Progress in RNAi-mediated Molecular Therapy of Acute and Chronic Myeloid Leukemia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it