A new therapeutic potential for cancers: One CAR with 2 different engines!
Bibliographic record
Abstract
Tumor cells escape from immune recognition by several mechanisms such as down-regulating of MHC class I molecules, losing of tumor antigens, etc. The purpose of cancer immunotherapy is to robust or reconstruct the capacity of the immune system to recognize and kill tumor cells by overwhelming the mechanisms by which tumors escape the immune response. One of the novel immunotherapeutic strategies were used to potentiate NK- and T cell functions is chimeric antigen receptor (CAR). CARs are composed of an antigen-binding domain of a molecule such as an antibody (that binds to a tumor associated antigens expressed on the surface of tumor cells) and an intracellular T cell activation domain. The CARs provide the recognition of target antigen in a MHC-independent manner. CAR-armed T cells may be unable to kill their targets in the absence of co-stimulators like NK cells. On the other hand, CAR-armed NK cells may also be unable to destroy their targets without receiving help signals from Th cells. Thus, if CAR-armed NK cells use together with CAR-armed T cells, NK cells will be aggregated to the tumor site. Thus, not only CAR T cells will obtain the necessary cytokines/costimulators from NK cells, but also other tumor specific T cells will be primed by recognition of tumor specific antigen (TSA) associated with MHC class I. These new specific primed T cells probably combat against tumor cells which have lost their TAAs that CAR-T cells are redirected to them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".