Optimization of a DNA Vaccine Against SARS
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus (SARS-CoV) first appeared in Southern China in November 2002, and then quickly spread to 33 countries on five continents along international air travel routes. Although the SARS epidemic has been contained, there is a clear need for a safe and effective vaccine should an outbreak of a SARS-CoV infection reappear in human population. In this study, we tested four DNA-vaccine constructs: (1) pLL70, containing cDNA for the SARS-CoV spike (S) gene; (2) pcDNA-SS, containing codon-optimized S gene for SARS-CoV S protein (residues 12-1255) fused with a leader sequence derived from the human CD5 gene; (3) pcDNA-St, containing the gene encoding the N-portion of the codon-optimized S gene (residues 12-532) with the CD5 leader sequence; (4) pcDNA-St-VP22C, containing the gene encoding the N-portion of the codon-optimized S protein with the CD5 leader sequence fused with the C-terminal 138 amino acids of the bovine herpesvirus-1 (BHV-1) major tegument protein VP22. Each of these plasmids was intradermally administered to C57BL/6 mice in three separate immunizations. Analysis of humoral and cellular immune responses in immunized mice demonstrated that pcDNA-SS and pcDNA-St-VP22C are the most immunogenic SARS vaccine candidates.
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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) | 0.001 | 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 | 0.000 |
| 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".