Progress in RNAi-mediated Molecular Therapy of Acute and Chronic Myeloid Leukemia
Bibliographic record
Abstract
Leukemias arise from genetic alterations in normal hematopoietic stem or progenitor cells, leading to impaired regulation of proliferation, differentiation, apoptosis, and survival of the transformed cells. With the advent of RNA interference (RNAi) and the short interfering RNA (siRNA) as its pharmacological mediator, it is becoming possible to modulate specific targets at will. This article summarizes current attempts to utilize RNAi reagents for therapy of leukemias, focusing on acute and chronic myeloid leukemia. We first present unique aspects of RNAi-mediated therapy, followed by a brief background on the delivery technology of RNAi reagents. The need for leukemia-specific delivery of siRNA is discussed by describing approaches that targeted agents to leukemic cells. Pharmacokinetics and biodistribution of RNAi agents are then presented, highlighting the critical issues pertinent to emerging siRNA therapy. Efforts to deliver specific RNAi therapies are then summarized in the context of expected clinical outcomes, focusing on limiting leukemic cell survival, sensitizing malignant cells to chemotherapy, mobilization of leukemic cells, and eradication of leukemic stem cells. We conclude with a perspective on the future of RNAi therapy, emphasizing the technological requirements and mechanistic challenges for clinical entry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".