Differential effects of C5a on human and mouse mast cells may be mediated by C5aR and C5L2
Bibliographic record
Abstract
Human MC (LAD2) and bone marrow-derived MC (BMMC) were stimulated with C5a and degranulation was determined by measurement of β-hexosaminidase release. BMMC were cultured from bone marrow of wild type and CL52 knock-out mice in interleukin-4 (300U/ml) and stem cell factor (50ng/ml). Tumor necrosis factor (TNF), granulocyte macrophage colony-stimulating factor (GM-CSF), monocyte chemotactic protein-1 (MCP-1) and interferon-inducible protein-10 (IP-10) production was measured by cytometric bead array. Expression of C5aR and C5L2 receptors was analyzed by quantitative PCR (qPCR) and flow cytometry. C5a (100ng/ml) stimulated LAD2 degranulation (25%) and production of TNF (22±1.3pg/ml), GM-CSF (15±0.4pg/ml), MCP-1 (53±3.6pg/ml) and IP-10 (32±4.3pg/ml). qPCR showed that LAD2 expressed mRNA for C5a and C5L2 and flow cytometry showed that LAD2 expressed surface C5L2 but not C5aR. BMMC from wild type mice expressed both C5aR and C5L2 but did not degranulate in response to C5a. However, BMMC from C5L2 knock-out mice expressed C5aR but not C5L2 and degranulated (14.5±0.8%). LAD2 express C5L2 but not C5aR and respond to C5a by releasing granule contents, cytokines and chemokines suggesting that C5L2 is an excitatory receptor in human MC. BMMC expressing C5aR but lacking C5L2 degranulate in response to C5a suggesting that C5L2 may function as a decoy receptor in mouse MC. These data show the potential importance and complexity of C5a and its two receptors in immune responses.
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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) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".