The World Health Organization (WHO) study shows that HIV-1 denoted as Human Immunodeficiency Virus, is the most serious world healthcare challenge, with approximately 38 million individuals being affected. Wei, Duffy, and Allison, (2018) reveal that different viruses and strains of this infectious disease with recurrent conditions occur in most parts of Europe and middle Asia. Ramusuran, (2019) denotes that in Europe, Subtype C of HIV -1 is most predominant, with a 46% overall rate. The HIV 1 Subtype C has risen to be the dominant type in many European countries and is virulent more than other strains. The HIV-1 Subtype C is common in countries of Europe such as; Luxembourg, Monaco, Netherlands, France, Austria, and Liechtenstein. The evolution of the Subtype C virus is dependent on genetic variations between virus subtypes among the infected populations in Europe (Von, Boni and Shah et al., 2011). The increasing cases of HIV-1 are a result of the regeneration of high and risky populations in periods of socio-political and economic changes in most parts of Europe.
Notably, the HIV-1 Subtype C comprises a larger population than other strains and variants. The prevalence of this strain is due to the phenotypic variations between Subtype C and different viral strains permitting increased infection rates in the continent (Sen, Kaminski and Kurachi et al., 2016). This strain’s transmission, pathogenicity, and efficiency depend on socio-demographic trends in various parts of the continent. Bimewies, Roberts and Kerstenetai et al., (2018) assert that it is significant to analyze how Subtype C eliminates other variants in this continent. To understand the evolutionary aspects of escalating Subtype C infection rate in the continent, there is a need to evaluate the mechanisms of this variant pathogenesis to less dominant variants considering the phenotypic and genetic characteristics (Sen,Kaminski and Kurachi et al., 2016).
HIV-1 virus is categorized into four main parts, namely: Main (M), Outlier (O), Non-M (N), and group P viruses. Zanini, Brodin and Thebo et al., (2015) assert that these groups evolve from the independent transmission of cross-species occurrence in human beings and Non-Human Primates (NHP). This research article will significantly dwell on group M. This group contains ten subtysubtypes of equal importance and measure: A, B, C, D, E, F, G, H, I, J, and K (Ramusuran, 2019). There are also numerous circulating recombinant forms (CRFs) with diversity sequences ranging from 20 to 35% depending on the genetic region evaluated and subtypes categorized. The HIV-1 Subtype C is primarily predominant in low-income areas of Eastern Europe, Southern Africa, Ethiopia, and Eastern Africa. These regions commonly bear the burdens and effects of HIV-1 Subtype C, with most adults affected hence affecting marriages among couples.
Fig.1; The HIV structure (Spivak and Planelles 2018)
Most phylogenic data reveal that Group M of HIV -1 originated from Kinshasa in the Democratic Republic of Congo from 1909 to 1930. Taylor, McCutchman, and Hammar et al., (2020) assert that the evolution of this strain of Group M has been predominant in most parts of the world. The originality of Subtype C HIV was in Mbuji-Mayi, a city in the DRC. The dense population of this city escalated the spread of this variant globally since most immigrants from neighboring countries work in these diamond mines. This signifies that this city was a central high-risk area for spreading HIV variants globally. Tokarev, Sivro and Omole et al., (2020) reveal that original strains of HIV-1 were introduced to neighboring countries such as Kenya, Uganda, Zimbabwe, and Namibia by the workers coming from DRC diamond mines. This evolution of this disease coincided with timelines of the socio-political changes within South Africa. Vercauteren, Wensing and Balotta et al, (2009) asserts that this Migration in and out of South Africa established to increase trade and commerce escalated the infection rates of HIV-1 due to high fertility rates, facilitating the exponential growth of the Subtype C variant in the late 1980s and mid-1990s. Most studies reveal that the socio-demographic patterns of Southern Africa influenced the prevalence of HIV-1 in those regions, with South Africa mostly affected.
Fig3. The HIV-1 Replication cycle (Ramusuran 2019)
This article significantly analyses and evaluates the efficiency of replication, pathogenesis mechanisms, and efficiency of disease progression and transmission in understanding the escalating expansion of this variant more than others in the continent (Sen, Kaminski and Kurachi et al 2016). Phylogenetic data reveals that Southern Africa has more viral load than any other country, with 98% of most infections caused by Subtype C. However, the Subtype C is increasing in most parts of Europe hence being a primary global health concern (Spivak and Planelles, 2018). This research paper focuses on Subtype C, which is increasingly predominant within Europe with significant concerns on married couples in middle and low-income areas.
Figure 2: Evolution of HIV-1 Subtype C Prevalence (McBrien, Kumar and Silvestri et al. 2018)
The application of Molecular epidemiology is the most efficient tool for establishing sensitive diagnostic procedures for managing individual infections through evaluating transmission patterns, and the evolution of the variants (Parken, Sammons and Maze et al., 2016). Molecular epidemiology study in Europe is at the early stages of development and has not contributed to establishing policy and programs.
