Strategies Used by Bacteria to Grow in Macrophages
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 it