Genetic Characterization of Hepatitis C Virus Among People Who Use Crack Cocaine: A Study Conducted on the Brazilian Amazon Coast
Bibliographic record
Abstract
People who use crack cocaine (PWUCC) constitute a key population due to vulnerability and marginalization, especially in a socio-ecologically diverse, relatively isolated region with limited public health infrastructure. This study aimed to perform a genetic characterization of circulating HCV among PWUCC in the municipality of Bragança, situated on the Brazilian Amazon coast, identifying viral genotypes, subtypes, resistance-associated substitutions (RAS)-naturally occurring mutations in the viral genome that can reduce the efficacy of direct-acting antiviral (DAA) agents-and predictions of phenotypic resistance. METHODS: Between 2016 and 2018, biological samples and epidemiological data were obtained from 165 PWUCC. Viral detection was performed using RT-PCR, while genotyping, subtyping, and RAS profiling were conducted through nucleotide sequencing and fragment analysis. RESULTS: In 165 PWUCC, 22 (13.3%) tested positive for HCV RNA. Most of them had not had access to public health services (91.5%), and more than half (57.0%) reported living in unstable housing conditions. HCV subtypes 1a (27.3%), 1b (40.9%), and 3a (31.8%) were detected. Evidence of resistance associated with DAAs, such as daclatasvir and dasabuvir, was detected in five PWUCC with HCV (22.7%). CONCLUSIONS: The high prevalence of HCV infection, predominantly subtype 1b, and significant levels of resistance are very concerning. This demonstrates the urgent need for targeted public health interventions to expand access to testing, treatment, and effective antiviral therapy in this vulnerable population of the Brazilian Amazon.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".