The Atypical Zeta (ζ) Isoform of Protein Kinase C Regulates CD11b/CD18 Activation in Human Neutrophils
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to examine the role of protein kinase C zeta (PKCzeta) in interleukin (IL)-8-mediated activation of Mac-1 (CD11b/CD18) in human neutrophils. MATERIALS AND METHODS: Neutrophils were stimulated with IL-8 in the presence or absence of pharmacologic inhibitors of PKC or a myristoylated PKCzeta pseudosubstrate. The resulting changes in Mac-1 surface expression, affinity, and avidity, as measured by clustering, were determined by using a combination of flow cytometry and immunofluorescence (IF). Colocalization of Mac-1 with PKCzeta was also probed using IE Finally, neutrophil adhesion to matrix proteins was examined under static conditions and adhesion to tumor necrosis factor-alpha-stimulated human umbilical vein endothelial cells was examined under flow conditions, using a parallel-plate flow chamber RESULTS: PKCzeta and Mac-1 colocalized following stimulation with IL-8. Blocking PKCzeta prevented IL-8-induced Mac-1 clustering while simultaneously increasing Mac-1 affinity. To determine the relative contribution of affinity versus avidity in neutrophil adhesion, we examined adhesion under both static and flow conditions, and found that blocking PKCzeta prevented neutrophil adhesion, despite increased affinity of Mac-1. CONCLUSIONS: These data suggest that PKCzeta is a negative regulator of Mac-1 affinity and a positive regulator of Mac-1 avidity. Further, Mac-1 avidity is more important than increased affinity alone in regulating neutrophil firm adhesion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".