Complexity of dendritic cell subsets and their function in the host immune system
Bibliographic record
Abstract
Dendritic cells (DCs) are professional antigen-presenting cells that are critical for induction of adaptive immunity and tolerance. Traditionally DCs have been divided into two discrete subtypes, which comprise conventional and non-conventional DCs. They are distributed across various organs in the body and comprise a heterogeneous population, which has been shown to display differences in terms of surface marker expression, function and origins. Recent studies have shed new light on the process of DC differentiation and distribution of DC subtypes in various organs. Although monocytes, macrophages and DCs share a common macrophage-DC progenitor, a common DC progenitor population has been identified that exclusively gives rise to DCs and not monocytes or macrophages. In this review, we discuss the recent advances in our understanding of DC differentiation and subtypes and provide a comprehensive overview of various DC subtypes with emphasis on their function and origins. Furthermore, in light of recent developments in the field of DC biology, we classify DCs based on the precursor populations from which the various DC subsets originate. We classify DCs derived from common DC progenitor and pre-DC populations as conventional DCs, which includes both migratory and lymphoid-resident DC subsets and classify monocyte-derived DCs and plasmacytoid DCs as non-conventional DCs.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".