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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Metabolic Profiling for Detection of Staphylococcus aureus Infection and Antibiotic Resistance
|
|---|---|
| Published in |
PLOS ONE, February 2013
|
| DOI | 10.1371/journal.pone.0056971 |
| Pubmed ID | |
| Authors |
Henrik Antti, Anna Fahlgren, Elin Näsström, Konstantinos Kouremenos, Jonas Sundén-Cullberg, YongZhi Guo, Thomas Moritz, Hans Wolf-Watz, Anders Johansson, Maria Fallman |
| Abstract |
Due to slow diagnostics, physicians must optimize antibiotic therapies based on clinical evaluation of patients without specific information on causative bacteria. We have investigated metabolomic analysis of blood for the detection of acute bacterial infection and early differentiation between ineffective and effective antibiotic treatment. A vital and timely therapeutic difficulty was thereby addressed: the ability to rapidly detect treatment failures because of antibiotic-resistant bacteria. Methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) were used in vitro and for infecting mice, while natural MSSA infection was studied in humans. Samples of bacterial growth media, the blood of infected mice and of humans were analyzed with combined Gas Chromatography/Mass Spectrometry. Multivariate data analysis was used to reveal the metabolic profiles of infection and the responses to different antibiotic treatments. In vitro experiments resulted in the detection of 256 putative metabolites and mice infection experiments resulted in the detection of 474 putative metabolites. Importantly, ineffective and effective antibiotic treatments were differentiated already two hours after treatment start in both experimental systems. That is, the ineffective treatment of MRSA using cloxacillin and untreated controls produced one metabolic profile while all effective treatment combinations using cloxacillin or vancomycin for MSSA or MRSA produced another profile. For further evaluation of the concept, blood samples of humans admitted to intensive care with severe sepsis were analyzed. One hundred thirty-three putative metabolites differentiated severe MSSA sepsis (n = 6) from severe Escherichia coli sepsis (n = 10) and identified treatment responses over time. Combined analysis of human, in vitro, and mice samples identified 25 metabolites indicative of effective treatment of S. aureus sepsis. Taken together, this study provides a proof of concept of the utility of analyzing metabolite patterns in blood for early differentiation between ineffective and effective antibiotic treatment in acute S. aureus infections. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Netherlands | 1 | <1% |
| Portugal | 1 | <1% |
| Norway | 1 | <1% |
| Unknown | 106 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 24% |
| Student > Ph. D. Student | 21 | 19% |
| Student > Master | 15 | 14% |
| Other | 10 | 9% |
| Student > Bachelor | 9 | 8% |
| Other | 15 | 14% |
| Unknown | 14 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 33 | 30% |
| Medicine and Dentistry | 20 | 18% |
| Immunology and Microbiology | 12 | 11% |
| Biochemistry, Genetics and Molecular Biology | 8 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 6% |
| Other | 13 | 12% |
| Unknown | 17 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 13 August 2019.
All research outputs
#7,480,934
of 25,711,518 outputs
Outputs from PLOS ONE
#104,012
of 224,015 outputs
Outputs of similar age
#58,711
of 206,303 outputs
Outputs of similar age from PLOS ONE
#1,753
of 5,382 outputs
Altmetric has tracked 25,711,518 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 224,015 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. This one has gotten more attention than average, scoring higher than 52% 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 206,303 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 5,382 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.