Lipid signaling and the modulation of surface charge during phagocytosis
Bibliographic record
Abstract
Phagocytosis is an important component of innate and adaptive immunity. The formation of phagosomes and the subsequent maturation that capacitates them for pathogen elimination and antigen presentation are complex processes that involve signal transduction, cytoskeletal reorganization, and membrane remodeling. Lipids are increasingly appreciated to play a crucial role in these events. Sphingolipids, cholesterol, and glycerophospholipids, notably the phosphoinositides, are required for the segregation of signaling microdomains and for the generation of second messengers. They are also instrumental in the remodeling of the actin cytoskeleton and in directing membrane traffic. They accomplish these feats by congregating into liquid-ordered domains, by generating active metabolites that activate receptors, and by recruiting and anchoring specific protein ligands to the membrane, often altering their conformation and catalytic activity. A less appreciated role of acidic phospholipids is their contribution to the negative surface charge of the inner leaflet of the plasmalemma. The unique negativity of the inner aspect of the plasma membrane serves to attract and anchor key signaling and effector molecules that are required to initiate phagosome formation. Conversely, the loss of charge that accompanies phospholipid metabolism as phagosomes seal facilitates the dissociation of proteins and the termination of signaling and cytoskeleton assembly. In this manner, lipids provide a binary electrostatic switch to control phagocytosis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".