Alveolar Epithelial Cells in <b><i>Mycobacterium tuberculosis</i></b> Infection: Active Players or Innocent Bystanders?
Bibliographic record
Abstract
Tuberculosis (TB) is a disease that kills one person every 18 s. TB remains a global threat due to the emergence of drug-resistant Mycobacterium tuberculosis (M.tb) strains and the lack of an efficient vaccine. The ability of M.tb to persist in latency, evade recognition following seroconversion, and establish resistance in vulnerable populations warrants closer examination. Past and current research has primarily focused on examination of the role of alveolar macrophages and dendritic cells during M.tb infection, which are critical in the establishment of the host response during infection. However, emerging evidence indicates that the alveolar epithelium is a harbor for M.tb and critical during progression to active disease. Here we evaluate the relatively unexplored role of the alveolar epithelium as a reservoir and also its capacity to secrete soluble mediators upon M.tb exposure, which influence the extent of infection. We further discuss how the M.tb-alveolar epithelium interaction instigates cell-to-cell crosstalk that regulates the immune balance between a proinflammatory and an immunoregulatory state, thereby prohibiting or allowing the establishment of infection. We propose that consideration of alveolar epithelia provides a more comprehensive understanding of the lung environment in vivo in the context of host defense against M.tb.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".