Intrinsic Defects in the T-Cell Lineage Results in Natural Killer T-Cell Deficiency and the Development of Diabetes in the Nonobese Diabetic Mouse
Bibliographic record
Abstract
T-cell-mediated autoimmune diabetes in nonobese diabetic (NOD) mice is closely associated with natural killer T (NKT)-cell deficiency. To determine whether intrinsic defects of the T-cell lineage contribute to the pathogenesis of the disease and NKT cell deficiency, we reconstituted the T-cell compartment in NOD.scid or BALB.scid mice with T-cells from NOD, nonobese diabetes-resistant (NOR), or AKR thymic precursor cells and examined the development of the NKT cell population. NKT cells developed well from AKR thymic precursor cells but not from other precursor cells in both recipient strains. Insulitis and diabetes developed only in the NOD.scid recipients of NOD or NOR precursor cells. When thymic precursor cells of beta2-microglobulin gene-deficient AKR mice, which have a deficient NKT population, were introduced into NOD.scid recipients, both CD4(+) and CD8(+) T-cell populations developed and the recipient mice developed insulitis and diabetes. We conclude that NKT cells originate from a T-cell-committed thymic precursor population and that the deficiency in the NKT cell population in NOD mice results from intrinsic defects within the T-cell lineage and plays a major role in the development of autoimmune diabetes in the presence of both the NOD thymus and antigen-presenting cells.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 0.000 |
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".