Recent analysis and evaluation of the epidemiology of Subtype C in Europe reveal the rising prevalence of this variant among the population. Sen Kaminskiand Kurachi et al., (2016) reveals that the spread of this variant is slowly increasing among the middle and low-income people in the continent. Countries such as Norway, Denmark, Sweden, Greece, and France had increased prevalence rates of this variant more than other counterparts in Europe (Tokarev, Sivro and Omole et al., 2020). However, the escalating epidemic rate in risky populations consists of injecting drug users (IDU), and female commercial sex workers in these countries, males having sexual intercourse with males (MSM). HIV-1 Exposures between individuals linked to traveling abroad also prompt the spread of this strain pattern on the continent.
The major objective of this research project is to evaluate the evolution of HIV-1 Subtype C in Europe from the years 1995-to 2000 and 2019-to 2021. The following are the study sub-objective:
This research aims to evaluate and review the increase of the HIV-1 Subtype C in Europe, with significant emphasis being between 1995-2000 and 2019-to 2021(McBrien, Kumar and Silvestri et al., 2018). This research paper considers and analyzes the prevalence and prevailing rates of HIV-1 Subtype C variant infections. Notably, it reviews the existential evidence of the HIV -1 pattern in these periods and understands the transmission patterns in the continent.
This research is a component of a systematic review of data patterns complimented by an established significant focus on the published HIV-1 Subtype C molecular epidemiology p-published literature in Europe. The research entailed most countries in Europe with most focus on the middle and low-income populations. Parrish, Gao and Zajic et al., (2013) denote that due to the geographical similarity and proximity in the continent and the demographic trends of population patterns of the socio-cultural context, the study included the Middle East and Egypt.
Patient: The significant source of data information in this research was the European Epidemiology Synthesis Project, which was obligated to harness data on sexually transmitted infections (STI), HIV, and sexually transmitted diseases in Europe. Major investigations were conducted through the spread programs from September 1995 to 2000 and from to 2019-2021 for the transmitted drug-resistant (TDR) project (Huang, Postow and Manne et al., 2017). This research was conducted in all countries with 2576 patients with newly diagnosed infection of HIV-1 Subtype C being interviewed (Colby et al., 2018). The observable importance the of SPREAD program is to analyze and evaluate the TDR in European countries. This enabled the study of temporal patterns and assessed the possible predictors for TDR.
Kaslow, Tang and Dorak et al., denote that in conducting interviews, the overall prevalence TDR rate was 10.2% (300 of 2576 patients; 94% confidence interval (C.I) 7.6% -9.7% interval), frequency of nucleoside reverse- transcriptase inhibitor (NRTI) resistance was 5.7% (204 of 2576 patient; 96% CI, 4.1 % – 5.2 %), the prevalence of no nucleoside reverse-transcriptase inhibitor (NNRTI) resistance was 4%. Of the 101 of 2576 patients, 93% C.I., 3.4 %-5.4%, protein inhibitor (P.I.) resistance was 2.9% (Hui, Cheung and Taylor et al., 2017). The overall TDR, NRTI, and NNRTI resistance were not in the time trend of the research study; however, statistically, there was a reduction in P.I. resistance (p =.03) and a significant increase in NNRTI resistance after initial resistance (p=.05).
The study recognized the stabilization of TDR in Europe is consistent with decreased resistance to drugs hence high viral suppression of HIV-1 Subtype C individuals. Bennet, Otelea and Feury et al., 2009 assert that, despite the successful treatment of HIV-1, a few groups of patients have failed in treatment and adherence procedures and are at high risk of spreading this variant in the continent.
Methods: Behavioral, clinical, and epidemiological data were collected through standardized questionnaires and phylogenetic analysis. Leyre, Kroon and Colby et al., (2020) state that the phylogenetic analysis is significant in evaluating and analyzing the HIV-1 transmission networks. It effectively evaluates transmitted drug resistance cases and public health transmission initiatives. This procedure involves the application of standardized characterization of laboratory tests using probability and quality assurance tests (Joseph, Swanstrom and Kashuba et al., 2015). The main objective of phylogenetic analysis is to evaluate the evolutionary forms of HIV-1 Subtype C in Europe. Three methods of phylogenetic analysis are; distance, maximum parsimony, and maximum likelihood.
Markowitz, Mendo and Gotuzzo et al., (2007) state that the three modes of phylogenetic analysis are commonly used in analyzing evolutionary trees conforming to the evolution trends of HIV-1 Subtype C in Europe through observation of variation of group sequences. These methods require different standardized applications while researching HIV-1. Microbiology programs and laboratory tests commonly apply the distance method due to its reliability and efficiency (Margolis, Garcia and Hazuda et al., 2016). The reliability of the distance procedure enables the application of an extensive sequence of tests or units within short periods. The maximum likelihood methods are always challenging since they require a deep understanding of evolutionary trends of infections in various geographical locations. Dufour, Gantner and Fromentin et al., (2021) asseassertt maximum likelihood methods require many computational procedures dramatically due to the rapid increase of sequencing of multiple variants.
