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Official websites use. Share sensitive information only on official, secure websites. Stoletov Blvd, Sofia, Bulgaria. In this study in Bulgaria, we investigate the origin and spatiotemporal evolutionary history of HIV-1 infections in PWIDs and the distribution of antiretroviral resistance mutations and hepatitis co-infections in these populations. Of these, 50 The overall prevalence of resistance mutations was 6. Our study provides valuable molecular epidemiological information on the introduction and distribution of the main HIV-1 subtypes, resistance mutations and hepatitis co-infections among PWIDs with HIV-1 in Bulgaria which can be used to target prevention efforts. While this trend has remained relatively stable over time, transmission from and within PWIDs is rapidly expanding in some East European countries, including Ukraine and Russia, and a number of outbreaks among PWIDs have been reported in Greece and Romania, both of which border Bulgaria Paraskevis et al. Since the early s, drug abuse and drug trafficking have emerged as visible and serious socioeconomic and public health problems in Bulgaria EMCDDA, a , b. Concomitantly, a high prevalence of other blood-borne infections, including HCV and to a lesser extent HBV, were also seen in this population Vassilev et al. This finding contrasts with the early epidemic in Bulgaria that predominated in heterosexual groups Alexiev et al. Nonetheless, little is known about how and where HIV-1 originated in PWIDs or if those groups will be bridges for resurgence of the epidemic in other risk groups as has been reported in Eastern Europe Bozicevic et al. We performed molecular epidemiological and phylogeographic analyses using polymerase pol sequences from PWIDs and other risk groups to infer the spatiotemporal evolutionary history of the HIV-1 epidemic in Bulgaria. HIV-1 diversity was studied in patients from this group for whom plasma samples were available, some of whom have been described in earlier studies Alexiev et al. Of these, 99 PWIDs Patients were from 11 regions, including Sofia and Plovdiv where most of the PWIDs were found and 9 other cities from various regions of the country Fig. Specimens and sequences were linked to demographic and clinical data through an anonymous numerical code in accordance with the ethical standards of the National Centre of Infectious and Parasitic Diseases, Sofia, Bulgaria. Pie charts show subtype distribution within Sofia and Plovdiv. Numbers of each subtype are in parentheses for the smaller towns. Nucleotide substitution model selection and alignments for phylogenetic analyses were performed using MEGA6 Tamura et al. All drug resistance mutation codons were manually removed from the alignment to exclude the possibility of convergent evolution at these sites. Statistical support for the inferred Bayesian trees was assessed by posterior probabilities. For the Bayesian phylogenetic analyses, an uncorrelated lognormal relaxed molecular clock model was used and each run consisted of two independent million Markov chain Monte Carlo MCMC generations with sampling every 10,th generation and a constant coalescent tree prior. The tree with the maximum product of the posterior clade probabilities maximum clade credibility MCC tree was chosen from the posterior distribution of sampled trees after burning in the first sampled trees with the program TreeAnnotator version 1. The inferred tree was edited and displayed using FigTree v1. The skyline analysis was run with 50— million MCMC chains with sampling every 10,th generation until convergence was reached in Tracer. A relaxed molecular clock and time aware smoothing of the GMRF coalescent and a Bayesian stochastic search variable selection diffusion model with a symmetric substitution of transition rates between locations were used to simultaneously infer the discrete phylogeography and TMRCAs. Two MCMC chains of million iterations each were run for each subtype dataset until convergence was reached as assessed in Tracer and both log and tree files were then combined using LogCombiner. Spatial reconstruction of the inferred evolutionary dynamics were visualized, and support for diffusion rates and locations were inferred using Bayes factors BF in the program SPREAD v1. Two-tailed tests were used to determine the significance of the comparisons using the software Graph Pad Prism v5. New pol sequences generated in this study have the accession numbers KT - KT As shown in Table 1 , Individual ages of PWIDs varied between 17 and 43 years, with a mean of The proportion of young patients below 19 years old was significantly higher among PWIDs as compared to other risk groups PWIDs were concentrated in the major cities of Plovdiv and Sofia, with a higher prevalence in Plovdiv, as compared to other risk groups The vast majority of PWIDs We observed a similar trend in incarcerated persons Resistance mutations to protease inhibitors PIs were not found in this group. Drug resistance mutations present in HIVinfected persons who inject drugs in Bulgaria. Drug resitance results for patients with codes in bold italics were reported previously Alexiev et al. Of the pol gene sequences, 50 Comparison of Roma and non-Roma patients showed a relatively uniform distribution of the main subtypes with no significant differences observed Table 3. We then performed phylogenetic analysis using maximum likelihood ML to determine their genetic relationships and to identify closely related global pol sequences for further analysis. In both ML trees Figs. In contrast, most Bulgarian HET sequences were independently dispersed in the trees or were grouped in small and distant clusters. Confidence values of tree branches and clusters were assessed by using the Shimodaira-Hasegawa test and are given as probabilities. Black and blue branches indicate globally distributed and Bulgarian pol sequences, respectively. Inferred effective population sizes Ne over time in years are on the y- and x-axes, respectively. Analysis included 70 polymerase pol sequences from Bulgaria, 41 sequences from persons