Bibliographic record
Abstract
CRISPR/Cas9 technology has brought revolution to the field of gene editing, offering precise and efficient tools for genetic modifications. One of its most promising applications lies in improving CAR-T cell therapy for treating hematological malignancies. Among approaches to treating blood cancers, CAR-T therapy, which programs T cells to recognize and eliminate cancer cells, has shown some success for B-cell malignancies such as diffuse large B-cell lymphoma (DLBCL) and B-cell acute lymphoblastic leukemia (ALL). However, challenges such as immune evasion, cytokine release syndrome (CRS), neurotoxicity, and limited CAR-T persistence remain significant barriers. CRISPR/Cas9 can optimize CAR-T therapy by precisely inserting CAR genes into specific loci, knocking out inhibitory genes like PD-1 to enhance persistence, and enabling multi-targeting strategies to overcome tumor immune escape. Clinical trials demonstrate the feasibility and potential of CRISPR-edited CAR-T cells, showing improved safety, durability, and efficacy. This study explores the synergistic application of CRISPR/Cas9 in CAR-T therapy, addressing its current limitations and providing a pathway to safer and more effective treatments for hematological malignancies
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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) | 0.002 | 0.000 |
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".