Most procedures of maximum likelihood computational methods are characterized by smaller ranges of periodical sequences used in establishing the transmission of variants in human cells.
The maximum likelihood methods require computer application programs to determine and analyze many variant sequencings in HIV-1 Subtype C. Huang, Postow and Manne et al., 2017 state that maximum parsimony involves a character-based approach linking a phylogenetic tree by reducing the number of evolutionary procedures differentiating the values of data tasked on the leaves.
Phylogenic tree. [online image] Available from: https://www.ebi.ac.uk/Tools/services/web/toolresult.ebi?jobId=clustalo-E20220428-152744-0049-50994844-p2m&analysis=phylotree[28/04/2022]
Phylogenetic procedures rely significantly on DNA profiling technology due to their success in linking genetic transmission among patients with HIV-1 Subtype C in Europe (Bennet, otelea and Fleury et al., 2009). It is significant since human genomes remain constant throughout the lifetime of individuals. DNA profiling technology is challenging to incubate subspecies of constantly evolving strains in HIV-1 patients; however, variants and strains can be easily predicted through the maximum likelihood methods (Huang, Postow and Manne et al., 2017). Phylogenetic evaluation inhibits the molecular epidemiology of viruses and strains, thus easing contact tracing of patients’ naïve to Antiretroviral Drug (ARVs) adherence.
Charpentiev, Rueller and Kaiser et al., (2013) assert that through phylogenetic inferences, DNA profiling technology effectively draws specific conclusions on the epidemiological bases in patients affected by Subtype C patients, thus aiding local control programs of virus transmission. DNA profiling technology enables forensic investigations and techniques to analyze the links between patients’ Subtype B and C strains. This link analysis facilitates the evaluation of drug compatibility among patients with different strains of the virus.
DNA profiling technology reveals the transmission period, the transmission paths, and intermediary links of different strains of HIV-1. Garcia, Maddali and Jiang et al., (2019) assert that involvement of the timing of infections facilitates predictions of the transmission occurrence of different strains through genetic analysis. It is easier to identify the transmission and window periods among patients through DNA profiling technology. Various research methods on HIV-1 use phylogenetic procedures in determining transmission rates and epidemiology factors among patients. The expansion of DNA profiling technology globally has facilitated the standardization of laboratory tests in determining various strains affecting population groups in Europe.
A thorough and critical analysis of phylogenetic evaluations through DNA profiling technology was conducted on various groups of HIV-1 Subtype C patients in Europe (Johnson, Wei, and Craig et al., 2009). The distance, parsimony, and maximum likelihood methods were used to collect and analyze the facts of the data. The measure of infectious capacity and CD4 cell counts were sampled from patients between 1995-2000 and 2019-2021. Leyre et al., (2020) state that genotypic resistance testing and analysis were conducted on blood samples six months after diagnosis. The population integrated nucleotide sequencing was performed through genetic PCR in local and international laboratories. Hue, Dunn and Dolling et al., (2014) reveal that the reverse transcriptase (R.T.) and protease (P.R.) gene sequencing were conducted through Rapid Diagnostic Kits (RDTs), microscopy, and in-house procedures. Sequencing through GenBank, accession of digits was listed after each patient test.
The genetic sequencing methods used in establishing phenotypic trees help trace and reconstruct the evolutionary history of viral transmission paths. McElrath, DeRosa and Janes et al., (2008) denote that it is significant to note that the timing of the donor and recipient of the HIV-1 virus through DNA profiling technology should recur with the transmission periods. The timing of the viral transmission is significant in evaluating the time node of predating transmissions among individuals. This study noted that the parental viruses were preferentially transmitted, hence establishing inconsistencies linking the transmission timelines in patients’ genes (Ndlovu, Kazer and Nkosi et al., 2019). The phenotypic evaluation through DNA profiling technology demonstrated that the molecular clock model and increased persistence of HIV-1 Subtype C strain should be associated with similar source-recipient of lineages of patients.
Johnson Wei and Craig et al., (2008) state that the findings reveal that projected timing nodes in the phylogenetic trees facilitated the estimation of window transmission periods among the patients with HIV-1 Subtype C variants. The significant inconsistencies linking the timing of infection and deriving independent estimates show that patients infected at the beginning and the end of transmission indicate molecular clock values in the transmission rates in the phylogenetic trees. Ambiguity, avidity counting, and molecular multi-assay algorithms efficiently estimate the window periods of infection through DNA profiling technology Ndlovu, Kazer and Nkosi et al., (2019). These counting and algorithms facilitate quality performance in patients’ maximum likelihood mapping.
