Organoid Models of Lymphoid Tissues
Bibliographic record
Abstract
Lymphoid organs are critical for organizing the development of the immune system, generating immune tolerance, and orchestrating the adaptive immune response to foreign antigens. Defects in their structure and function can lead to immunodeficiency, hypersensitivity, cancer, or autoimmune diseases. To better understand these diseases and assess potential therapies, complex models that recapitulate the anatomy and physiology of these tissues are required. Organoid models possess a number of advantages, including complex 3D microarchitecture, scalability, and personalization, which make them ideal for modelling lymphoid organs and related pathologies. Organoids have been developed for both primary and secondary lymphoid tissues; however, these models possess several limitations, including immature phenotypes and incomplete stromal cell populations. Furthermore, these organoids are often heterogeneous in both structure and function. Several lymphoid organs, such as the spleen, do not yet have robust organoid models, offering opportunities for breakthroughs in the field. Overall, development of lymphoid organoids will pave the way for the rapid development and testing of novel therapies, organ modelling, and personalized medicine. This review summarizes current advances in models for the primary lymphoid organ—bone marrow and thymus—as well as the secondary lymphoid organs of the lymph node and spleen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".