Molecular epidemiological study on hepatitis C virus among drug users in Zhuhai, China
Bibliographic record
Abstract
Objective To investigate the molecular epidemiology of hepatitis C virus (HCV) among drug users in Zhuhai, China. Methods Anti-HCV and HCV RNA were detected by using ELISA and real-time RT-PCR respectively. HCV genotypes and subtypes were determined by direct sequencing of amplicons in partial NS5B regions and BLAST GenBank, followed by phylogenetic analysis. Results Of 435 blood plasma from drug users, 242 was negative for anti-HCV and HCV RNA, 147 were positive for anti-HCV and HCV RNA, 46 positive HCV RNA or anti-HCV. The infection rate of HCV was 44.37%. Three genotypes and five subtypes of HCV were detected in 78 samples of positive HCV RNA. Subtypes 6 a, 3b, 3 a, 1b and 1 a, were detected at frequencies of 60.26%, 15.38%, 14.10%, 6.41% and 3.85%, respectively. Compared with strains from Shenzhen, Foshan, Guangzhou, Shanwei, Hong Kong, Denmark, Canada, USA, United Kingdom, India and Nepal, HCV among drug users in Zhuhai were homophylic. Conclusions There were homology among HCV in drug users from Zhuhai and strains from some regions and countries. Subtype 6 a was the most predominant. Subtype 1 a and 3 a were discovered in Zhuhai. Key words: Drug users; HCV; Genotype; Phylogenetic tree; Homology
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 | 0.000 |
| 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 | 0.000 | 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".