who inject drugs PWIDs and 29 heterosexual HET individuals and 56 pol sequences from other countries. Time scale in years provided on x-axis and country or city origin of the sequences provided in color and defined in key. Analysis included 69 polymerase pol sequences from Bulgaria, 50 sequences from persons who inject drugs PWIDs , 19 heterosexual HET individuals and 22 pol sequences from other countries. Risk factor information was not reported at GenBank for these two sequences to allow inference of behavioral or epidemiological linkage to the Bulgarian PWIDs. Similar to our previous study in Bulgaria Vassilev et al. As previously reported, the early HIV-1 epidemic in Bulgaria was driven mostly by heterosexual transmission with PWIDs composing only a small fraction of infections Salemi et al. As a population at increased risk to blood-borne infections, PWIDs can potentially form a large social and transmission network allowing rapid spread of infectious agents to other PWIDs, as well as to their sexual partners. In addition, we found that a significant number of PWIDs were Roma individuals or were persons with a history of incarceration. These characteristics of the PWID populations in Bulgaria confirm those reported earlier that also had a higher proportion of vulnerable groups, including youths and minority groups with high-risk behaviors. Kelly et al. Additional concerns arise from the fact that resistance mutations found in most at-risk populations can result in accelerated dissemination of resistant strains within the risk group, as it was recently reported for the role of transmission clusters of MSM with HIV-1 in Croatia Grgic et al. As in our previous reports Salemi et al. It is interesting to note that both CRFs were divided between the two major cities, Sofia and Plovdiv, demonstrating the existence of independent PWID sub-epidemics in two distinct geographical areas within Bulgaria. In addition, almost all URFs found in PWIDs were found predominantly in a subgroup of incarcerated individuals suggesting possible superinfections of these persons with different HIV-1 strains which will require further study. Our analysis suggested that this subtype in PWIDs may have originated in Germany, where the majority of infections are in MSM, though the risk group for these two German HIV-1 sequences basal to those from Bulgaria was not provided in that report Frentz et al. The identification of hepatitis and HIV coinfections is important and will help in the concerted public health response of care and treatment management of PWIDs. Furthermore, screening for HCV could be utilized to identify populations at increased risk for HIV-1 infection to provide targeted interventions to prevent these dual infections and rapid spread of HIV Given the additional risks associated with the management of HIV-1 infection as well as the high potential for transmission of these pathogens in PWIDs, our findings highlight the need for additional public health strategies for preventing the spread of these pathogens. Increasing access to care and treatment and needle exchange programs have been successful in this regard Frieden et al. Indeed, our molecular analyses initiated a concerted public health response using specific prevention interventions targeting PWIDs and their heterosexual partners and has been successful in reducing HIV infection in PWIDs and their contacts. Our epidemiological data also revealed that PWIDs with HIV-1 in Bulgaria are often observed in vulnerable populations, including Roma and those reporting incarceration. These vulnerable groups may require specialized public health intervention strategies. While we highlight important outcomes of our study, it is not without limitations. We were also unable to test all persons in the study for hepatitis viruses as sample volumes for some persons were not sufficient for this testing. The effects of these possible sampling biases in our analyses is unknown but may influence genotype introduction and distribution over time as well as patterns of resistance mutations and prevalences of hepatitis co-infections. In addition, any potential association of infection with demographic and epidemiological characteristics is based on self-reporting and may be affected by recall or other biases. Further, our phylogeographic studies are limited to those sequences deposited in public databases and thus the global spatiotemporal spread of HIV-1 could change if additional sequences become available. Our study provides valuable molecular epidemiological information on HIVinfected PWIDs in Bulgaria and highlight the importance of sustained molecular surveillance of high-risk groups to better understand and control the changing epidemic in Bulgaria. Use of trade names is for identification only and does not imply endorsement by the U. As a library, NLM provides access to scientific literature. Infect Genet Evol. Published in final edited form as: Infect Genet Evol. Find articles by Ivailo Alexiev. Find articles by Anupama Shankar. Find articles by Reneta Dimitrova. Find articles by Anna Gancheva. Find articles by Asia Kostadinova. Find articles by Pavel Teoharov. Find articles by Elitsa Golkocheva. Find articles by Maria Nikolova. Find articles by Mariya Muhtarova. Find articles by Ivaylo Elenkov. Find articles by Mariyana Stoycheva. Find articles by Daniela Nikolova. Find articles by Tonka Varleva. Find articles by William M Switzer. Issue date Dec. PMC Copyright notice. The publisher's version of this article is available at Infect Genet Evol. Open in a new tab. Demographic and epidemiological characteristics of the Bulgarian study population. HIV-1 subtype diversity in persons who inject drugs in Bulgaria. Transparency declaration The authors declare no conflicts of interest. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Bulgarian citizens infected in Bulgaria not Roma, not prisoner. URFs b.
Patient compliance. 2. Long-term care. 3. Drug therapy – utilization. 4. Chronic disease – therapy. 5. Health behavior. 6.
How can I buy cocaine online in Plovdiv
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