Targeting innate immunity for tuberculosis vaccination
Bibliographic record
Abstract
Vaccine development against tuberculosis (TB) is based on the induction of adaptive immune responses endowed with long-term memory against mycobacterial antigens. Memory B and T cells initiate a rapid and robust immune response upon encounter with Mycobacterium tuberculosis, thus achieving long-lasting protection against infection. Recent studies have shown, however, that innate immune cell populations such as myeloid cells and NK cells also undergo functional adaptation after infection or vaccination, a de facto innate immune memory that is also termed trained immunity. Experimental and epidemiological data have shown that induction of trained immunity contributes to the beneficial heterologous effects of vaccines such as bacille Calmette-Guérin (BCG), the licensed TB vaccine. Moreover, increasing evidence argues that trained immunity also contributes to the anti-TB effects of BCG vaccination. An interaction among immunological signals, metabolic rewiring, and epigenetic reprogramming underlies the molecular mechanisms mediating trained immunity in myeloid cells and their bone marrow progenitors. Future studies are warranted to explore the untapped potential of trained immunity to develop a future generation of TB vaccines that would combine innate and adaptive immune memory induction.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".