Formulation optimization for high drug loading colonic drug delivery carrier

High drug loading (DL) carrier is an effective way to cure the cancerous cells. High drug loading is also one of the key issues in the drug delivery research, especially the colonic drug delivery system by oral administration. The times of drug intake could be remarkably reduced if high drug loading carriers are administered. At the same time, the related formulation materials could be effectively utilized. One major obstacle with the preparation of this system is the difficulty to encapsulate the hydrophilic drug into hydrophobic encapsulation polymer. A design of high drug loading delivery system with biodegradable, biocompatible materials and optimization of the fabrication process is a potential solution to solve the problem. So in this research, 5-Fluorouracil (5-FU) loaded Poly (lactide-co-glycolide) (PLGA) nanoparticles were prepared by double emulsion and solvent evaporation method. Several fabrication parameters including theoretical drug loading, volume ratio of outer water phase to the first emulsion, pH value of outer aqueous phase and emulsifier PVA concentration were optimized to get a high drug loading nanoparticles. The result shows that with the increase of theoretical drug loading, the actual drug loading increased gradually. When adjusted the pH value of outer aqueous phase to the isoelectric point (8.02) of 5-Fluorouracil, the drug loading exhibited a higher one compared to other pH value solution. Relative higher volume ratio of outer water phase to the first emulsion was also beneficial for the enhancement of drug loading. But the nanoparticles size increased simultaneously due to the lower shearing force. When increased the PVA concentration, the drug loading showed an increase first and following a drop

Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks

Despite the broad application of Machine Learning models as a Service (MLaaS), they are vulnerable to model stealing attacks. These attacks can replicate the model functionality by using the black-box query process without any prior knowledge of the target victim model. Existing stealing defenses add deceptive perturbations to the victim's posterior probabilities to mislead the attackers. However, these defenses are now suffering problems of high inference computational overheads and unfavorable trade-offs between benign accuracy and stealing robustness, which challenges the feasibility of deployed models in practice. To address the problems, this paper proposes Isolation and Induction (InI), a novel and effective training framework for model stealing defenses. Instead of deploying auxiliary defense modules that introduce redundant inference time, InI directly trains a defensive model by isolating the adversary's training gradient from the expected gradient, which can effectively reduce the inference computational cost. In contrast to adding perturbations over model predictions that harm the benign accuracy, we train models to produce uninformative outputs against stealing queries, which can induce the adversary to extract little useful knowledge from victim models with minimal impact on the benign performance. Extensive experiments on several visual classification datasets (e.g., MNIST and CIFAR10) demonstrate the superior robustness (up to 48% reduction on stealing accuracy) and speed (up to 25.4x faster) of our InI over other state-of-the-art methods. Our codes can be found in https://github.com/DIG-Beihang/InI-Model-Stealing-Defense.Comment: Accepted by ACM Multimedia 202

USP7: Novel Drug Target in Cancer Therapy

Ubiquitin specific protease 7 (USP7) is one of the deubiquitinating enzymes (DUB) that erases ubiquitin and protects substrate protein from degradation. Full activity of USP7 requires the C-terminal Ub-like domains fold back onto the catalytic domain, allowing the remodeling of the active site to a catalytically competent state by the C-terminal peptide. Until now, numerous proteins have been identified as substrates of USP7, which play a key role in cell cycle, DNA repair, chromatin remodeling, and epigenetic regulation. Aberrant activation or overexpression of USP7 may promote oncogenesis and viral disease, making it a target for therapeutic intervention. Currently, several synthetic small molecules have been identified as inhibitors of USP7, and applied in the treatment of diverse diseases. Hence, USP7 may be a promising therapeutic target for the treatment of cancer

A \u3cem\u3eLIN28B\u3c/em\u3e Tumor-Specific Transcript in Cancer

The diversity and complexity of the cancer transcriptome may contain transcripts unique to the tumor environment. Here, we report a LIN28B variant, LIN28B-TST, which is specifically expressed in hepatocellular carcinoma (HCC) and many other cancer types. Expression of LIN28B-TST is associated with significantly poor prognosis in HCC patients. LIN28B-TST initiates from a de novo alternative transcription initiation site that harbors a strong promoter regulated by NFYA but not c-Myc. Demethylation of the LIN28B-TST promoter might be a prerequisite for its transcription and transcriptional regulation. LIN28B-TST encodes a protein isoform with additional N-terminal amino acids and is critical for cancer cell proliferation and tumorigenesis. Our findings reveal a mechanism of LIN28B activation in cancer and the potential utility of LIN28B-TST for clinical purposes

