The Hunt for New Tuberculosis Vaccines: Anti-TB Immunity and Rational Design of Vaccines
Bibliographic record
Abstract
Tuberculosis (TB) remains to be a leading infectious cause of death worldwide. Apparently, the current BCG vaccine that has been used for 80 years, has failed to control the TB epidemic. Hunting for improved TB vaccine formulations represents a daunting task to TB research community. Anti-TB host defense requires T cell-mediated immunity and we are in desperate need of enhanced understanding of how to develop a new generation of TB vaccines that are able to provoke potent and long-lasting protective cell-mediated immunity, different from almost all of the vaccines currently in use. It is of importance to successful TB vaccine development to identify the key cellular and molecular events governing the generation of anti-TB immunity, but unfortunately little has been understood as to why 90% of infected humans never develop active TB. However, waiting would not help us to win the battle and an ever-intensifying effort is being made to develop various new formulations according to the immunology that we have been learning, in large part, from experimental models. This review article attempts to unite the current understanding of anti-TB immunity with the rational design of anti-TB vaccines. It examines what may have confounded the immunogenicity of current BCG vaccine and the major obstacles to successful development of TB vaccines. It also discusses about antigen presentation, activation of Th1 and Tc1 cells, anti-TB immune effectors and the generation of memory T cells. The vaccine section describes four types of major TB vaccines under development: mycobacterial-, subunit-, plasmid DNA- and viral-based vaccines. A special section is dedicated to the rationale and current design of cytokine-based adjuvant formulations for TB vaccines. We also take this opportunity to introduce our recent development in cytokine transgene adjuvanted BCG vaccination and recombinant adenoviral-based TB vaccines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".