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Standardization of platelet releasate products for clinical applications in cell therapy: a mathematical approach

Overview of attention for article published in Journal of Translational Medicine, May 2017
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Title
Standardization of platelet releasate products for clinical applications in cell therapy: a mathematical approach
Published in
Journal of Translational Medicine, May 2017
DOI 10.1186/s12967-017-1210-z
Pubmed ID
Authors

Francesco Agostini, Jerry Polesel, Monica Battiston, Elisabetta Lombardi, Stefania Zanolin, Alessandro Da Ponte, Giuseppe Astori, Cristina Durante, Mario Mazzucato

Abstract

Standardized animal-free components are required for manufacturing cell-based medicinal products. Human platelet concentrates are sources of growth factors for cell expansion but such products are characterized by undesired variability. Pooling together single-donor products improves consistency, but the minimal pool sample size was never determined. Supernatant rich in growth factors (SRGF) derived from n = 44 single-donor platelet-apheresis was obtained by CaCl2 addition. n = 10 growth factor concentrations were measured. The data matrix was analyzed by a novel statistical algorithm programmed to create 500 groups of random data from single-donor SRGF and to repeat this task increasing group statistical sample size from n = 2 to n = 20. Thereafter, in created groups (n = 9500), the software calculated means for each growth factor and, matching groups with the same sample size, the software retrieved the percent coefficient of variation (CV) between calculated means. A 20% CV was defined as threshold. For validation, we assessed the CV of concentrations measured in n = 10 pools manufactured according to algorithm results. Finally, we compared growth rate and differentiation potential of adipose-derived stromal/stem cells (ASC) expanded by separate SRGF pools. Growth factor concentrations in single-donor SRGF were characterized by high variability (mean (pg/ml)-CV); VEGF: 950-81.4; FGF-b: 27-74.6; PDGF-AA: 7883-28.8; PDGF-AB: 107834-32.5; PDGF-BB: 11142-48.4; Endostatin: 305034-16.2; Angiostatin: 197284-32.9; TGF-β1: 68382-53.7; IGF-I: 70876-38.3; EGF: 2411-30.2). In silico performed analysis suggested that pooling n = 16 single-donor SRGF reduced CV below 20%. Concentrations measured in 10 pools of n = 16 single SRGF were not different from mean values measured in single SRGF, but the CV was reduced to or below the threshold. Separate SRGF pools failed to differently affect ASC growth rate (slope pool A = 0.6; R(2) = 0.99; slope pool B = 0.7; R(2) 0.99) or differentiation potential. Results deriving from our algorithm and from validation utilizing real SRGF pools demonstrated that pooling n = 16 single-donor SRGF products can ameliorate variability of final growth factor concentrations. Different pools of n = 16 single donor SRGF displayed consitent capability to modulate growth and differentiation potential of expanded ASC. Increasing the pool size should not further improve product composition.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 36%
Lecturer 2 18%
Student > Ph. D. Student 2 18%
Student > Doctoral Student 1 9%
Student > Master 1 9%
Other 1 9%
Readers by discipline Count As %
Medicine and Dentistry 4 36%
Biochemistry, Genetics and Molecular Biology 2 18%
Social Sciences 1 9%
Agricultural and Biological Sciences 1 9%
Unspecified 1 9%
Other 2 18%

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 19 May 2017.
All research outputs
#6,219,177
of 10,490,471 outputs
Outputs from Journal of Translational Medicine
#1,338
of 2,157 outputs
Outputs of similar age
#147,654
of 263,568 outputs
Outputs of similar age from Journal of Translational Medicine
#55
of 82 outputs
Altmetric has tracked 10,490,471 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,157 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,568 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 82 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.