It is easier to conduct epidemiological information and analysis in contact tracing of many patients in Europe through phylogenetic trees. Ondondo, Murakoshi and Wee et al., (2008) declare that most epidemiological investigations establish information required in tracing other potential sources of infections among the population. Phylogenetic evidence through DNA profiling technology reveals the accuracy of testing the HIV-1 monophyletic strains in patients. Notably, in cases where data is insufficient, there is a need for a phylogenetic expert to conclude depending on the findings of the transmission periods of transmission. Parrish, Gau and Zajic et al., (2013) state that most of the new methods of phylogenetic sequencing enable the dissemination of information and data on transmission periods and rates in Europe, with most countries in the western part mainly being affected. The DNA profiling technology and phylogenetic methods enable health experts to track down super-transmitters, thus preventing future transmission by treating them. The transmission timing should be developed from the vital genetic sequencing of gene trees. Beyrer, Baral and Wirtz et al., (2012) dedeclarehat this development facilitates the linking of variants of the donors and receptors hence facilitating the timing of transmission dates and window periods.
Hue, Dunn and Dolling et al., (2014) reveal that the overall prevalence of transmission of resistance in Europe indicates that the rates of newly diagnosed individuals with HIV-1 Subtype C are increasing by 10% annually. Generally, the NTRI transmission resistance stabilizes while P.I. and NNRTI resistance constantly decreases over the two periods. Garcia, Maddali and Jiang et al., (2019) denote that according to the GenBank accession digits, the study noted that the Subtype C strain has increased and stabilized over a long period. The following GenBank ascension digits were used to evaluate the stability of the Subtype C variant in the two respective periods.
AY938492/AY940272, AY938498/AY940297, AY938499/
AY940249, AY938500/AY940270, AY938501/AY940298,
AY938502/AY940251, AY938503/AY940302, AY938504/
AY940217, AY938507/AY940279, AY938508/AY940291,
AY938509/AY940274, AY938510/AY940271, AY938511/
AY940301, AY938512/AY940234, AY938513/AY940263, AY938514/AY940265, AY938515/AY940267, AY938516/ AY940268, AY938517/AY940260, AY938519/AY940266, AY938520/AY940250, AY938521/AY940304, AY938522/ AY940307, AY938523/AY940305, AY938524/AY940306,EU248399, EU248400, EU248401, EU248403, , EU248406–EU248408, EU248410–EU248412, EU248415–EU248419, EU248421–EU248426, EU248428,EU248431, EU248435, EU248439, EU248440–EU248444, EU248446, EU248448, EU248449, EU248451,EU248453, EU248455–EU248457, EU248459–EU248461,EU248463–EU248466, EU248468–, EU248476,EU248477, EU248479, EU248480, EU248483, EU248485,EU248487–EU248490, EU248492, EU248494–EU248498,EU248500–EU248505, EU248507, EU248509, EU248512,EU248515, EU248517–EU248521, EU248523, EU248526–EU248569, EU248571–EU248582, EU248584–EU248588,EU673374–EU673397, FJ030767, FJ030769, FJ030771,FJ030772, FJ185113–FJ185120, FJ185122, FJ185124,FJ185125, FJ185127, and GQ398826–GQ401
GenBank data <https://www.ncbi.nlm.nih.gov>. from the National Library of Medicine.
The research was conducted in all the European countries to evaluate the quality of genotypic data generated through DNA profiling technology. Mandatory definition of TDR was conducted in line with the currently established list of mutation surveillance through the World Health Organization (WHO) recommendations. Margolis, Garcia and Hazoda et al., (2016) evaluates the specific timelines of patient infections were unknown (n=1978 patients with known genetic antibody testing results and dates). Von, Boni and Shah et al., (2011) state that the seroconversion rate was documented (n=918). However, the positive reactions laboratory tests were completed before seroconversion (n=354) was applied as the accurate infection rate for patients with laboratory evidence of acute conversion. For the re3maining seroconverts (n=498), the infection date was calculated as the mid-points of the last HIV-1 documented analysis and the first recognized positive antibody result.
The period for conducting these two tests was ≤ 3.6 years. For better efficient analysis and evaluation, countries in Western Europe were recognized to be having a consistent history of better access to quality ARVs and reasonable adherence rates (Sen, Kaminski and Kurachi et al., 2016). These countries were; Austria, Greece, Portugal, Belgium, Denmark, Finland, Sweden, Ireland, Italy, The Netherlands, Switzerland, and Greece (Hamelaar, Loganathan and Elangovan et al., 2020). Other countries such as Egypt and UAE were included in this research study. The integration of these countries in the study examined the HIV-1 Subtype C patterns due to movements and socio-economic trends like commerce and trade.
For determining TDR predictors, multivariate and univariate analyses were applied. Zanini, Brodin and Thebo et al., (2015) state that the inadequate distribution of univariate predictors was considered a possible factor affecting the multivariate trend analysis due to statistical significance (p< .06). These confounding factors affecting research are vital in the quality assurance of tests. In determining The ega HIV-1 subtyping tool , HIV-1 Subtype C(version 2.0 was used and is accessed at https://www.bioafrica.net/subtypetool/html/). Analysis of facts was carried out by use of R’s statistical software.
Calculation of Confidence Interval was performed by 95% of Wilson Score using the binomial expansion values. A comparison of categorical data was conducted by applying x2 tests, logistic regression techniques, and Fishers’ exact tests (Hamelaar, Loganathan and Elangovan et al., 2020). The performance of phylogenetic analysis tests was conducted in investigating if the samples were derived from the suitable tight clusters. The continuous data were examined using Poisson and linear regression and students’ t-tests.
