Functions of reconstituted T cells in vitro by grafting with mixed fetal thymus in nude mice
Bibliographic record
Abstract
Objective To establish the model of mixed thymus transplantation by grafting with fetal F344 rat thymus and BALB/c mouse thymus in nude mice and to observe the functions of the reconstituted T cells in the recipients. Methods Transplantation model was established by implantation of the mixed fetal thymic tissue under the kidney capsule in nude mice. The reconstitution of T cell subsets in peripheral blood of the recipients was detected by flow cytometry. The in vitro functional status of the reconstituted T cells in the nude mice and mouse splenocytes was stimulated with Con A mitogens, allogeneic and xenogeneic splenocytes. Results At 3 months after mixed thymus transplantation, efficient reconstitution of mouse CD4 +T cells and CD8 +T cells in the peripheral blood was observed. The levels of mouse CD4 +T cells and CD8+T cells in peripheral white blood cells were similar to those of the normal euthymic mice. Con A stimulation and mixed lymphocyte reaction assay revealed that mouse lymphocytes showed a strong response to either the stimulation of allogeneic (C57BL/6 mouse) cells or Con A mitogens, and were tolerant for donor F344 rat antigens. Conclusion Mixed thymus transplantation can support the reconstitution of CD4 +T cells and CD8 +T cells in nude mice. Furthermore, the function of reconstituted T cells is normal in vitro .
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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 | 0.000 | 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".