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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Computational Model of the Fetal Circulation to Quantify Blood Redistribution in Intrauterine Growth Restriction
|
|---|---|
| Published in |
PLoS Computational Biology, June 2014
|
| DOI | 10.1371/journal.pcbi.1003667 |
| Pubmed ID | |
| Authors |
Patricia Garcia-Canadilla, Paula A. Rudenick, Fatima Crispi, Monica Cruz-Lemini, Georgina Palau, Oscar Camara, Eduard Gratacos, Bart H. Bijens |
| Abstract |
Intrauterine growth restriction (IUGR) due to placental insufficiency is associated with blood flow redistribution in order to maintain delivery of oxygenated blood to the brain. Given that, in the fetus the aortic isthmus (AoI) is a key arterial connection between the cerebral and placental circulations, quantifying AoI blood flow has been proposed to assess this brain sparing effect in clinical practice. While numerous clinical studies have studied this parameter, fundamental understanding of its determinant factors and its quantitative relation with other aspects of haemodynamic remodeling has been limited. Computational models of the cardiovascular circulation have been proposed for exactly this purpose since they allow both for studying the contributions from isolated parameters as well as estimating properties that cannot be directly assessed from clinical measurements. Therefore, a computational model of the fetal circulation was developed, including the key elements related to fetal blood redistribution and using measured cardiac outflow profiles to allow personalization. The model was first calibrated using patient-specific Doppler data from a healthy fetus. Next, in order to understand the contributions of the main parameters determining blood redistribution, AoI and middle cerebral artery (MCA) flow changes were studied by variation of cerebral and peripheral-placental resistances. Finally, to study how this affects an individual fetus, the model was fitted to three IUGR cases with different degrees of severity. In conclusion, the proposed computational model provides a good approximation to assess blood flow changes in the fetal circulation. The results support that while MCA flow is mainly determined by a fall in brain resistance, the AoI is influenced by a balance between increased peripheral-placental and decreased cerebral resistances. Personalizing the model allows for quantifying the balance between cerebral and peripheral-placental remodeling, thus providing potentially novel information to aid clinical follow up. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 50% |
| Spain | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | <1% |
| United Kingdom | 1 | <1% |
| Egypt | 1 | <1% |
| Belgium | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 117 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 20% |
| Researcher | 21 | 17% |
| Student > Master | 20 | 16% |
| Student > Bachelor | 12 | 10% |
| Student > Postgraduate | 11 | 9% |
| Other | 18 | 15% |
| Unknown | 17 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 40 | 33% |
| Engineering | 27 | 22% |
| Agricultural and Biological Sciences | 6 | 5% |
| Computer Science | 5 | 4% |
| Physics and Astronomy | 5 | 4% |
| Other | 12 | 10% |
| Unknown | 28 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 14 June 2014.
All research outputs
#14,915,133
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#6,346
of 8,960 outputs
Outputs of similar age
#120,740
of 243,582 outputs
Outputs of similar age from PLoS Computational Biology
#83
of 142 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 27th percentile – i.e., 27% 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 243,582 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.