Strategies Used by Bacteria to Grow in Macrophages
Bibliographic record
Abstract
Intracellular bacteria are often clinically relevant pathogens that infect virtually every cell type found in host organisms. However, myeloid cells, especially macrophages, constitute the primary cells targeted by most species of intracellular bacteria. Paradoxically, macrophages possess an extensive antimicrobial arsenal and are efficient at killing microbes. In addition to their ability to detect and signal the presence of pathogens, macrophages sequester and digest microorganisms using the phagolysosomal and autophagy pathways or, ultimately, eliminate themselves through the induction of programmed cell death. Consequently, intracellular bacteria influence numerous host processes and deploy sophisticated strategies to replicate within these host cells. Although most intracellular bacteria have a unique intracellular life cycle, these pathogens are broadly categorized into intravacuolar and cytosolic bacteria. Following phagocytosis, intravacuolar bacteria reside in the host endomembrane system and, to some extent, are protected from the host cytosolic innate immune defenses. However, the intravacuolar lifestyle requires the generation and maintenance of unique specialized bacteria-containing vacuoles and involves a complex network of host-pathogen interactions. Conversely, cytosolic bacteria escape the phagolysosomal pathway and thrive in the nutrient-rich cytosol despite the presence of host cell-autonomous defenses. The understanding of host-pathogen interactions involved in the pathogenesis of intracellular bacteria will continue to provide mechanistic insights into basic cellular processes and may lead to the discovery of novel therapeutics targeting infectious and inflammatory diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".