Universal CAR T cell: engineering of universal T cell, modular CAR system, and applications
Bibliographic record
Abstract
Universal Chimeric Antigen Receptor T cells (CAR T), an alternative design based on conventional CAR T cells, uses a switchable adaptor for a better redirection towards the target site. This technology overcomes the obstacles of the conventional Car T cells system, such as immunogenicity, massive expression of cytokine and fixed antigen specificity. This article introduces universal CAR T cells from both the perspectives of the universal T cells and its modular CAR systems, illustrating the advancement of universal CAR T cells to overcome the limitation of conventional CAR T cells and serve as a more controllable and highly promising system. The universal CAR T cells section focuses on the challenges of choosing T cell sources and the corresponding solutions, while the modular CAR system section summarizes the different types of switchable adaptors in combination with clinical applications in various types of cancer treatments. Overall, universal CAR T cells therapy is a novel development that not only out-competes but also recovers the shortage of the conventional CAR T cells system. With the use of switchable adaptors, the universal CAR T cells system is commercially beneficial for the public and a safe product to allow the industry to expand the clinical application of different types of cancers.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".