Bibliographic record
Abstract
Irrespective of the immune status, the vast majority of all lymphocytes reside in peripheral tissues whereas those present in blood only amount to a small fraction of the total. It has been estimated that T cells in healthy human skin outnumber those present in blood by at least a factor of two. How lymphocytes within these two compartments relate to each other is not well understood. However, mounting evidence suggest that the study of T cell subsets present in peripheral blood does not reflect the function of their counterparts at peripheral sites. This is especially true under steady-state conditions whereby long-lived memory T cells in healthy tissues, notably those in epithelial tissues at body surfaces, are thought to fulfill a critical immune surveillance function by contributing to the first line of defense against a series of local threats, including microbes, tumors, and toxins, and by participating in wound healing. The relative scarcity of information regarding peripheral T cells and the factors regulating their localization is primarily due to inherent difficulties in obtaining healthy tissue for the extraction and study of immune cells on a routine basis. This is most certainly true for humans. Here, we review our current understanding of T cell homing to human skin and compare it when possible with gut-selective homing. We also discuss candidate chemokines that may account for the tissue selectivity in this process and present a model whereby CCR8, and its ligand CCL1, selectively regulate the homeostatic migration of memory lymphocytes to skin tissue.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".