Hepatitis C genotype distribution and homology among geographically disparate injecting drug users in Afghanistan
Bibliographic record
Abstract
Hepatitis C virus (HCV) prevalence is high among injecting drug users in Afghanistan, but transmission dynamics are poorly understood. Samples from HCV-infected injecting drug users were sequenced to determine circulating genotypes and potential transmission linkages. Serum samples were obtained from injecting drug user participants in Hirat, Jalalabad, and Mazar-i-Sharif between 2006 and 2008 with reactive anti-HCV rapid tests. Specimens with detected HCV viremia were amplified and underwent sequence analysis. Of 113 samples evaluated, 25 samples (35.2%) were only typeable in NS5B, nine samples (12.7%) were only typeable in CE1, and 37 samples (52.1%) were genotyped in both regions. Of those with typeable HCV, all were Afghan males with a mean age of 31.1 (standard deviation [SD] ± 8.0) years and mean duration of injecting of 3.9 (SD ± 4.3) years. Most reported residence outside Afghanistan in the last decade (90.1%) and prior incarceration (76.8%). HCV genotypes detected were: 1a, (35.2%, n = 25), 3a (62.0%, n = 44), and 1b (2.8%, n = 2). Cluster formation was detected in NS5B and CE1 and were generally from within the same city. All participants within clusters reported being a refugee in Iran compared to 93.5% of those outside clusters. Only 22.2% (4/11) of those within clusters had been refugees in Pakistan and these four individuals had also been refugees in Iran. Predominance of genotype 3a and the association between HCV viremia and having been a refugee in Iran potentially reflects migration between Afghanistan and Iran among IDUs from Mazar-i-Sharif and Hirat and carry implications for harm reduction programs for this migratory population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".