Single-Dose Mucosal Immunotherapy With Chimpanzee Adenovirus-Based Vaccine Accelerates Tuberculosis Disease Control and Limits Its Rebound After Antibiotic Cessation
Bibliographic record
Abstract
BACKGROUND: The development of strategies to accelerate disease resolution and shorten antibiotic therapy is imperative in curbing the global tuberculosis epidemic. Therapeutic application of novel vaccines adjunct to antibiotics represents such a strategy. METHODS: By using a murine model of pulmonary tuberculosis (TB), we have investigated whether a single respiratory mucosal therapeutic delivery of a novel chimpanzee adenovirus-vectored vaccine expressing Ag85A (AdCh68Ag85A) accelerates TB disease control in conjunction with antibiotics and restricts pulmonary disease rebound after premature (nonsterilizing) antibiotic cessation. RESULTS: We find that immunotherapy via the respiratory mucosal, but not parenteral, route significantly accelerates pulmonary mycobacterial clearance, limits lung pathology, and restricts disease rebound after premature antibiotic cessation. We further show that vaccine-activated antigen-specific T cells, particularly CD8 T cells, in the lung play an important role in immunotherapeutic effects. CONCLUSIONS: Our results indicate that a single-dose respiratory mucosal immunotherapy with AdCh68Ag85A adjunct to antibiotic therapy has the potential to significantly accelerate disease control and shorten the duration of conventional treatment. Our study provides the proof of principle to support therapeutic applications of viral-vectored vaccines via the respiratory route.
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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".