Recursive weighted treelike networks

We propose a geometric growth model for weighted scale-free networks, which is controlled by two tunable parameters. We derive exactly the main characteristics of the networks, which are partially determined by the parameters. Analytical results indicate that the resulting networks have power-law distributions of degree, strength, weight and betweenness, a scale-free behavior for degree correlations, logarithmic small average path length and diameter with network size. The obtained properties are in agreement with empirical data observed in many real-life networks, which shows that the presented model may provide valuable insight into the real systems

Leaf size of woody dicots predicts ecosystem primary productivity

A key challenge in ecology is to understand the relationships between organismal traits and ecosystem processes. Here, with a novel dataset of leaf length and width for 10 480 woody dicots in China and 2374 in North America, we show that the variation in community mean leaf size is highly correlated with the variation in climate and ecosystem primary productivity, independent of plant life form. These relationships likely reflect how natural selection modifies leaf size across varying climates in conjunction with how climate influences canopy total leaf area. We find that the leaf size–primary productivity functions based on the Chinese dataset can predict productivity in North America and vice-versa. In addition to advancing understanding of the relationship between a climate-driven trait and ecosystem functioning, our findings suggest that leaf size can also be a promising tool in palaeoecology for scaling from fossil leaves to palaeo-primary productivity of woody ecosystems

GEP100/Arf6 Is Required for Epidermal Growth Factor-Induced ERK/Rac1 Signaling and Cell Migration in Human Hepatoma HepG2 Cells

BACKGROUND: Epidermal growth factor (EGF) signaling is implicated in the invasion and metastasis of hepatoma cells. However, the signaling pathways for EGF-induced motility of hepatoma cells remain undefined. METHODOLOGY/PRINCIPAL FINDINGS: We found that EGF dose-dependently stimulated the migration of human hepatoma cells HepG2, with the maximal effect at 10 ng/mL. Additionally, EGF increased Arf6 activity, and ectopic expression of Arf6 T27N, a dominant negative Arf6 mutant, largely abolish EGF-induced cell migration. Blocking GEP100 with GEP100 siRNA or GEP100-△PH, a pleckstrin homology (PH) domain deletion mutant of GEP100, blocked EGF-induced Arf6 activity and cell migration. EGF also increased ERK and Rac1 activity. Ectopic expression GEP100 siRNA, GEP100-△PH, or Arf6-T27N suppressed EGF-induced ERK and Rac1 activity. Furthermore, blocking ERK signaling with its inhibitor U0126 remarkably inhibited both EGF-induced Rac1 activation as well as cell migration, and ectopic expression of inactive mutant form of Rac1 (Rac1-T17N) also largely abolished EGF-induced cell migration. CONCLUSIONS/SIGNIFICANCE: Taken together, this study highlights the function of the PH domain of GEP100 and its regulated Arf6/ERK/Rac1 signaling cascade in EGF-induced hepatoma cell migration. These findings could provide a rationale for designing new therapy based on inhibition of hepatoma metastasis

Fluid dynamics analysis of sloshing pressure distribution in storage vessels of different shapes

© 2019 Elsevier Ltd A series of numerical simulations were performed to investigate the influences of storage vessels shapes on sloshing dynamics under horizontal excitation by employing the open source code OpenFOAM, which has been extensively validated by experimental data for the sloshing flow problem. The results show that the membrane liquefied natural gas (LNG) tanks are subject to lower impact pressure than the cylindrical, rectangular and spherical tanks with the same volume of liquid and the overall tank dimensions, as the slope at the storage vessels bottom changes the flow direction of the liquid and therefore reduces the impact on the vertical wall. In the cylindrical and spherical tanks, higher impact pressure was found on the wall directly opposite to the excitation direction and the maximum impact point will shift away from the external excitation direction as the wave breaks up violently until a quasi-steady state of the sloshing wave rotating along the side wall is reached. The curved surface of the spherical tank could also help reduce the impact pressure when compared with the cylindrical tank