Parken, Sammons and Maze et al., 2016 declare a huge extent of HIV-1 subtype C than any other subtype in Europe, with improved replication analysis in each of the studies. The study revealed that in vitro virus replication capacity due to co-infection of weak cells with various viral loads differentiates diverse subtypes (Huang et al., 2017). The results indicate that HIV-1 Subtype C was overcome by other subtypes (A, B, and D) by T cells, CD4 counts, macrophages, and all activated PBMC cultures, revealing the reduction of replicative fitness among patients.
Generally, all these dual competition analyses reveal that HIV-1 Subtype C virus decreases replication quantity compared with other group M viruses. The decreased replication capacity reduces the illness development of HIV-1 Subtype C hence facilitating the degree and magnitude of infections. The study reveals that decreased replications kinetics increases the half-life of infected cells Pitman, Lau and Lewin et al., (2018). The transmission of genetic fluid in the cells escalates the transmission of infection rate. As part of the SPREAD program, many citations were retrieved on the categorical molecular epidemiology research. The exclusion of non-relevant studies and analysis revealed the spread of transmission pathways in the population.
The HIV-1 data was derived from the Los Alamos HIV database for all the European countries as directed by the WHO. Wei, Duffy and Allison et al., (2018) state that during this period infection patterns in the continent revealed the protocols and policies required by the WHO. The evaluated research indicated the application of standardized methodologies in the molecular analysis comprising of integration of specific genomic regions. Bimewise, Roberts and Kerstenetai et al., (201declareres that most of these genomic regions use the sequencing of genetic PCR and phylogenetic tree analysis. However, there is a challenge in studying comparison and periodic molecular research variations.
Parken, Sammons and Maze et al., (2016) state that various suggestions and literature reveal that HIV-1 Subtype C affects transmission rates among individuals. In Western Europe, data indicates that the transmission rate of mother to child is higher in the Subtype C variant than in any other variant. Most expectant women affected with Subtype C variant are more prone to infections than Subtypes A and B since they can release vaginal cells, revealing more transmission rates. The research investigates the longitudinal cohort amgivinggive a jab to the drug user in Denmark conducted between 1995 and 1998 (Tokarev, Sivro and Omole et al., 2020). The results indicate that there was an increased transmission probability rate of CRF01-AE. Though there were challenges in virology, epidemiologic and other residency factors affecting the spread, it was noted that the frequency of CRF01-AE was more prevalent in Western Europe.
The infection rate of HIV-1 Subtype C is commonly associated with men having sexual intercourse with fellow men (MSM). This variant was more common in Western Europe than in any part of the world. Mc Elrath, DeRosa and Janes et al., (2008) state that The SPREAD program is more promising in surveillance of TDR. The data represents the stabilizing trends realized in the growth and development of HIV-1 Subtype C in European countries between 1995 to 2000 (Beyrer, Baral and Wirtz et al., 2012). The genetic variety of TDR trends reveals a substantial protagonist in determining the resistance and viability of HIV-1 Subtype C (Ramusuran, 2019). The study showed that a group of genes influences disease progression and susceptibility among patients. These groups consist of genes coding for human leukocyte antigens (HLAs), genes coding for coreceptors CCR5and those connected in the immune response to HIV-1. The transmission of CCR5 coreceptors and CD4 receptors in Subtype C in the human body is more prevalent in Western Europe. Johnson, Wei and Craig et al., (2008) state that this virus exclusively depends on CCR5 to survive in the starting moments or premature stages of the disease infection in human beings. The condition of this variant maintains the progression of the disease to levels of 50%.
The graph below shows the prevalence of HIV-1 Subtype C in Europe between 1995-2000
HIV-1 Subtype C prevalence in Europe between 1995-2000 (Colby et al. 2018)
Mortality rates of HIV-1 Subtype C infections in 1995-2000 (Colby et al. 2018)
Notably, in this period the genetic variations in the CCR5 strain among ethnicities and groups facilitate the prevalence of HIV-1 in different geographical locations (Margolis, Garcia and Hazuda et al., 2016). The CCR5 32bp deletion produces protein not shown in the variant cells. This lack of revelation protects the virus from associating with the host cell. The study reveals that some homozygous individuals in this mutation are highly resistant to viral infections. However, other findings indicate that CCR5 32 bp individuals have got heterozygotes 2-4 years compared to AIDS (Bimewies, Roberts and Kerstenetai et al., 2018). The European Caucasians have got HIV-1 Subtype C prevalence rate of 15% of the population. The I prevalence of HIV-1 in the periods of 2019-2021 reveals that CCR5 (-2459G>A) polymorphism that facilitates increased CCR 5 was found to be 98% among adults in New Papua Guinea. This country has a high prevalence rate of HIV-1 Subtype C, thus revealing the CCRR alleles among different ethnic groups and populations.
