Review on Anti-CD19 CAR T Therapy against B-Cell Malignancies and Future Implications
Bibliographic record
Abstract
This review examines CAR T CD19 immunotherapy, a newly FDA approved targeted therapy for B-Cell Acute Lymphoblastic Leukemia treatment. This therapy utilizes modified T cells from the patient's immune system, engineered to possess an anti-CD19 receptor that can recognize the specific CD19 antigen expressed on the surface of malignant B-lymphocytes. Using this highly individualized treatment, cancer types with a high rate of metastasis or relapse can be treated by the targeted nature of this therapy. The review aims to summarize the process through which CAR T was developed, from its inception to FDA approval. The material examined is current until March 2019 and explores the mechanisms and management of CAR T cell toxicity experienced by patients undergoing treatment. Clinical trials from respective stages of development are also detailed and summarized. The viable treatment options for patients suffering from B-cell acute lymphoblastic leukemia (B-ALL) are outlined. Despite the promising remission rates of CAR T therapy, its accessibility is limited due to current cost of treatment. With advancements in technology and improved understanding of immune-based therapies, it is possible that this method can become a more viable and affordable treatment option for patients in the future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".