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Factors influencing the efficiency of generating genetically engineered pigs by nuclear transfer: multi-factorial analysis of a large data set

Overview of attention for article published in BMC Biotechnology, May 2013
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Title
Factors influencing the efficiency of generating genetically engineered pigs by nuclear transfer: multi-factorial analysis of a large data set
Published in
BMC Biotechnology, May 2013
DOI 10.1186/1472-6750-13-43
Pubmed ID
Authors

Mayuko Kurome, Ludwig Geistlinger, Barbara Kessler, Valeri Zakhartchenko, Nikolai Klymiuk, Annegret Wuensch, Anne Richter, Andrea Baehr, Katrin Kraehe, Katinka Burkhardt, Krzysztof Flisikowski, Tatiana Flisikowska, Claudia Merkl, Martina Landmann, Marina Durkovic, Alexander Tschukes, Simone Kraner, Dirk Schindelhauer, Tobias Petri, Alexander Kind, Hiroshi Nagashima, Angelika Schnieke, Ralf Zimmer, Eckhard Wolf

Abstract

Somatic cell nuclear transfer (SCNT) using genetically engineered donor cells is currently the most widely used strategy to generate tailored pig models for biomedical research. Although this approach facilitates a similar spectrum of genetic modifications as in rodent models, the outcome in terms of live cloned piglets is quite variable. In this study, we aimed at a comprehensive analysis of environmental and experimental factors that are substantially influencing the efficiency of generating genetically engineered pigs. Based on a considerably large data set from 274 SCNT experiments (in total 18,649 reconstructed embryos transferred into 193 recipients), performed over a period of three years, we assessed the relative contribution of season, type of genetic modification, donor cell source, number of cloning rounds, and pre-selection of cloned embryos for early development to the cloning efficiency.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 2%
Unknown 42 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 21%
Researcher 8 19%
Student > Master 5 12%
Student > Doctoral Student 4 9%
Student > Bachelor 2 5%
Other 6 14%
Unknown 9 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 33%
Biochemistry, Genetics and Molecular Biology 8 19%
Veterinary Science and Veterinary Medicine 2 5%
Computer Science 2 5%
Engineering 2 5%
Other 5 12%
Unknown 10 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 May 2013.
All research outputs
#19,292,491
of 23,881,329 outputs
Outputs from BMC Biotechnology
#770
of 937 outputs
Outputs of similar age
#149,409
of 197,798 outputs
Outputs of similar age from BMC Biotechnology
#12
of 16 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 937 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.