Trained Immunity Affecting Dendritic Cell Differentiation and Function in Rheumatoid Arthritis
Bibliographic record
Abstract
Abstract Rheumatoid arthritis affects ∼0.5-1% of the adult population and results in joint inflammation, chronic pain, and many systemic comorbidities. Immune and inflammatory tissue damage is the main pathogenic mechanism in rheumatoid arthritis, and involves the hyperactivation of both innate and adaptive immune systems. Trained immunity has become well established as an important feature of the innate immune system that allows the host to mount functionally altered immune responses based on their previous history of immune exposures, independently of the classical adaptive immunological memory. However, the role of trained immunity in systemic autoimmune and inflammatory disorders remains poorly understood. In the current work, we demonstrate that emergency myelopoiesis is induced in chronic rheumatoid arthritis in murine models and acts not only to enhance innate immune cell numbers but also to produce functionally altered innate immune cells. Such effects are cell-intrinsic to hematopoietic stem and progenitor cells (HSPCs) and persist independently of the inflammatory disease milieu. Importantly, these trained immunity mechanisms impact not only macrophages but also dendritic cells, which are the major antigen presenting cells that bridge the innate and adaptive immune responses. ‘Trained’ dendritic cells show changes in global gene expression profiles, and significantly altered responses to recall immune stimulation and capacity for T cell activation. This study therefore represents the first demonstration of ‘dendritic cell trained immunity’ in rheumatoid arthritis.
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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".