Unraveling the role of long non-coding RNAs in therapeutic resistance in acute myeloid leukemia: New prospects & challenges
Bibliographic record
Abstract
Acute Myeloid Leukemia (AML) is a fatal hematological disease characterized by the unchecked proliferation of immature myeloid blasts in different tissues developed by various mutations in hematopoiesis. Despite intense chemotherapeutic regimens, patients often experience poor outcomes, leading to substandard remission rates. In recent years, long non-coding RNAs (lncRNAs) have increasingly become important prognostic and therapeutic hotspots, due to their contributions to dysregulating many functional epigenetic, transcriptional, and post-translational mechanisms leading to alterations in cell expressions, resulting in increased chemoresistance and reduced apoptosis in leukemic cells. Through this review, I highlight and discuss the latest advances in understanding the major mechanisms through which lncRNAs confer therapy resistance in AML. In addition, I also provide perspective on the current strategies to target lncRNA expressions. A better knowledge of the critical role that lncRNAs play in controlling treatment outcomes in AML will help improve existing medications and devise new ones.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".