Low hepatitis D co-infection among hepatitis B virus surface antigen-positive blood donors in Kenya
Bibliographic record
Abstract
Background: Hepatitis delta virus (HDV) is a highly pathogenic virus, and causes rapid disease progression from fulminant hepatitis (FH) to development of hepatocellular carcinoma (HCC) in patients co-infected with hepatitis B virus (HBV). However, its exact global burden of HBV-HDV co-infections remains largely obscure, particularly in sub-Saharan Africa. The objective of this study was to determine the prevalence of anti-hepatitis delta virus (anti-HDV) in hepatitis B virus surface antigen (HBsAg)-positive blood donors from Kenya. Methods: A total of 239 HBsAg-positive serum samples, obtained from healthy Kenyan blood donors from June 2014 to November 2017 were analyzed in this cross-sectional study. ELISA was done using the International Immunodiagnostics HDV Ab EIA kit, according to the instructions of the manufacturer, for anti-HDV immunoglobulin G (IgG) determination. Results: Of the 239 HBsAg-positive blood donors, 187 (78.24%) were male, and 52 (21.76%) were female. The average age of the study participants was 24.11 years. Serological analysis revealed that 3/239 (1.26%) study participants were HDV seropositive. Conclusions: Our data suggest that HDV infection is rare among blood donors in Kenya, with anti-HDV positivity rates being relatively lower compared to other countries. Nonetheless, ongoing surveillance is essential to track any potential changes in prevalence over time.
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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) | 0.003 | 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".