Recent advances in immunotherapy of B-CLL using ex vivo modified dendritic cells
Bibliographic record
Abstract
Chronic lymphocytic leukemia (CLL) results from the relentless accumulation of small mature, slowly dividing, monoclonal B-lymphocytes. The clinical course is heterogeneous, some patients with aggressive form of the disease progressing rapidly with early death while others exhibit a more stable, possibly, non-progressing indolent type of the disease lasting many years. Despite progress in modern treatment modalities, relapse invariably occurs and disease still remains incurable. The clinical management of CLL is therefore challenging and considerable effort has been directed towards novel therapeutic strategies aimed at reducing minimal residual disease which can increase remission duration. Recent insight into the role of dendritic cells (DCs) as pivotal antigen presenting cells that initiate immune responses may provide the basis for generating more specific and effective immune responses. Ex-vivo modified and monocyte-derived DCs represents a promising approach within the context of CLL. However, understanding the relationship between DCs and the cellular immune response is crucial in devising strategies for manipulating immune responses. After a brief survey of general properties of DCs, this review focuses on the different approaches exploiting monocyte-derived DCs in CLL, which may help to design novel strategies for phase-I clinical trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".