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Alteration of skeletal muscle energy metabolism assessed by 31P MRS in clinical routine: Part 2. Clinical application

Overview of attention for article published in NMR in Biomedicine, October 2023
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#36 of 1,995)
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

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28 X users

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1 Dimensions

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Title
Alteration of skeletal muscle energy metabolism assessed by 31P MRS in clinical routine: Part 2. Clinical application
Published in
NMR in Biomedicine, October 2023
DOI 10.1002/nbm.5031
Pubmed ID
Authors

Antoine Naëgel, Hélène Ratiney, Jabrane Karkouri, Djahid Kennouche, Nicolas Royer, Jill M. Slade, Jérôme Morel, Pierre Croisille, Magalie Viallon

Abstract

In this second part of a two-part paper, we intend to demonstrate the impact of the previously proposed advanced quality control pipeline. To understand its benefit and challenge the proposed methodology in a real scenario, we chose to compare the outcome when applying it to the analysis of two patient populations with significant but highly different types of fatigue: COVID-19 and multiple sclerosis (MS). 31 P-MRS was performed on a 3 T clinical MRI, in 19 COVID-19 patients, 38 MS patients, and 40 matched healthy controls. Dynamic acquisitions using an MR-compatible ergometer ran over a rest (40 s), exercise (2 min), and a recovery phase (6 min). Long and short TR acquisitions were also made at rest for T1 correction. The advanced data quality control pipeline presented in Part 1 is applied to the selected patient cohorts to investigate its impact on clinical outcomes. We first used power and sample size analysis to estimate objectively the impact of adding the quality control score (QCS). Then, comparisons between patients and healthy control groups using the validated QCS were performed using unpaired t tests or Mann-Whitney tests (p < 0.05). The application of the QCS resulted in increased statistical power, changed the values of several outcome measures, and reduced variability (standard deviation). A significant difference was found between the T1PCr and T1Pi values of MS patients and healthy controls. Furthermore, the use of a fixed correction factor led to systematically higher estimated concentrations of PCr and Pi than when using individually corrected factors. We observed significant differences between the two patient populations and healthy controls for resting [PCr]-MS only, [Pi ], [ADP], [H2 PO4 - ], and pH-COVID-19 only, and post-exercise [PCr], [Pi ], and [H2 PO4 - ]-MS only. The dynamic indicators τPCr , τPi , ViPCr , and Vmax were reduced for COVID-19 and MS patients compared with controls. Our results show that QCS in dynamic 31 P-MRS studies results in smaller data variability and therefore impacts study sample size and power. Although QCS resulted in discarded data and therefore reduced the acceptable data and subject numbers, this rigorous and unbiased approach allowed for proper assessment of muscle metabolites and metabolism in patient populations. The outcomes include an increased metabolite T1 , which directly affects the T1 correction factor applied to the amplitudes of the metabolite, and a prolonged τPCr , indicating reduced muscle oxidative capacity for patients with MS and COVID-19.

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X Demographics

The data shown below were collected from the profiles of 28 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 20%
Student > Master 1 20%
Unknown 3 60%
Readers by discipline Count As %
Environmental Science 1 20%
Unknown 4 80%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 11 October 2023.
All research outputs
#2,243,359
of 26,760,626 outputs
Outputs from NMR in Biomedicine
#36
of 1,995 outputs
Outputs of similar age
#38,272
of 371,856 outputs
Outputs of similar age from NMR in Biomedicine
#2
of 35 outputs
Altmetric has tracked 26,760,626 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,995 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 98% of its peers.
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 371,856 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.