Key players for T-cell regeneration
Bibliographic record
Abstract
PURPOSE OF REVIEW: The thymus provides a unique and essential microenvironment for T-cell precursors to develop into mature functionally competent T lymphocytes. Ageing causes architectural changes in the thymus resulting in a loss of thymic epithelial space required for thymopoiesis - a process known as thymic involution. Additionally, cytoablative regimens used to treat malignancies also destroy thymic architecture. The net result of both processes is diminished thymic output and function that may lead to impaired immunity. Thus, immunocompromised individuals would benefit from strategies aimed at enhancing T-cell reconstitution. RECENT FINDINGS: Here we discuss strategies such as the use of sex steroid ablation, keratinocyte growth factor, interleukin-7, and in-vitro-generated progenitor T cells as candidates for restoring T-cell immunity. Using various animal models of ageing or hematopoietic stem cell transplantation, these strategies have been shown to restore thymic architecture and cellularity, resulting in increased output and T-cell function in the periphery. SUMMARY: These candidate approaches are currently being tested in clinical trials, with preliminary evidence showing encouraging effects on T-cell reconstitution. Nevertheless, although these strategies show clear promise in animal models, and in early human trials, further data are needed to determine their efficacy in patients.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".