Optimization of a DNA Vaccine Against SARS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it