Parrish, Gao and Zajic et al., 2013 reveal that in the human genome and surface encodes, the HLA is the most polymorphic gene locus representing foreign antigens mandated with controlling disease in the human body. The human genome encodes two groups comprising HLA class I and II—the HLA class I incorporates three genes, namely, HLA-A, HLA-B, and HLA-C. The significant function of HLAs is to foster foreign antigens initiating T cell responses. The HLA I is associated with an increased disease prevalence of AIDS among Caucasians from the USA, the Netherlands, and the Rwandese (Johnson, Wei and Craig et al., 2008). Between 2019 and 2021, HLA alleles are significant in the Subtype C disease outcomes. The study shows that HLA-B*51;01 is substantial in protecting individuals in the formative years of the disease (Hamelaar, Loganathan and Elangovan et al, 2020). The HLA-B*35:01 and HLA-B*07:02 are also linked with the protection of disease alleles in the Subtype C variant. Generally, disease progression and prevalence facilitate HLA alleles in the body.
Variations of disease transmission and progression influence the HIV-1 Subtype C variant infections in the low and middle socio-demographic income patterns. Leyre, Kroon and Colby et al., (2020) declare that the periods of 2019 – 2021 reveal the clinical characteristics of HIV-1variant in the human body. This study’s median period of infection rates before enrolment was 28 years. This variant was exclusively responsible for infections in Western Europe, while the subtypes A and D were common in North America (Dofour, Gantner and Fromentin et al., 2021). The correlation of HLA types, and hand hazard ratio analysis reveals that individuals infected with Subtype C variant lowly progressed to CD4 T cell counts and AIDS endpoints. The study shows that the untreated individuals infected with Subtype C in Western Europe had higher overall infection rates influencing the HIV-1 endpoints.
Due to the absence of selective drug pressure, the HLA type’s variants are resistant to mutations, increasing infection rates. Patients chronically affected and drug naïve patients tend to resist genotypic variations in the body.
HIV-1 Subtype C infection in 2019-2021 (Wee et al. 2016)
Mortality Rate of HIV-1 Subtype C in 2019 – 2021 (Wee et al. 2016)
Ondondo, Murakoshi and Wee et al., (2016) state that A total of 2576 HIV-1 Subtype C conformed to the predefined inclusion methods. In this criterion, the HIV -1 patient who was drug naïve were below 20 years old with a sample of genotypic evaluation of one year after diagnosis. This group, after being tested, had a viral weight of < 1000 copies /mL (Bennet, Otelea and Feury et al., 2009). This viral load prompted the success of < 95% in all the samples tested. Of most patients in Western Europe who were tested, 67% were MSM, while a small percentage interviewed were unknown (Gracia ,Maddali and Jiang et al., 2019). From 1995-to 2000, the TDR prevalence was 7.9%. The occurrence of the mutations linked to spread among 300 of 2576 patients with 96% CI, 8.4%-9.3%, Nucleoside Reverse Transcriptase Inhibitor (NRTI) was 6%. These patients had non-NNRTI resistance was 4.3% (102 of 2576 patients, 95% CI, 1.9%-2.8%). Transmitted P.I resistance was 2.9% (109 of 2576 patients 95% C.I, 2.6% – 3.5%).
In this study, it was noted that there were 18 cases that which dual-resistant were discovered (0.94%). Among the patients with HIV-1 Subtype C, the highly predominant Amino-acid variation was RT215or relevant RT215Y/F (88 (2.65%) of patients). Markowitz, Mendo and Gatuzzo et al., (20stateates that this research identified 52 RT41L cases (1.76%) There were also other (frequent cases in > 0.5%). The mutations were PR46I/L (1.46%), RT219Q/E/R/N (1.2%), RT103N/S (1.56%), and RT210W (0.82%).
The logistic regression between 2019 -2021 reveals that the overall TDR (odds ratio (OR) 1.12% (95% CI, 1.22% -2.56%); p=.19) and Spread NRTI resistance (OR, 1.12% (95% CI, 0.7-2.45%); p= .13) (Parish, Gau and Zajic et al., 2013). The Transmitted NNRTI resistance revealed statistically crucial parabolic periods with higher levels towards the end of 2020 (p=0.3). Furthermore, there was a tremendous reduction over time in transmitted P.I. resistance (OR, 0.95(95% CI, 0.67-1.6); p=.05), revealing a statistical reduction I PR90M (OR, 0.4195% CI, .030-0.89);p =.007 (Gracia, Maddali and Jiang et al., 2019). The univariate testing of the Subtype C virus revealed the transmission routes of various statistical routes associated with TDR. The study revealed that being infected with the Subtype C variant was the multivariate approach to the independent inhibitors of TDR.
