HCV Genotype Variability and Its Impact on Viral Load Dynamics in a Clinical Population from Erbil, Kurdistan Region, Iraq
Bibliographic record
Abstract
Background: The Hepatitis C virus (HCV) poses a significant risk to public health. The Hepatitis C virus has emerged as the main cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma worldwide. Objective: This study focused on determining the distribution of HCV genotypes among infected persons in Erbil, Kurdistan Region of Iraq, examine their relationships with demographic variables, and evaluate the link between particular genotypes and serum viral load levels. Methods: The research comprised 120 individuals with verified HCV infection, assessed at the Public Health Laboratory in Erbil Province, Kurdistan Region of Iraq, from September 2024 to March 2025. Blood specimens were obtained, and serum was isolated for the identification of HCV antibodies, subsequently followed by the extraction of HCV RNA. Reverse transcriptase polymerase chain reaction was employed to measure the viral load, and the viral genotype was determined. Statistical analysis were performed to evaluate the correlation between genotype and viral load. Results: In the patient cohort study, genotype 1a was the most frequent, involving 33.6% of cases, followed by genotype 4 at 25.4% and genotype 3 at 17.2%. Genotype 3 had the greatest median viral load, recorded at 431,000,000 IU/mL in comparison to other genotypes. Genotype 1a was mostly detected in male patients, whereas genotype 4 was more prevalent in females. Statistical study revealed no significant correlation between HCV genotypes and patients’ sex, age, or viral load (p > 0.05). Conclusion: Genotypes 1a, 4, and 3 were the most prominent among individuals infected with HCV. Genotype 3 demonstrated the greatest median viral load. Statistical analysis indicated that there was no association between HCV genotypes and the patients’ sex, age, or viral burden. These findings enhance the comprehension of HCV epidemiology and may aid in the development of more effective ways for managing and preventing hepatitis C virus infection.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".