Genetic History of Hepatitis C Virus in Venezuela: High Diversity and Long Time of Evolution of HCV Genotype 2
Bibliographic record
Abstract
BACKGROUND: The subtype diversity of the hepatitis C virus (HCV) genotypes is unknown in Venezuela. METHODOLOGY/PRINCIPAL FINDINGS: Partial sequencing of the NS5B region was performed in 310 isolates circulating in patients from 1995 to 2007. In the samples collected between 2005 and 2007, HCV genotype 1 (G1) was the most common genotype (63%), composed as expected of mainly G1a and G1b. G2 was the second most common genotype (33%), being G2a almost absent and G2j the most frequent subtype. Sequence analysis of the core region confirmed the subtype assignment performed within the NS5b region in 63 isolates. The complete genome sequence of G2j was obtained. G2j has been described in France, Canada and Burkina Fasso, but it was not found in Martinique, where several subtypes of G2 circulate in the general population. Bayesian coalescence analysis indicated a most recent common ancestor (MRCA) of G2j around 1785, before the introduction of G1b (1869) and G1a (1922). While HCV G1a and G1b experienced a growth reduction since 1990, coincident with the time when blood testing was implemented in Venezuela, HCV G2j did not seem to reach growth equilibrium during this period. CONCLUSIONS/SIGNIFICANCE: Assuming the introduction of G2j from Africa during the slave trade, the high frequency of G2j found in Venezuela could suggest: 1- the introduction of African ethnic groups different from the ones introduced to Martinique or 2- the occurrence of a founder effect. This study represents an in-depth analysis of the subtype diversity of HCV in Venezuela, which is still unexplored in the Americas and deserves further studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".