Better Together: Current Insights Into Phagosome-Lysosome Fusion
Bibliographic record
Abstract
Following phagocytosis, the nascent phagosome undergoes maturation to become a phagolysosome with an acidic, hydrolytic, and often oxidative lumen that can efficiently kill and digest engulfed microbes, cells, and debris. The fusion of phagosomes with lysosomes is a principal driver of phagosomal maturation and is targeted by several adapted intracellular pathogens. Impairment of this process has significant consequences for microbial infection, tissue inflammation, the onset of adaptive immunity, and disease. Given the importance of phagosome-lysosome fusion to phagocyte function and the many virulence factors that target it, it is unsurprising that multiple molecular pathways have evolved to mediate this essential process. While the full range of these pathways has yet to be fully characterized, several pathways involving proteins such as members of the Rab GTPases, tethering factors and SNAREs have been identified. Here, we summarize the current state of knowledge to clarify the ambiguities in the field and construct a more comprehensive phagolysosome formation model. Lastly, we discuss how other cellular pathways help support phagolysosome biogenesis and, consequently, phagocyte function.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.003 |
| 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".