Airway Macrophages Mediate Mucosal Vaccine–Induced Trained Innate Immunity against <i>Mycobacterium tuberculosis</i> in Early Stages of Infection
Bibliographic record
Abstract
Abstract Mycobacterium tuberculosis, the causative agent of pulmonary tuberculosis (TB), is responsible for millions of infections and deaths annually. Decades of TB vaccine development have focused on adaptive T cell immunity, whereas the importance of innate immune contributions toward vaccine efficacy has only recently been recognized. Airway macrophages (AwM) are the predominant host cell during early pulmonary M. tuberculosis infection and, therefore, represent attractive targets for vaccine-mediated immunity. We have demonstrated that respiratory mucosal immunization with a viral-vectored vaccine imprints AwM, conferring enhanced protection against heterologous bacterial challenge. However, it is unknown if innate immune memory also protects against M. tuberculosis. In this study, by using a murine model, we detail whether respiratory mucosal TB vaccination profoundly alters the airway innate immune landscape associated with AwM prior to M. tuberculosis exposure and whether such AwM play a critical role in host defense against M. tuberculosis infection. Our study reveals an important role of AwM in innate immune protection in early stages of M. tuberculosis infection in the lung.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".