Innate Lymphocytes in Psoriasis
Bibliographic record
Abstract
Skin is a fundamental component of our host defense system that provides a dynamic physical and chemical barrier against pathogen invasion and environmental insults. Cutaneous barrier function is mediated by complex interactions between structural cells such as keratinocytes and diverse lineages of immune cells. In contrast to the protective role of these intercellular interactions, uncontrolled immune activation can lead to keratinocyte dysfunction and psoriasis, a chronic inflammatory disease affecting 2% of the global population. Despite some differences between human and murine skin, animal models of psoriasiform inflammation have greatly informed clinical approaches to disease. These studies have helped to identify the interleukin (IL)-23-IL-17 axis as a central cytokine network that drives disease. In addition, they have led to the recent description of long-lived, skin-resident innate lymphocyte and lymphoid cells that accumulate in psoriatic lesions. Although not completely defined, these populations have both overlapping and unique functions compared to antigen-restricted αβ T lymphocytes, the latter of which are well-known to contribute to disease pathogenesis. In this review, we describe the diversity of innate lymphocytes and lymphoid cells found in mammalian skin with a special focus on αβ T cells, Natural Killer T cells and Innate Lymphoid cells. In addition, we discuss the effector functions of these unique leukocyte subsets and how each may contribute to different stages of psoriasis. A more complete understanding of these cell types that bridge the innate and adaptive immune system will hopefully lead to more targeted therapies that mitigate or prevent disease progression.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".