Thymic Microenvironment: Interactions Between Innate Immune Cells and Developing Thymocytes
Bibliographic record
Abstract
The thymus is a crucial organ for the development of T cells. T cell progenitors first migrate from the bone marrow into the thymus. During the journey to become a mature T cell, progenitors require interactions with many different cell types within the thymic microenvironment, such as stromal cells, which include epithelial, mesenchymal and other non-T-lineage immune cells. There are two crucial decision steps that are required for generating mature T cells: positive and negative selection. Each of these two processes needs to be performed efficiently to produce functional MHC-restricted T cells, while simultaneously restricting the production of auto-reactive T cells. In each step, there are various cell types that are required for the process to be carried out suitably, such as scavengers to clean up apoptotic thymocytes that fail positive or negative selection, and antigen presenting cells to display self-antigens during positive and negative selection. In this review, we will focus on thymic non-T-lineage immune cells, particularly dendritic cells and macrophages, and the role they play in positive and negative selection. We will also examine recent advances in the understanding of their participation in thymus homeostasis and T cell development. This review will provide a perspective on how the thymic microenvironment contributes to thymocyte differentiation and T cell maturation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".