6,062 research outputs found

    Emerging Functions of Regulatory T Cells in Tissue Homeostasis

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    CD4+Foxp3+ regulatory T-cells (Tregs) are a unique subset of helper T-cells, which regulate immune response and establish peripheral tolerance. Tregs not only maintain the tone and tenor of an immune response by dominant tolerance but, in recent years, have also been identified as key players in resolving tissue inflammation and as mediators of tissue healing. Apart from being diverse in their origin (thymic and peripheral) and location (lymphoid and tissue resident), Tregs are also phenotypically heterogeneous as per the orientation of ongoing immune response. In this review, we discuss the recent advances in the field of Treg biology in general, and non-lymphoid and tissue-resident Tregs in particular. We elaborate upon well-known visceral adipose tissue, colon, skin, and tumor-infiltrating Tregs and newly identified tissue Treg populations as in lungs, skeletal muscle, placenta, and other tissues. Our attempt is to differentiate Tregs based on distinctive properties of their location, origin, ligand specificity, chemotaxis, and specific suppressive mechanisms. Despite ever expanding roles in maintaining systemic homeostasis, Tregs are employed by large varieties of tumors to dampen antitumor immunity. Thus, a comprehensive understanding of Treg biology in the context of inflammation can be instrumental in effectively managing tissue transplantation, autoimmunity, and antitumor immune responses. © 2018 Sharma and Rudra1

    Reading: Amit Majmudar

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    Because of COVID-19 this event is canceled. Amit Majmudar, a multi-genre author and translator, offers a Sacred Arts Festival reading that explores the concept of Building Bridges. Co-sponsored by the Department of English and the Sacred Arts Festival

    Regulatory T cells as therapeutic targets and mediators

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    With the advent of the concept of dominant tolerance and the subsequent discovery of CD4þ regulatory T cells expressing the transcription factor FOXP3 (Tregs), almost all productive as well as nonproductive immune responses can be compartmentalized to a binary of immune effector T cells and immune regulatory Treg populations. A beneficial immune response warrants the timely regulation by Tregs, whereas a nonproductive immune response indicates insufficient effector functions or an outright failure of tolerance. There are ample reports supporting role of Tregs in suppressing spontaneous auto-immune diseases as well as promoting immune evasion by cancers. To top up their importance, several non-immune functions like tissue homeostasis and regeneration are also being attributed to Tregs. Hence, after being in the center stage of basic and translational immunological research, Tregs are making the next jump towards clinical studies. Therefore, newer small molecules, biologics as well as adoptive cell therapy (ACT) approaches are being tested to augment or undermine Treg responses in the context of autoimmunity and cancer. In this brief review, we present the strategies to modulate Tregs towards a favorable clinical outcome. c. 2019 Taylor & Francis Group, LLC11sciescopu

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    sj-docx-1-pie-10.1177_09544089231221541 - Supplemental material for Experimental investigation in sustainable electric discharge drilling of Inconel alloy using grey-based fuzzy logic approach

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    Supplemental material, sj-docx-1-pie-10.1177_09544089231221541 for Experimental investigation in sustainable electric discharge drilling of Inconel alloy using grey-based fuzzy logic approach by Tasnim Arif and Amit Sharma in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p

    A Novel Model for Visual Content Based Image Retrieval using Transfer Learning

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    Abstract: At present, the revolution brought by deep learning based technologies in the field of computer vision gaining momentum in the world of artificial intelligence. In particular, the best models for retrieving common images today are based on features generated by deep convolutional neural networks (DCNNs). However, this great success was expensive. A comprehensive amount of tagged data had to be collected, followed by model design and training. Meanwhile, a transfer-of-learning approach has been developed that avoids this costly step by applying a sophisticated, pre-trained generic DCNN model to completely different data domains. With the use of transfer learning, it becomes possible to use deep CNN models for small datasets with better retrieval performance with respect to handcrafted feature based retrieval methods. In this paper a deep CNN based model has been proposed which uses concept of transfer learning and achieves good classification accuracy.Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved
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