Protective Efficacy and Long-Term Immunogenicity in Cynomolgus Macaques by Ebola Virus Glycoprotein Synthetic DNA Vaccines
Bibliographic record
Abstract
Background: There remains an important need for prophylactic anti-Ebola virus vaccine candidates that elicit long-lasting immune responses and can be delivered to vulnerable populations that are unable to receive live-attenuated or viral vector vaccines. Methods: We designed novel synthetic anti-Ebola virus glycoprotein (EBOV-GP) DNA vaccines as a strategy to expand protective breadth against diverse EBOV strains and evaluated the impact of vaccine dosing and route of administration on protection against lethal EBOV-Makona challenge in cynomolgus macaques. Long-term immunogenicity was monitored in nonhuman primates for >1 year, followed by a 12-month boost. Results: Multiple-injection regimens of the EBOV-GP DNA vaccine, delivered by intramuscular administration followed by electroporation, were 100% protective against lethal EBOV-Makona challenge. Impressively, 2 injections of a simple, more tolerable, and dose-sparing intradermal administration followed by electroporation generated strong immunogenicity and was 100% protective against lethal challenge. In parallel, we observed that EBOV-GP DNA vaccination induced long-term immune responses in macaques that were detectable for at least 1 year after final vaccination and generated a strong recall response after the final boost. Conclusions: These data support that this simple intradermal-administered, serology-independent approach is likely important for additional study towards the goal of induction of anti-EBOV immunity in multiple at-risk populations.
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.
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 | 0.000 |
| 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".