Human cell‐based artificial antigen‐presenting cells for cancer immunotherapy
Bibliographic record
Abstract
Adoptive T-cell therapy, where anti-tumor T cells are first prepared in vitro, is attractive since it facilitates the delivery of essential signals to selected subsets of anti-tumor T cells without unfavorable immunoregulatory issues that exist in tumor-bearing hosts. Recent clinical trials have demonstrated that anti-tumor adoptive T-cell therapy, i.e. infusion of tumor-specific T cells, can induce clinically relevant and sustained responses in patients with advanced cancer. The goal of adoptive cell therapy is to establish anti-tumor immunologic memory, which can result in life-long rejection of tumor cells in patients. To achieve this goal, during the process of in vitro expansion, T-cell grafts used in adoptive T-cell therapy must be appropriately educated and equipped with the capacity to accomplish multiple, essential tasks. Adoptively transferred T cells must be endowed, prior to infusion, with the ability to efficiently engraft, expand, persist, and traffic to tumor in vivo. As a strategy to consistently generate T-cell grafts with these capabilities, artificial antigen-presenting cells have been developed to deliver the proper signals necessary to T cells to enable optimal adoptive cell therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".