Regulation of dendritic cell function by Notch receptors and ligands (172.40)
Bibliographic record
Abstract
Abstract Cells of the innate immune system, such as Dendritic Cells (DCs), are the first responders and play a key role in creating pathogen-specific microenvironments through the generation of secreted mediators including cytokine, chemokines and lipids, and through modification of cell phenotype. T cells respond to the different activation phenotypes of DCs and in turn differentiate accordingly into distinct effector types, designated as lineages. Differentiation down different lineages is driven by “signal 3” or differentiation cues provided by the microenvironment. We have previously shown that signal 3 provided by DCs is a powerful regulator of T cell function not only at the time of activation, but during differentiation and recall responses. Notch ligand expression on DCs has been shown to be differentially regulated in response to various stimuli such as bacteria, worms, and allergens. Because Notch ligands show distinct and correlative expression patters in response to stimulation with various activators it is enticing to speculate they play a role in inducing or propagating differential immune responses. However their role remains controversial. Notch receptors are also expressed on DCs and their role in regulating DC function is just emerging. We will present evidence that Notch receptors participate in the activation of DCs. Furthermore, we propose that Notch ligands may indirectly influence Th differentiation through direct modification of DC function.
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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) | 0.002 | 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".