Genotype Distribution and Migration Patterns of Hepatitis C Virus in Shandong Province, China: Molecular Epidemiology and Phylogenetic Study
Bibliographic record
Abstract
Background: Hepatitis C virus (HCV) remains a significant public health concern in China, particularly in Shandong Province, where detailed molecular epidemiological data are limited. HCV exhibits substantial genetic diversity, and understanding its genotype distribution and transmission dynamics is critical for developing effective control strategies. Objective: This study aimed to investigate the genetic diversity, geographic dissemination, and evolutionary history of HCV genotypes in Shandong Province, China, using molecular techniques and phylogenetic methods. Methods: A total of 320 HCV-positive serum samples were collected from multiple hospitals across Shandong Province between 2013 and 2021. HCV RNA was extracted and amplified targeting the 5' untranslated region (UTR), Core, and NS5B regions. Sequencing was conducted, and genotypes were determined using the National Center for Biotechnology Information's Basic Local Alignment Search Tool (NCBI BLAST). Phylogenetic trees were constructed using maximum likelihood methods with the general time reversible with Gamma-distributed rate variation among sites [(GTR)+Gamma model]. The temporal and geographic evolution of the major subtypes (1b and 2a) was analyzed using Bayesian Markov chain Monte Carlo (MCMC) methods implemented in Bayesian Evolutionary Analysis Sampling Trees (BEAST). The Bayesian skyline plot (BSP) was used to infer population dynamics and estimate the time to the most recent common ancestor (tMRCA). Results: Genotypes 1b (n=165) and 2a (n=131) were identified as the predominant subtypes, with a small number of genotypes 3b, 6a, 6k, and potential recombinant strains also detected. Phylogenetic analysis revealed distinct evolutionary clustering of 1b and 2a strains, suggesting multiple diffusion events within the province. The tMRCA of subtypes 1b and 2a were estimated to be 1957 and 1979, respectively. Bayesian skyline analysis showed that both subtypes experienced long-term population stability, followed by a rapid expansion period between 2014 and 2019 (1b) and 2014 to 2016 (2a), respectively. The analysis also identified key transmission hubs such as Jinan, Liaocheng, Tai'an, and Dezhou, indicating city-level variations in HCV spread. Conclusions: This study provides data-supported insights into the genotypic landscape and evolutionary patterns of HCV in Shandong Province. The identification of dominant subtypes, potential recombinant strains, and regional transmission pathways enhances our understanding of local HCV epidemiology. These findings have implications for public health policy, resource allocation, and targeted treatment strategies. The integration of molecular epidemiology and phylogenetics offers a valuable model for infectious disease surveillance and control in similar settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".