Hepatitis E Virus (HEV) Capsid Antigens Derived from Viruses of Human and Swine Origin Are Equally Efficient for Detecting Anti-HEV by Enzyme Immunoassay
Bibliographic record
Abstract
The recombinant truncated ORF2 (capsid) antigen derived from the Meng strain of swine hepatitis E virus (HEV) differs from that of the Sar-55 strain of human HEV by approximately 5% at the amino acid level. Serial serum samples from two chimpanzees and six rhesus monkeys experimentally infected with HEV were tested with one enzyme immunoassay (EIA) based on the Sar-55 antigen and with a second EIA based on the Meng antigen. We obtained 98% agreement (kappa = 0.952) by direct comparison. The virtually identical results obtained with these antigens in detecting seroconversion following infection with HEV suggests that they were reacting with antibodies that detect the same or very similar epitopes of HEV. We then tested human and swine serum samples for anti-HEV in EIAs that utilized one or the other of the two ORF2 antigens and showed that these results were also virtually identical. The specimens tested included swine sera from the United States, Canada, China, Korea, and Thailand and sera from veterinarians, U.S. and non-U.S. volunteer blood donors, and U.S. and non-U.S. animal handlers. We tested 792 swine sera and obtained 93% agreement (kappa = 0.839). We similarly tested 882 human sera and obtained 99% agreement (kappa = 0.938). Moreover, we found virtually no difference in the levels of prevalence of anti-HEV as measured by the two tests, again suggesting that the antigens derived from human and swine HEV contain the same immunodominant epitopes.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".