Human cell‐based artificial antigen‐presenting cells for cancer immunotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 it