T cells generated in the absence of a thoracic thymus fail to establish homeostasis
Bibliographic record
Abstract
Cervical thymus mimics the thoracic thymus in supporting T-cell development and exists in a subset of mice and humans. Importantly, it remains unknown whether the cervical thymus can generate T cells that are self-tolerant in the complete absence of signals from the thoracic thymus. Using a fetal liver reconstitution model in thoracic thymectomized RAG(-/-) mice, we found that T cells could be generated without contribution from the thoracic thymus. However, these mice had decreased T cells, increased proportions of effector memory T cells and Treg phenotype cells, increased serum IgG1/2b, and increased frequency of T cells expressing IFN-γ, IL-17 or IL-10. Half of the mice that received a thoracic thymectomy and fetal liver cells, unlike sham surgery controls, developed substantial morbidity with age. Disease was associated with lymphopenia-driven activation rather than inherent defects in the cervical thymus, as both thoracic and cervical thymocytes could generate disease in lymphopenic recipients. Administration of the homeostatic cytokine IL-7 caused a rapid, transient increase in T-cell numbers and reduced the time to disease onset. Together the data suggests that the cervical thymus can function in the complete absence of the thoracic thymus; however, the T cells generated do not establish homeostasis.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".