HIV-1 Subtype C
Phylogenetic data on HIV-1 Sub-type C (Wee et al. 2016)
HIV-1 Subtype C infections in 1995-2000 (Wee et al. 2016)
HIV-1 Subtype C infections in 2019-2021 (Wee et al. 2016)
Results of HIV-1 Sub-type C (Wee et al. 2016)
The differential strains of HIV-1 Subtype C in Europe are increasingly stabilizing with a rate of 10%. McElrath, DeRosa and Janes et al., (2008) state that Using the phylogenetic evaluation
Results of HIV-1 Sub-type C (Wee et al. 2016)
Through DNA profiling technology, this study reveals how various strains and viruses interact with the human host, thus affecting disease progression and transmission. The HIV-1 Subtype C strain can utilize the chemokine coreceptors CCR5 (R5 viruses). The transmission rate of these viruses in Europe is higher than those of strains using the CXCR4 coreceptors (X4) viruses. These transmissions imply that the X4 viruses are more susceptible to disease progression and transmissions (Gracia, Maddali and Jiang et al., 2019). . The researchers noted that all the subtypes of HIV-1 strains utilize all the receptors except Subtype D.
The significant aspect of transmission is the levels of disease progression in the continent, with several data revealing the origin of various HIV-1 Subtypes among different groups in the population. Leyre, Kroon and Colby et al., (2020) declare that a study conducted in the late early 1980s to late 1990s indicates that data collected in Western Europe examining multiple subtypes of HIV-1 among female sex workers had varying statistics (McBrien, Kumar and Silvestri et al 2018). The results projected the high infection rates due to lack of awareness and low adherence to drugs. In this period in Europe, understanding of the impacts of HIV as an infection was low due to a lack of awareness (Hue, Dunn, Dolling et al., 2014). The demographic curve of the infection rates from 1980-to 1985 is steep, while in the 1990s, there was a plateau, thus implying the reduction of infection rates. The decrease in infection rates in 1995-2000 reveals that the steps that facilitated the virus reduction yielded fruits.
Markowitz, Mendo and Gotuzzo et al., (2007) state that the study revealed a high probability of disease infection affected by Subtype C more than other strains. From 1995-to 2000, the reduction of infection rate among the Subtype C population in Western Europe, thus reducing mortality rates. The mortality rate was reduced due to the shorter survival periods of the patients infected with this strain.
The study noted a reduced mortality rate among the patients affected with Subtype C patients more than any other strain in the early 1980s. The ethnic and geographical group factors did not escalate the high death rate progression in the continent (Zanini, Brodin and Thebo et al, 2011). The recombinant forms of subtype C strain affected most patients with low TDR. The ability of subtypes C to reveal a high degree of dual tropism in Europe implies that this strain is associated with high levels of disease progression than any subtype (Huang et al., 2017). The notable associations relevant to this study reveal that nutritional status, host genetic factors, access to quality medical care, and mode of viral transmission are escalating disease progression in the continent.
In the period 2019-2021, the study noted that the known variations in HIV-1 transmissions and disease progressions incubating among hosts incubating specific HLA class I are different from other types. McBrien, Kumar and Nkosi et al., (2018) declare that patients with HIV-1 who were affected by the T cells reveal cross-cutting subtype specifications hence recognizing various viral epitopes among the strains of subtypes. The CD8 and T cells examined from patients infected with Subtype C show that viral epitopes from conserved segments have different genetical sequences from other genome subtypes. Wei, Duffy and Allison et al., (2018) state that the researchers noted that the response to immune in HIV-1 Subtype C is low due to reducing CD8 and subsequent T cells in the body system. The similarities in the T cell response and inter-sub type differences of plasma levels in the HIV-1 viral load signify the load levels of Vivo-viral infections among patients.
The most urgent question notably asking is how HIV-1 influences antiretroviral treatment response. Sen, Kaminski and Kurachi et al., (2016) state that the significant challenge of this aspect is that most global infections caused by HIV-1 Subtype B stand at 12%, while infections caused by subtype C stand at 47%. These differences in the clinical data of these subtype variants reveal the defective antiretroviral drugs for the subtype C in the past few years. Raw data from clinical cohorts shows that HIV-1 subtypes progressively spread more than HIV-2 hence posing challenges regarding treatment decisions. Parken, Sammons and Maze et al., (2016) assert that the data of the baseline antiretroviral viability collected from studies reveal that there is intrinsic resistance of nonnucleoside reverse transcriptase-inhibitors, therefore, affecting the admission of ARVs.
Though existential challenges affect the comparison of responses to therapy in Subtype C patients with low-income, the period 2019-2021 revealed an increased number of patients receiving ARVs. (Ndlovu, Kazer and Nkosi et al., 2019) asserts that although some Subtype C patients had low CD4+ cells, the segment having HIV-1 RNA viral load below 400 copies per milliliter at 18 months significantly differed. Taylor, McCutchan and Hammar et al., (2020) state that the study of a French cohort comprising 520 patients, 32% of who had Subtype C of HIV-1, revealed that twelve months after initiating ARVs, clinical progression was not affected by the HIV-1. The CD4+ count and viral load responding to the treatment were constant, thus revealing better results.
The study of Africans living in London who were exposed to HIV-1 Subtype C virus revealed no significant difference when they responded to ARVs. Vercauten, Wensing and Balotta et al., (2009) state that data from the World Health Organization indicates substantial improvement in patients receiving ARVs. The data reveals the TDR resistance among children living with HIV-1 Subtype C. Generally; the study notes that response to ARVs therapy improved hence the low mortality rates in Europe.
