Advancing CD19 CAR T Cell Therapy for Treatment of Primary Biliary Cholangitis
Bibliographic record
Abstract
Abstract CD19 Chimeric Antigen Receptor (CAR) T cell therapy is emerging as a revolutionary approach in autoimmune disease management, offering a promising alternative to conventional treatments like anti-CD20 antibody therapy. Unlike challenges associated with existing therapies, CD19 CAR T cells, engineered to recognize CD19 molecules on B cells, exhibit remarkable efficacy in achieving targeted B cell depletion. Upon target recognition, they become activated, efficiently eliminating the harmful B cells. Furthermore, the CAR T cells exhibit a propensity for proliferation upon target recognition, thereby ensuring their prolonged presence within the body. Our findings unequivocally establish the effectiveness of CD19 CAR T cells in specifically targeting and depleting B cells, both in vitro and in vivo. In a well-established PBC model involving NOD.c3 mice, the application of CD19 CAR T cell therapy led to a remarkable reduction in liver pathology scores, indicating a substantial decrease in tissue damage and inflammation compared to control groups. Beyond liver autoimmunity, our research holds promise for treating other autoimmune conditions where B cells play a detrimental role. These insights pave the way for innovative therapies that have the potential to revolutionize autoimmune disease management, offering a hope for affected individuals and improving their quality of life.
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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".