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Zhong Chen

Bio: Zhong Chen is an academic researcher from King's College London. The author has contributed to research in topics: Cardiac resynchronization therapy & Heart failure. The author has an hindex of 19, co-authored 52 publications receiving 1554 citations. Previous affiliations of Zhong Chen include University of Cambridge & Center for Excellence in Education.


Papers
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Journal ArticleDOI
TL;DR: This study demonstrates that native and post-contrast T1 values provide indexes with high diagnostic accuracy for the discrimination of normal and diffusely diseased myocardium.
Abstract: Objectives: The aim of this study was to examine the value of native and post-contrast T1 relaxation in the differentiation between healthy and diffusely diseased myocardium in 2 model conditions, ...

414 citations

Journal ArticleDOI
TL;DR: A standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images is presented and it is concluded that currently no algorithm is deemed clearly better than others.
Abstract: Background Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging can be used to visualise regions of fibrosis and scarring in the left atrium (LA) myocardium. This can be important for treatment stratification of patients with atrial fibrillation (AF) and for assessment of treatment after radio frequency catheter ablation (RFCA). In this paper we present a standardised evaluation benchmarking framework for algorithms segmenting fibrosis and scar from LGE CMR images. The algorithms reported are the response to an open challenge that was put to the medical imaging community through an ISBI (IEEE International Symposium on Biomedical Imaging) workshop.

161 citations

Journal ArticleDOI
TL;DR: In this article, genes encoding four glycosyltransferases, GtfB, C, D, E, were subcloned from Amycolatopsis orientalis strains that produce chloroeremomycin or vancomycin (GtfD, E) into Escherichia coli.
Abstract: The glycopeptides vancomycin and teicoplanin are clinically important antibiotics. The carbohydrate portions of these molecules affect biological activity, and there is great interest in developing efficient strategies to make carbohydrate derivatives. To this end, genes encoding four glycosyltransferases, GtfB, C, D, E, were subcloned from Amycolatopsis orientalis strains that produce chloroeremomycin (GtfB, C) or vancomycin (GtfD, E) into Escherichia coli. After expression and purification, each glycosyltransferase (Gtf) was characterized for activity either with the aglycones (GtfB, E) or the glucosylated derivatives (GtfC, D) of vancomycin and teicoplanin. GtfB efficiently glucosylates vancomycin aglycone using UDP-glucose as the glycosyl donor to form desvancosaminyl-vancomycin (vancomycin pseudoaglycone), with k(cat) of 17 min(-1), but has very low glucosylation activity, < or = 0.3 min(-1), for an alternate substrate, teicoplanin aglycone. In contrast, GtfE is much more efficient at glucosylating both its natural substrate, vancomycin aglycone (k(cat) = 60 min(-1)), and an unnatural substrate, teicoplanin aglycone (k(cat) = 20 min(-1)). To test the addition of the 4-epi-vancosamine moiety by GtfC and GtfD, synthesis of UDP-beta-L-4-epi-vancosamine was undertaken. This NDP-sugar served as a substrate for both GtfC and GtfD in the presence of vancomycin pseudoaglycone (GtfC and GtfD) or the glucosylated teicoplanin scaffold, 7 (GtfD). The GtfC product was the 4-epi-vancosaminyl form of vancomycin. Remarkably, GtfD was able to utilize both an unnatural acceptor, 7, and an unnatural nucleotide sugar donor, UDP-4-epi-vancosamine, to synthesize a novel hybrid teicoplanin/vancomycin glycopeptide. These results establish the enzymatic activity of these four Gtfs, begin to probe substrate specificity, and illustrate how they can be utilized to make variant sugar forms of both the vancomycin and the teicoplanin class of glycopeptide antibiotics.

139 citations

Journal Article
TL;DR: In this article, genes encoding four glycosyltransferases, GtfB, C, D, E, were subcloned from Amycolatopsis orientalis strains that produce chloroeremomycin or vancomycin (GtfD, E) into Escherichia coli.
Abstract: The glycopeptides vancomycin and teicoplanin are clinically important antibiotics. The carbohydrate portions of these molecules affect biological activity, and there is great interest in developing efficient strategies to make carbohydrate derivatives. To this end, genes encoding four glycosyltransferases, GtfB, C, D, E, were subcloned from Amycolatopsis orientalis strains that produce chloroeremomycin (GtfB, C) or vancomycin (GtfD, E) into Escherichia coli. After expression and purification, each glycosyltransferase (Gtf) was characterized for activity either with the aglycones (GtfB, E) or the glucosylated derivatives (GtfC, D) of vancomycin and teicoplanin. GtfB efficiently glucosylates vancomycin aglycone using UDP-glucose as the glycosyl donor to form desvancosaminyl-vancomycin (vancomycin pseudoaglycone), with k(cat) of 17 min(-1), but has very low glucosylation activity, < or = 0.3 min(-1), for an alternate substrate, teicoplanin aglycone. In contrast, GtfE is much more efficient at glucosylating both its natural substrate, vancomycin aglycone (k(cat) = 60 min(-1)), and an unnatural substrate, teicoplanin aglycone (k(cat) = 20 min(-1)). To test the addition of the 4-epi-vancosamine moiety by GtfC and GtfD, synthesis of UDP-beta-L-4-epi-vancosamine was undertaken. This NDP-sugar served as a substrate for both GtfC and GtfD in the presence of vancomycin pseudoaglycone (GtfC and GtfD) or the glucosylated teicoplanin scaffold, 7 (GtfD). The GtfC product was the 4-epi-vancosaminyl form of vancomycin. Remarkably, GtfD was able to utilize both an unnatural acceptor, 7, and an unnatural nucleotide sugar donor, UDP-4-epi-vancosamine, to synthesize a novel hybrid teicoplanin/vancomycin glycopeptide. These results establish the enzymatic activity of these four Gtfs, begin to probe substrate specificity, and illustrate how they can be utilized to make variant sugar forms of both the vancomycin and the teicoplanin class of glycopeptide antibiotics.

121 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: This 2017 Consensus Statement is to provide a state-of-the-art review of the field of catheter and surgical ablation of AF and to report the findings of a writing group, convened by these five international societies.

1,626 citations

Journal ArticleDOI
TL;DR: Christopher T. Walsh is the Hamilton Kuhn Professor of Biological Chemistry and Molecular Pharmacology (BCMP) at Harvard Medical School and has had extensive experience in academic administration, including Chairmanship of the MIT Chemistry Department and the HMS Biological Chemistry & molecular Pharmacology Department.
Abstract: biotics of the penicillin and cephalosporin families, 3,4 as well as the glycopeptides of the vancomycin family 5 (Figure 1a). * To whom correspondence should be addressed: christopher_walsh@ hms.harvard.edu. † Harvard Medical School. ‡ Harvard University. Christopher T. Walsh is the Hamilton Kuhn Professor of Biological Chemistry and Molecular Pharmacology (BCMP) at Harvard Medical School. He has had extensive experience in academic administration, including Chairmanship of the MIT Chemistry Department (1982−1987) and the HMS Biological Chemistry & Molecular Pharmacology Department (1987−1995) as well as serving as President and CEO of the Dana Farber Cancer Institute (1992−1995). His research has focused on enzymes and enzyme inhibitors, with recent specialization on antibiotics. He and his group have authored over 590 research papers, a text (Enzymatic Reaction Mechanisms), and two books (Antibiotics: Origins, Actions, Resistance and Posttranslational Modification of Proteins: Expanding Nature’s Inventory). He is a member of the National Academy of Sciences, the Institute of Medicine, and the American Philosophical Society.

1,279 citations