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      DNA engineered micromotors powered by metal nanoparticles for motion based cellphone diagnostics

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          Abstract

          HIV-1 infection is a major health threat in both developed and developing countries. The integration of mobile health approaches and bioengineered catalytic motors can allow the development of sensitive and portable technologies for HIV-1 management. Here, we report a platform that integrates cellphone-based optical sensing, loop-mediated isothermal DNA amplification and micromotor motion for molecular detection of HIV-1. The presence of HIV-1 RNA in a sample results in the formation of large-sized amplicons that reduce the motion of motors. The change in the motors motion can be accurately measured using a cellphone system as the biomarker for target nucleic acid detection. The presented platform allows the qualitative detection of HIV-1 (n = 54) with 99.1% specificity and 94.6% sensitivity at a clinically relevant threshold value of 1000 virus particles/ml. The cellphone system has the potential to enable the development of rapid and low-cost diagnostics for viruses and other infectious diseases.

          Abstract

          Micromotors have a range of potential healthcare applications. Here, the authors describe the development of a metal nanoparticle DNA micromotor which can be used to detect human HIV-1 by a change in the motion of the micromotors, monitored by cell phone camera, triggered by binding of HIV-1 RNA.

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          Most cited references52

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          Dermatologist-level classification of skin cancer with deep neural networks

          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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            Loop-mediated isothermal amplification of DNA.

            T. Notomi (2000)
            We have developed a novel method, termed loop-mediated isothermal amplification (LAMP), that amplifies DNA with high specificity, efficiency and rapidity under isothermal conditions. This method employs a DNA polymerase and a set of four specially designed primers that recognize a total of six distinct sequences on the target DNA. An inner primer containing sequences of the sense and antisense strands of the target DNA initiates LAMP. The following strand displacement DNA synthesis primed by an outer primer releases a single-stranded DNA. This serves as template for DNA synthesis primed by the second inner and outer primers that hybridize to the other end of the target, which produces a stem-loop DNA structure. In subsequent LAMP cycling one inner primer hybridizes to the loop on the product and initiates displacement DNA synthesis, yielding the original stem-loop DNA and a new stem-loop DNA with a stem twice as long. The cycling reaction continues with accumulation of 10(9) copies of target in less than an hour. The final products are stem-loop DNAs with several inverted repeats of the target and cauliflower-like structures with multiple loops formed by annealing between alternately inverted repeats of the target in the same strand. Because LAMP recognizes the target by six distinct sequences initially and by four distinct sequences afterwards, it is expected to amplify the target sequence with high selectivity.
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              The use of nanocrystals in biological detection.

              In the coming decade, the ability to sense and detect the state of biological systems and living organisms optically, electrically and magnetically will be radically transformed by developments in materials physics and chemistry. The emerging ability to control the patterns of matter on the nanometer length scale can be expected to lead to entirely new types of biological sensors. These new systems will be capable of sensing at the single-molecule level in living cells, and capable of parallel integration for detection of multiple signals, enabling a diversity of simultaneous experiments, as well as better crosschecks and controls.
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                Author and article information

                Contributors
                hshafiee@bwh.harvard.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                16 October 2018
                16 October 2018
                2018
                : 9
                : 4282
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, 02139 MA USA
                [2 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Medicine, , Harvard Medical School, ; Boston, 02115 MA USA
                [3 ]ISNI 0000 0000 9477 7793, GRID grid.412258.8, Faculty of Science, , Tanta University, ; Tanta, 31527 Egypt
                [4 ]ISNI 000000041936754X, GRID grid.38142.3c, Division of Infectious Diseases, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, 02139 MA USA
                Author information
                http://orcid.org/0000-0002-5499-9302
                http://orcid.org/0000-0002-9809-6690
                http://orcid.org/0000-0003-2240-7648
                Article
                6727
                10.1038/s41467-018-06727-8
                6191441
                30327456
                1b4920b3-bdf3-49b3-95ee-6fd6a9d59925
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 31 January 2018
                : 24 September 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000060, U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID);
                Award ID: R01AI118502
                Award Recipient :
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