Glycyrrhizin could reduce ocular hypertension induced by triamcinolone acetonide in rabbits

Purpose: To evaluate the hypotensive effects of glycyrrhizin (GL) on a rabbit model of ocular hypertension (OH) induced by triamcinolone acetonide (TA). Methods: Forty New Zealand White Rabbits were divided as follows: control (intravitreal injection of sterile saline solution); GL (intravitreal injection of sterile saline solution, then fed with 25mg GL/day); TA (intravitreal TA injection); TA+GL (intravitreal TA injection, then fed with GL) and GL+TA (pre-treated with GL for 3 days, then got TA injection and the following GL treatment). Intraocular pressure (IOP), flash electroretinogram (flash ERG) and flash visual evoked potential (flash VEP) were measured during the follow-up (28 days). The aqueous humor was analyzed, using (1)H-nuclear magnetic resonance spectroscopy and principal components analysis (PCA). Results: IOP elevation was observed in the TA group during the follow-up, compared to the controls (p<0.01). The IOP was decreased in the TA+GL group and the GL+TA group, compared to the TA group (p<0.05). Both in flash ERG and VEP, the amplitudes were decreased, and the implicit time was prolonged in the TA group, compared to the controls (p<0.05); and the parameters were improved after intervention of GL, compared to the TA group (p<0.05). PCA results indicated that TA could affect ocular metabolism (especially the sugar metabolism), and GL could inhibit it. Conclusions: The administration of GL could suppress OH induced by TA in rabbits, and improve their electrophysiological parameters. Metabolomics is a useful tool in ophthalmology research. Our results indicate that TA-induced ocular metabolism changes could be compensated by GL.Biochemistry & Molecular BiologyOphthalmologySCI(E)6ARTICLE2242056-20641

You Can Use But Cannot Recognize: Preserving Visual Privacy in Deep Neural Networks

Image data have been extensively used in Deep Neural Network (DNN) tasks in various scenarios, e.g., autonomous driving and medical image analysis, which incurs significant privacy concerns. Existing privacy protection techniques are unable to efficiently protect such data. For example, Differential Privacy (DP) that is an emerging technique protects data with strong privacy guarantee cannot effectively protect visual features of exposed image dataset. In this paper, we propose a novel privacy-preserving framework VisualMixer that protects the training data of visual DNN tasks by pixel shuffling, while not injecting any noises. VisualMixer utilizes a new privacy metric called Visual Feature Entropy (VFE) to effectively quantify the visual features of an image from both biological and machine vision aspects. In VisualMixer, we devise a task-agnostic image obfuscation method to protect the visual privacy of data for DNN training and inference. For each image, it determines regions for pixel shuffling in the image and the sizes of these regions according to the desired VFE. It shuffles pixels both in the spatial domain and in the chromatic channel space in the regions without injecting noises so that it can prevent visual features from being discerned and recognized, while incurring negligible accuracy loss. Extensive experiments on real-world datasets demonstrate that VisualMixer can effectively preserve the visual privacy with negligible accuracy loss, i.e., at average 2.35 percentage points of model accuracy loss, and almost no performance degradation on model training.Comment: 18 pages, 11 figure

Event-triggered impulsive control for second-order nonlinear multi-agent systems under DoS attacks

We investigated impulsive consensus in second-order nonlinear multi-agent systems (MASs) under Denial-of-Service (DoS) attacks. We consided scenarios where the communication network is subjected to DoS attacks, disrupting communication links and causing changes in the communication topology. An event-triggered impulsive control(ETIC) approach is proposed to flexibly address these issues. Additionally, an upper bound on the DoS attack period is introduced. Finally, a numerical example is given to verify the validity of the major results

Efficacy of intravenous amphotericin B-polybutylcyanoacrylate nanoparticles against cryptococcal meningitis in mice

