemes.Comment: 14 pages, 10 figures, submitted to IEEE Transactions on Communication