The Regulation of Immune Responses by DC Derived Type I IFN
Bibliographic record
Abstract
Our immune system bears the tremendous task of mounting effective anti-microbial responses whilst maintaining immunoregulatory functions to avoid autoimmunity. In order to quickly respond to pathogens, Dendritic cells (DC) are armed with pattern recognition receptors (PRRs), allowing them to recognize highly conserved pathogen-associated molecular patterns (PAMPs) that are uniquely expressed by invading microbes. PRR activation can trigger DCs to release the pleiotropic cytokine, Type I interferons (IFN), which facilitates various biological functions in different immune cell types. In this review, we will discuss the classical PRR-induced Type I IFN response in DCs as well as describe a novel mechanism for Type I IFN induction by the tumor-necrosis factor receptor superfamily (TNFRSF) members, TNFR-1 and lymphotoxin-β receptor (LTβR). While PRR activation during viral infection, produces large amounts of Type I IFN in a relative short period of time, TNFRSF-induced Type I IFN expression is modest with gradual kinetics. Type I IFN can exert pro-inflammatory effects, but in some cases it also facilitates immune-regulatory functions. Therefore, DCs are important regulators of immune responses by carefully modulating Type I IFN expression.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".