Mechanisms of lymphocyte migration in autoimmune disease
Bibliographic record
Abstract
The recruitment of leukocytes to inflamed tissues plays an essential role in combating infection and promoting wound healing. However, in autoimmune diseases such as multiple sclerosis and diabetes, leukocytes enter tissues and contribute to inappropriate inflammatory responses, which cause tissue injury and dysfunction. In diseases of this type, lymphocytes play critical roles in initiating and maintaining these aberrant inflammatory responses. The aim of this review is to examine the mechanisms whereby T-lymphocytes enter tissues in autoimmune diseases and to compare these mechanisms between various organs and diseases. An overview of the mechanisms of leukocyte recruitment and the techniques used to study leukocyte trafficking is provided, focusing on the use of intravital microscopy as a tool to assess the functional microvasculature in vivo. We also discuss the series of tissue homing events which allow naïve lymphocytes to first enter lymph nodes and undergo activation, then subsequently to home to the peripheral organ where their cognate antigen is present. Finally, we examine mechanisms of leukocyte recruitment in diseases such as multiple sclerosis, autoimmune diabetes, systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease and asthma.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".