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Mendeley readers
Attention Score in Context
| Title |
Bridging epidemiology with population genetics in a low incidence MSM-driven HIV-1 subtype B epidemic in Central Europe
|
|---|---|
| Published in |
BMC Infectious Diseases, February 2015
|
| DOI | 10.1186/s12879-015-0802-6 |
| Pubmed ID | |
| Authors |
Maja M Lunar, Anne-Mieke Vandamme, Janez Tomažič, Primož Karner, Tomaž D Vovko, Blaž Pečavar, Gabriele Volčanšek, Mario Poljak, Ana B Abecasis |
| Abstract |
The HIV-1 epidemic in Slovenia, a small Central European country, has some characteristics that make it an ideal model to study HIV-1 transmission. The epidemic is predominantly affecting men who have sex with men infected with subtype B (89% of all patients), has a low prevalence (less than 1/1000) and is growing slowly. The aim of the present study was to analyze in detail the evolutionary history and the determinants of transmission. A total of 223 partial pol gene sequences from therapy naïve individuals were included, representing 52% of all patients newly diagnosed in 13 years (2000-2012) and analyzed together with genetically similar worldwide sequences, selected in a BLAST search. Combined analysis (maximum likelihood and Bayesian) of HIV-1 transmission chains revealed 8 major clusters (n ≥ 10 patients), 1 group of 4 patients, 2 trios and 12 transmission pairs, thus leaving only 43 (19.3%) Slovenian patients infected with subtype B without a local epidemiological link, indicating a predominance of local transmission of HIV-1 infection. Bayesian analysis performed on a full set of sequences estimated several introductions of HIV-1 into Slovenia, with the most recent common ancestor (tMRCA) of the earliest Slovenian cluster dated to the late 1980s, although tMRCAs obtained from separate independent analysis of each cluster showed considerably more recent estimates. These findings indicate inconsistencies in molecular clock estimation, which we further explored. We hypothesize that these inconsistent dating estimates across the tree could be caused by an evolutionary rate acceleration of HIV-1 after entering the Slovenia epidemic that is not taken into account by the molecular clock model. It could be caused by a lower transmission rate in this setting, as demonstrated by the low epidemic growth rate estimated by Bayesian skyline plot analysis. HIV-1 subtype B was introduced into Slovenia at several time points from the late 80s onward. The majority of patients had a local transmission link, indicating a closed HIV community. The observed slower epidemic rate suggests that individuals with a long-lasting infection are the driving force of the epidemic in this region. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 20% |
| Student > Ph. D. Student | 5 | 14% |
| Other | 3 | 9% |
| Student > Doctoral Student | 3 | 9% |
| Student > Bachelor | 3 | 9% |
| Other | 6 | 17% |
| Unknown | 8 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 23% |
| Agricultural and Biological Sciences | 6 | 17% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Psychology | 3 | 9% |
| Nursing and Health Professions | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 8 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 29 March 2015.
All research outputs
#15,288,925
of 23,498,099 outputs
Outputs from BMC Infectious Diseases
#4,219
of 7,839 outputs
Outputs of similar age
#222,132
of 389,035 outputs
Outputs of similar age from BMC Infectious Diseases
#79
of 154 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,839 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one is in the 41st percentile – i.e., 41% 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 389,035 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 154 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.