Molecular programming of steady‐state dendritic cells: impact on autoimmunity and tumor immune surveillance
Bibliographic record
Abstract
Dendritic cells are master regulators of immunity. Immature dendritic cells are essential for maintaining self-tolerance, while mature dendritic cells initiate a variety of specialized immune responses. Dendritic cell quiescence is often viewed as a default state that requires exogenous stimuli to induce maturation. However, recent studies have identified dendritic cell quiescence factors that actively program dendritic cells to an immature state. In the absence of these factors, dendritic cells spontaneously become immunogenic and can induce autoimmune responses. Herein we discuss two such factors, NF-κB1 and A20, that preserve dendritic cell immaturity through their regulation of NF-κB signaling. Loss of either of these factors increases dendritic cell immunogenicity, suggesting that they may be important targets for enhancing dendritic cell-based cancer immunotherapies. Alternatively, defects in molecules critical for maintaining steady-state DCs may provide novel biomarkers that identify patients who have enhanced natural antitumor immunity or that correlate with better responses to various immunotherapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".