Bibliographic record
Abstract
T cell immunotherapies are in a rapidly evolving field that aim to provide durable and curative therapies which have found recent clinical successes in treating haematological malignancies. The scope of T cell immunotherapies continues to expand to other indications, but current standards are limited by the dependency on autologous cell sources for T cell manufacturing. In vitro T cell differentiation takes various stem cell sources and converts them into large numbers of differentiated T cells. This technology has the potential to provide a readily available and sustainable source of cell material compared to autologous sources. Our current capabilities using the OP9-Delta-like 4 (DL4) cell co-culture system, a 2-dimensional monolayer culture, produces the early gamut of T cell progenitors as well as functionally mature cytotoxic CD8 T cells, but lacks the generation of helper CD4 T cells. The inability to generate helper CD4 T cells is a roadblock to realizing the full therapeutic potential of in vitro T cell differentiation. Here, I utilize retro- and lenti-viral expression systems to demonstrate the generation of CD4 single positive T cells in OP9-DL4 co-cultures through the overexpression of the CD4 T cell master regulator, ThPOK, encoded by the Zbtb7b gene. The results from this study provide initial support for the viability of generating human CD4 T cells using the OP9-DL4 cell system.
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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) | 0.004 | 0.002 |
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".