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Ashish Garg

Researcher at Apple Inc.

Publications -  28
Citations -  775

Ashish Garg is an academic researcher from Apple Inc.. The author has contributed to research in topics: Liposome & Land cover. The author has an hindex of 13, co-authored 28 publications receiving 738 citations. Previous affiliations of Ashish Garg include University of Minnesota & Microsoft.

Papers
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Journal ArticleDOI

Self-assembly and applications of biomimetic and bioactive peptide-amphiphiles

TL;DR: Several examples where peptide-amphiphiles have found applications as soft bioactive materials for model studies of bioadhesion and characterization of different cellular phenomena, as well as scaffolds for tissue engineering, regenerative medicine, and targeted drug delivery are described.
Journal ArticleDOI

Targeting colon cancer cells using PEGylated liposomes modified with a fibronectin-mimetic peptide.

TL;DR: Stealth liposomes are designed that will target colon cancer cells that express the integrin alpha(5)beta(1) and are as cytotoxic as free 5-Fluorouracil (5-FU) and exert the highest cytotoxicity on CT26.
Journal ArticleDOI

PR_b-targeted PEGylated liposomes for prostate cancer therapy.

TL;DR: In this article, the authors used fibronectin-mimetic peptide (PR_b) to construct a stealth liposome that can bind to α5β1-expressing LNCaP human prostate cancer cells and efficiently release the encapsulated load.
Journal ArticleDOI

pH-Sensitive PEGylated Liposomes Functionalized With a Fibronectin-Mimetic Peptide Show Enhanced Intracellular Delivery to Colon Cancer Cells

TL;DR: The goal of this study is to bring bio-functionality to pH-sensitive PEGylated liposomes in order to facilitate their potential use as a targeted drug delivery agent and demonstrate that PR_b-functionalized pH- sensitive targeted delivery systems have the potential to deliver a payload directly to cancer cells in an efficient and specific manner.
Patent

Application integration with a digital assistant

TL;DR: In this paper, a system and processes for application integration with a digital assistant are provided, where the intent object and the parameter are derived from the natural language user input, and the method further includes identifying a software application associated with the intent objects of the set of intent objects.