Targeting innate immunity for tuberculosis vaccination
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
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.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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