Bibliographic record
Abstract
Phagocytosis is a part of immune response. IgG opsonized particles of greater than 1 um are recognized by Fcy receptors on the surface of professional phagocytes such as macrophages and neutrphils. IgGs are part of the immune system and is a cognate ligand of the Fc receptor. Live Cell Affinity Receptor chromatography (LARC) was used to capture an activated Fcy receptor supramolecular complex from the surface of live human neutrophils, by allowing IgG opsonized microbeads to bind to the cell surface. The cells were burst in PBS, collected and digested along side with controls. Isolated FcyR complex was analysed by LC-MS/MS. Fc and control experiment lists of SEQUEST correlated proteins were screened for a total cumulative score of at least 2400 and a minimum of three different peptides. This served as the basis of protein involvement in the FcyR mediated phagocytosis, which were then searched with iHOP for their interaction partners. Gathered interactions were then exported and Cytoscape, Osprey and String algorithms were used to generate network of interacting proteins. PAKs2-4 and PAK6 were detected with LARC. PAK2 and PAK4 were predicted by algorithms to have a central role in particle uptake. From Western Blotting, endogenous PAKs2-4 and PAK6 were detected in murine macrophages. Immunofluorescent staining was then used to verify the presence of these proteins in the forming phagosome and showed localization of PAKs to the phagosome. The same effect was observed with transfection of GFP constructs of PAKs. Upon transfection with dominant negative PAKs reduction in phagocytosis was observed.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".