Dual roles of GM-CSF in modulating NK-cell migratory properties (CAM4P.147)
Bibliographic record
Abstract
Abstract Background: Natural Killer (NK) cells play a key role in innate immunity against viral, microbial infections and transformed cells and their migration for effector function to peripheral tissues or inflamed lymph nodes are tightly regulated. Of interest, production of Granulocyte-Macrophage Colony Stimulating Factor (GM-CSF) by cancer cells is correlated to host immune suppression and tumor metastasis, suggesting an immune evasion property of GM-CSF. Here we examined role(s) of recombinant GM-CSF in the regulation of NK-cell migratory properties in vitro. Methods:Previously published “Y” shape microfluidic platform was used to study the roles of GM-CSF gradient on NK-cell migrations. IL-2 activated human primary NK cells were used in the migration studies. Results:Our microfluidic-based migration study demonstrated a novel role of GM-CSF in regulating repulsive NK-cell migration under the stable GM-CSF gradient (at 20 ng/ml), followed by subsequent arrest in cell migration. Blocking of GM-CSF-Rα abolished the repulsive migratory behavior but not the arrest. In contrast, lower concentrations of GM-CSF induced hyper-polarization, immediate arrest of NK cells, and little/or no NK-cell migrations. Circularity measurement in controls and above experiments confirmed statistically the correlation between hyperpolarization and migration arrest. Future analyses will elucidate the mechanisms underlying the dual roles of GM-CSF in the regulation of NK-cell migratory properties.
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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".