Zannini, Boni and Thebo et al., (2015) state that various studies reveal that resistance trends in Subtype C patients receiving ARVs reveal that polymorphism found in this subtype recognizes the specific pathways to secondary resistance of mutations.; These mutations facilitating resistance in different pathways are frequent in HIV-1Subtype C strains.
HIV-1transmission rate in Europe is stabilizing hence the need for enhanced surveillance of this infection. Colby Trautman and Pinyakonn, et al., (2018) denote the need for essential dissemination of information, thus enabling the monitoring and evaluation of the epidemic in the continent. This monitoring influences the regulation of infectious transmissions (Sen, Kaminski and Kurachi et al., 2016). For this objective to be achieved, the research denotes that the surveillance data should be high quality and accurate. The WHO European Region should achieve full coverage reporting of HIV awareness.
There is a need for more research on efficient vaccine development capable of neutralizing antibodies binding the trimetric envelope of the virus. There is a need for further trials of monomeric types of external glycoprotein 120. Ramusuran et al., (2019) assert that there is a need for immunogenic neutralizing designs that mimic glycol-protein 120 and glycol-protein 41envelope trimmers (Johnson, Wei and Craig et al 2008). This will enable establishing strategies used in confronting these challenges, including applying consensus sequencing. There should be escalated efforts to produce a vaccine regimen developed by the Vaccine Research Center of the National Institute of Allergy and Infectious Diseases (Hue, Dunn and Dolling et al., 2014). This regimen involves making multigene and multisubtype vaccines.
The research denotes that despite the similarities in T-cell responses and inter-subtype variations affecting plasma variations, further research is needed on investigating Vivo-viral load transmission and disease progression rates. Margolis, Garcia and Hazuda et al., (2016) state that this will facilitate findings on the impacts of different types of HIV on viral loads among patients, thus establishing accurate regimen applications while treating patients.
Research from most data reveals that pro0visisonal results are mostly being recorded and frequent updates are not conducted hence challenging results outcomes. There is a need to verify and update HIV –AIDS reports regularly by the public health officials from the European Commission. Von, Boni and Shah Et al, (2011) state that official data reports from various sources in the European Commission do not tally hence not presenting the existential evidence of HIV prevalence in Europe. These provisional reports prompt challenges in evaluating and analyzing incubation windows and disease progression in the continent. The European Commission should annually analyze, evaluate and record data from each country. This enables the keying of information for diagnostic, follow-ups, and planning purposes.
Although many countries in Europe strive to achieve quality results relating to HIV-1Subtypes variants, little has been done in improving the state events regarding HIV recording and analysis. Zanini, Brodin and Thebo et al., (2015) state that only a few European countries have managed to issue reports concerning the delays in their surveillance and recording systems. Data from the EU reveals that under-reporting and surveillances of HIV-AIDS deaths are rampant and only one-third of the countries in the continent can analyze their records comprehensively (Taylor, McCutchan and Hammar et al., 2020). There is a need for a lot of improvement in the recording and surveillance of data since there will be a true projection pandemic.
Conclusion
The continuous spread of HIV in the world poses a significant health challenge, thus prompting significant unprecedented epidemic challenges causing genetic and geographical aspects ( Parrish, Gau and Zijac et al., (2013). Some of the factors affecting the spread of HIV-1 Subtype C have not been identified. Kaslow, Tang and Dorak et al., (2014) state that the mutation and recombination are critical features of HIV replication, affecting the diversity of the virus among patients hence the need for vaccine development and anti-retroviral therapy, thus reducing the pandemic.
The challenges of HIV/AIDS in Europe are very critical and need effective guidelines and policies guiding this disease management. McErath, De Rosa, Janes et al., (2008) states that due to migrations from African and Middle –East countries, European Commission should not assume that it can ban itself from the pandemic (Kaslow, Tang and Dorak et al., 2014). Most migrants from Africa originate from countries with a high prevalence of HIV-1. These policies should develop structures regulating the movement of immigrants within most European borders. The major challenge Europe is facing is the correlation between various HIV strains and viruses (Bennet, Otelea and Feury et al., 2009). The correlation of these strains in Europe poses a major health challenge in this continent.
The mutation and recombination genetic analysis review indicate the complex models needed for virus management. The study reveals the evolution, history, and demographic trends of HIV -1 Subtype C (Bimewies, Roberts and Kerstentei, et al 2018). The development and genetic formation of the virus enable a deep understanding of healthcare management of HIV. The mutation of CCR5-delta 32 in Europe is a critical aspect of developing vaccines and ARVs (Margolis, Garcia and Hazuda et al., 2016). The CCR5-delta 32 outlines fresh opportunities in the prevention of HIV-1 Subtype C strains. The CCR5-delta 32mutation patients have got the opportunistic advantage of testing in Europe (Parrish, Gau and Zajic et al.,2013). However, these patients should live healthy lifestyles so that effective and successful treatments should be established.
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First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download