Cytokine/Stromal Cell Networks and Lymphoid Tissue Environments
Bibliographic record
Abstract
Initiation of an effective adaptive immune response against a foreign pathogen requires orchestrated encounters between lymphocytes and antigen-presenting cells. The tissues of the lymphoid system provide the ideal environment for increasing the efficiency of these encounters. Within the spleen, the mucosal-associated lymphoid tissues, and the lymph nodes, an intricate network of stromal cells, collagen fibers, and extracellular matrix exists that effectively compartmentalizes immune cells as they transit through these tissues. The stromal cells within lymphoid tissues are by no means homogenous, and it is now clear that these cells are not merely sessile bystanders during immune responses. Indeed, stromal cells within lymphoid tissues are the source of important cytokines and chemokines that guide and polarize immune cells. Here, we review the cytokines that maintain the integrity of this important stromal scaffold system within the lymphoid tissue, paying particular attention to the Lymphotoxin pathway, which is an important player in stromal cell biology. How cytokines maintain the organization of lymphoid tissues during development, in the adult animal, during inflammation and during disease will be discussed in sequence, and the clinical implications of targeting cytokines that regulate lymphoid tissue stroma will be considered.
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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 | 0.001 |
| 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".