ENHANCING MASS TRANSPORT ACROSS BIOLOGICAL MEMBRANES

Aharon Azagury

Controlled drug delivery has been the focus of many types of research for decades while noninvasive drug delivery is one of its fundamental aspects. Noninvasive drug delivery routes use external accessible membranes such as the skin, nasal and tympanic cavities, chorioamnion membrane, and oral drug delivery. Since most of these membranes are not naturally permeable, it is thus crucial to enhance the mass transport across them for drug delivery purposes. In order to achieve effective enhancement one first need to understand the structure of these membranes and more importantly the rate limiting step for mass transport. Many approaches have been used throughout the years to enhance mass transport across biological membranes, mainly for the skin. Among these methods are chemical penetration enhancers (CPEs) and ultrasound (US). In this presentation, the effect of US on transdermal mass transport and its mechanism of action would be discussed followed by the effects of US and CPEs (separately and in tandem) on the mass transport across the chorioamnion membrane (CAM). The aim of latter research was to enhance the mass transport across the CAM for early sampling of the amniotic fluid in a non-invasive manner, in order to detect abnormalities and assess the fetus’s health. Another option is to deliver drugs directly to the fetus. In order to achieve that, first CPEs were encapsulated inside nano-PLGA (nPLGA) particles resulting in an increased CPEs effect with lower concentration of CPEs. The mechanisms of actions were determined by experiments evaluation permeability with dyes, cryo-TEM & SEM, FTIR, biomimicking colorimetric screening measurements, and custom made image analysis.
Enhancing mass transport across the gastrointestinal (GI) track is probably the most challenging approach. Nanoencapsulation approaches for oral drug delivery is one of the solutions and investigation of their interactions with the different segments of the GI would be discussed. First, a novel double-walled (DW) method termed single step DW nanoparticles for oral drug delivery was developed in our lab. DW morphology was confirmed via FTIR, DSC, and a unique AFM mode. Second, the interactions between polymeric nanoparticles and the GI membrane were investigated using DLS and Zeta potential measurements, followed by presenting my newly developed method for detection of polymers in biological specimens using FTIR. Lastly, a brief description of a novel method currently under development for calculating effective diffusion coefficient using 3D SEM would be presented.

You are invited to attend a lecture

Meta-Engineering of Light in the Linear, Nonlinear and Quantum Optical Regimes

:By

Nir Shitrit

University of California, Berkeley

Abstract

Through subwavelength structuring of materials, metamaterials have shown exquisite control over electromagnetic properties, experimentally demonstrating phenomena not found in nature such as a negative index of refraction. Metasurfaces constitute a more recent branch of metamaterials research, in which two-dimensional ultrathin arrays of engineered nanostructures mold optical wavefronts at will, aiming to revolutionize optical designs by realizing virtually flat, lightweight optics that replaces bulky optical elements. In this talk, we will detail how photonic meta-engineering has advanced metasurfaces as a new paradigm for linear, nonlinear and quantum optics. First, we demonstrated beam shaping via metasurfaces based on optical nanoantennas for visible light, where the photon spin is utilized as a new degree of freedom. We also used metasurfaces as architecture to reveal the role of surface symmetry properties on light-matter interactions and, more specifically, observed the optical analog of the electronic Rashba effect—that is, spin-split dispersion due to broken inversion symmetry. In the nonlinear regime, we explored functionalities which cannot be fundamentally achieved by linear metasurfaces, and demonstrated two-way asymmetric transport of light at nonlinear metasurfaces, paving the way for ultrathin optical diodes. Finally, we showed the extension of metasurfaces to the quantum optical regime, while theoretically demonstrating that a metasurface can efficiently mediate quantum entanglement between two macroscopically separated quantum emitters at the chip level. To conclude, we propose several future directions of structures and materials by design including non-Markovian metasurfaces, mechanical metamaterials, and innovating optoelectronic devices via metastructures, which address the significant impact of meta-engineering on science and technology.

Bio: Nir Shitrit is a postdoctoral fellow at the University of California, Berkeley. He received his Ph.D. in 2014, in Mechanical Engineering, from the Technion -Israel Institute of Technology. His research interests lie at the interface of engineering and applied physics and focus on optics and photonics and, more specifically, on nano-engineered structures and materials with a custom-designed electromagnetic response.  

On Thursday, January 10, 2019, 15:00

Room 011, Kitot Building

Speaker: Nir Zarrabi-Itzhak

M.Sc. student under the supervision of Prof. Shai Avidan and Prof. Yael Moses

Wednesday, January 2^nd, 2019 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

CrowdCam: Dynamic Region Segmentation
 

Abstract

            We consider the problem of segmenting dynamic regions in CrowdCam images, where a dynamic region is the projection of a moving 3D object on the image plane. Quite often, these regions are the most interesting parts of an image. CrowdCam images is a set of images of the same dynamic event, captured by a group of non-collaborating users. Almost every event of interest today is captured this way. This new type of images raises the need to develop new algorithms tailored specifically for it. We propose an algorithm that segments the dynamic regions in CrowdCam images. The proposed algorithm combines cues that are based on geometry, appearance and proximity. First, geometric reasoning is used to produce rough score maps that determine, for every pixel, how likely it is to be the projection of a static or dynamic scene point. These maps are noisy because CrowdCam images are usually few and far apart both in space and in time. Then, we use similarity in appearance space and proximity in the image plane to encourage neighboring pixels to be labeled similarly as either static or dynamic. We define an objective function that combines all the cues and solves it using an MRF solver. The proposed method was tested on publicly available CrowdCam datasets, as well as a new and challenging dataset we collected. Our results are better than the current state-of-the-art.