Amphotericin B deoxycholate (AmB), a classic antifungal drug, remains the initial treatment of choice for deep fungal infections, but it is not appropriate for treatment of cryptococcal meningitis due to its inability to pass through the blood–brain barrier (BBB). We examined the efficacy of amphotericin B-polybutylcyanoacrylate nanoparticles (AmB-PBCA-NPs) modified with polysorbate 80 that had a mean particle diameter less than 100 nanometers (69.0 ± 28.6 nm). AmB-PBCA-NPs were detected in the brain 30 minutes after systemic administration into BALB/c mice and had a higher concentration than systemically administered AmB liposome (AmB-L, P < 0.05); AmB was not detected in the brain. Following infection for 24 hours and then 7 days of treatment, the survival rate of mice in the AmB-PBCA-NP group (80%) was significantly higher than that of the AmB (0%) or AmB-L (60%) treatment groups. Fungal load was also lower when assessed by colony-forming unit counts obtained after plating infected brain tissue (P < 0.05). Our study indicates that AmB-PBCA-NPs with polysorbate 80 coating have the capacity to transport AmB across the BBB and is an efficient treatment against cryptococcal meningitis in a mouse model

RGD-conjugated gold nanorods induce radiosensitization in melanoma cancer cells by downregulating αvβ3 expression

Background: Melanoma is known to be radioresistant and traditional treatments have been intractable. Therefore, novel approaches are required to improve the therapeutic efficacy of melanoma treatment. In our study, gold nanorods conjugated with Arg-Gly-Asp peptides (RGD-GNRs) were used as a sensitizer to enhance the response of melanoma cells to 6 mV radiation. Methods and materials: A375 melanoma cells were treated by gold nanorods or RGD-GNRs with or without irradiation. The antiproliferative impact of the treatments was measured by MTT assay. Radiosensitizing effects were determined by colony formation assay. Apoptosis and cell cycle data were measured by flow cytometry. Integrin alpha(v)beta(3) expression was also investigated by flow cytometry. Results: Addition of RGD-GNRs enhanced the radiosensitivity of A375 cells with a dose-modifying factor of 1.35, and enhanced radiation-induced apoptosis. DNA flow cytometric analysis indicated that RGD-GNRs plus irradiation induced significant G2/M phase arrest in A375 cells. Both spontaneous and radiation-induced expressions of integrin alpha(v)beta(3) were downregulated by RGD-GNRs. Conclusion: Our study indicated that RGD-GNRs could sensitize melanoma A375 cells to irradiation. It was hypothesized that this was mainly through downregulation of radiation-induced alpha(v)beta(3), in addition to induction of a higher proportion of cells within the G2/M phase. The combination of RGD-GNRs and radiation needs further investigation.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000302718200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Nanoscience &amp; NanotechnologyPharmacology &amp; PharmacySCI(E)22ARTICLE915-924

Retinal image synthesis from multiple-landmarks input with generative adversarial networks

Background Medical datasets, especially medical images, are often imbalanced due to the different incidences of various diseases. To address this problem, many methods have been proposed to synthesize medical images using generative adversarial networks (GANs) to enlarge training datasets for facilitating medical image analysis. For instance, conventional methods such as image-to-image translation techniques are used to synthesize fundus images with their respective vessel trees in the field of fundus image. Methods In order to improve the image quality and details of the synthetic images, three key aspects of the pipeline are mainly elaborated: the input mask, architecture of GANs, and the resolution of paired images. We propose a new preprocessing pipeline named multiple-channels-multiple-landmarks (MCML), aiming to synthesize color fundus images from a combination of vessel tree, optic disc, and optic cup images. We compared both single vessel mask input and MCML mask input on two public fundus image datasets (DRIVE and DRISHTI-GS) with different kinds of Pix2pix and Cycle-GAN architectures. A new Pix2pix structure with ResU-net generator is also designed, which has been compared with the other models. Results and conclusion As shown in the results, the proposed MCML method outperforms the single vessel-based methods for each architecture of GANs. Furthermore, we find that our Pix2pix model with ResU-net generator achieves superior PSNR and SSIM performance than the other GANs. High-resolution paired images are also beneficial for improving the performance of each GAN in this work. Finally, a Pix2pix network with ResU-net generator using MCML and high-resolution paired images are able to generate good and realistic fundus images in this work, indicating that our MCML method has great potential in the field of glaucoma computer-aided diagnosis based on fundus image