Immunogenicity and Protective Efficacy of Recombinant Human T-Cell Leukemia/Lymphoma Virus Type 1 NYVAC and Naked DNA Vaccine Candidates in Squirrel Monkeys (<i>Saimiri sciureus</i>)
Bibliographic record
Abstract
We assessed the immunogenicities and efficacies of two highly attenuated vaccinia virus-derived NYVAC vaccine candidates encoding the human T-cell leukemia/lymphoma virus type 1 (HTLV-1) env gene or both the env and gag genes in prime-boost pilot regimens in combination with naked DNA expressing the HTLV-1 envelope. Three inoculations of NYVAC HTLV-1 env at 0, 1, and 3 months followed by a single inoculation of DNA env at 9 months protected against intravenous challenge with HTLV-1-infected cells in one of three immunized squirrel monkeys. Furthermore, humoral and cell-mediated immune responses against HTLV-1 Env could be detected in this protected animal. However, priming the animal with a single dose of env DNA, followed by immunization with the NYVAC HTLV-1 gag and env vaccine at 6, 7, and 8 months, protected all three animals against challenge with HTLV-1-infected cells. With this protocol, antibodies against HTLV-1 Env and cell-mediated responses against Env and Gag could also be detected in the protected animals. Although the relative superiority of a DNA prime-NYVAC boost regimen over addition of the Gag component as an immunogen cannot be assessed directly, our findings nevertheless show that an HTLV-1 vaccine approach is feasible and deserves further study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".