Genome-wide analysis for identification of adaptive diversification between hepatitis C virus subtypes 1a and 1b
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a major cause of liver disease and has been estimated to infect approximately 2%-3% of the world's population. HCV genotype 1 is the subject of intense research and clinical investigations because of its worldwide prevalence and poor access to treatment for patients in developing countries and marginalized populations. The predominant subtypes 1a and 1b of HCV genotype 1 present considerable differences in epidemiological features. However, the genetic signature underlying such phenotypic functional divergence is still an open question. Here, we performed a genome-wide evolutionary study on HCV subtypes 1a and 1b. The results show that adaptive selection has driven the diversification between these subtypes. Furthermore, the major adaptive divergence-related changes have occurred on proteins E1, NS4B, NS5A, and NS5B. Structurally, a number of adaptively selected sites cluster in functional regions potentially relevant to (i) membrane attachment and (ii) the interactions with viral and host cell factors and the genome template. These results might provide helpful hints about the molecular determinants of epidemiological divergence between HCV 1a and 1b.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".