Hepatitis C virus genotypes in patients with chronic hepatitis C infection in southern Iran from 2016 to 2019
Bibliographic record
Abstract
Hepatitis C is a liver disease caused by the hepatitis C virus (HCV). The treatment of HCV infection has become more complicated due to various genotypes and subtypes of HCV. The treatment of HCV has made significant advances with direct-acting antivirals. However, for the choice of medicine or the combination of drugs for hepatitis C, it is imperative to detect and discriminate the crucial HCV genotypes. The main objective of this study was to determine the pattern of circulating HCV genotypes in southern Iran, from 2016 until 2019. The other aim of the study was to determine possible associations of patients' risk factors with HCV genotypes. A total of 803 serum samples were collected in 4 years (2016-2019) from patients with HCV antibody positive results. A total of 728 serum samples were HCV-RNA positive. The prevalence of HCV genotypes was detected using the genotype-specific RT-PCR test for serum samples obtained from 615 patients. The HCV genotype 1 (G1) was the most prevalent (48.8%) genotype in the area, with G1a, G1b, and mixed G1a/b representing 38.4%, 10.1%, and 0.3%, respectively. Genotype 3a was the next most prevalent (47.2%). Mixed genotypes 1a/3a were detected in 22 (3.6%) and finally G4 was found in 3 (0.5%) patients. The other HCV genotypes were not detected in any patient. Genotype 1 (1a and 1b alone, 1a/1b and 1a/3a coinfections) is the most prevalent HCV genotype in southern Iran. HCV G1 shows a significantly higher rate in people under 40 years old.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".