High Prevalence and Diversity of Hepatitis Viruses in Suspected Cases of Yellow Fever in the Democratic Republic of Congo
Bibliographic record
Abstract
ABSTRACT The majority of patients with acute febrile jaundice (>95%) identified through a yellow fever surveillance program in the Democratic Republic of Congo (DRC) test negative for antibodies against yellow fever virus. However, no etiological investigation has ever been carried out on these patients. Here, we tested for hepatitis A (HAV), hepatitis B (HBV), hepatitis C (HCV), hepatitis D (HDV), and hepatitis E (HEV) viruses, all of which can cause acute febrile jaundice, in patients included in the yellow fever surveillance program in the DRC. On a total of 498 serum samples collected from suspected cases of yellow fever from January 2003 to January 2012, enzyme-linked immunosorbent assay (ELISA) techniques were used to screen for antibodies against HAV (IgM) and HEV (IgM) and for antigens and antibodies against HBV (HBsAg and anti-hepatitis B core protein [HBc] IgM, respectively), HCV, and HDV. Viral loads and genotypes were determined for HBV and HVD. Viral hepatitis serological markers were diagnosed in 218 (43.7%) patients. The seroprevalences were 16.7% for HAV, 24.6% for HBV, 2.3% for HCV, and 10.4% for HEV, and 26.1% of HBV-positive patients were also infected with HDV. Median viral loads were 4.19 × 10 5 IU/ml for HBV (range, 769 to 9.82 × 10 9 IU/ml) and 1.4 × 10 6 IU/ml for HDV (range, 3.1 × 10 2 to 2.9 × 10 8 IU/ml). Genotypes A, E, and D of HBV and genotype 1 of HDV were detected. These high hepatitis prevalence rates highlight the necessity to include screening for hepatitis viruses in the yellow fever surveillance program in the DRC.
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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 | 0.002 |
| 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 | 0.001 |
| 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".