Phagosome–Bacteria Interactions from the Bottom Up
Bibliographic record
Abstract
When attempting to propagate infections, bacterial pathogens encounter phagocytes that encase them in vacuoles called phagosomes. Within phagosomes, bacteria are bombarded with a plethora of stresses that often lead to their demise. However, pathogens have evolved numerous strategies to counter those host defenses and facilitate survival. Given the importance of phagosome-bacteria interactions to infection outcomes, they represent a collection of targets that are of interest for next-generation antibacterials. To facilitate such therapies, different approaches can be employed to increase understanding of phagosome-bacteria interactions, and these can be classified broadly as top down (starting from intact systems and breaking down the importance of different parts) or bottom up (developing a knowledge base on simplified systems and progressively increasing complexity). Here we review knowledge of phagosomal compositions and bacterial survival tactics useful for bottom-up approaches, which are particularly relevant for the application of reaction engineering to quantify and predict the time evolution of biochemical species in these death-dealing vacuoles. Further, we highlight how understanding in this area can be built up through the combination of immunology, microbiology, and engineering.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".