Dynamic life and death interactions between Mycobacterium smegmatis and J774 macrophages
Bibliographic record
Abstract
After internalization into macrophages non-pathogenic mycobacteria are killed within phagosomes. Pathogenic mycobacteria can block phagosome maturation and grow inside phagosomes but under some conditions can also be killed by macrophages. Killing mechanisms are poorly understood, although phago-lysosome fusion and nitric oxide (NO) production are implicated. We initiated a systematic analysis addressing how macrophages kill 'non-pathogenic'Mycobacterium smegmatis. This system was dynamic, involving periods of initial killing, then bacterial multiplication, followed by two additional killing stages. NO synthesis represented the earliest killing factor but its synthesis stopped during the first killing period. Phagosome actin assembly and fusion with late endocytic organelles coincided with the first and last killing phase, while recycling of phagosome content and membrane coincided with bacterial growth. Phagosome acidification and acquisition of the vacuolar (V) ATPase followed a different pattern coincident with later killing phases. Moreover, V-ATPase localized to vesicles distinct from classical late endosomes and lysosomes. Map kinase p38 is a crucial regulator of all processes investigated, except NO synthesis, that facilitated the host for some functions while being usurped by live bacteria for others. A mathematical model argues that periodic high and low cellular killing activity is more effective than is a continuous process.
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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 | 0.000 | 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 | 0.000 |
| 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 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".