"Identification of Mechanisms By Which Mesenchymal Stem/Stromal Cells Contribute to Acute Myeloid Leukemia"
Bibliographic record
Abstract
Acute myeloid leukemia (AML) is a blood malignancy resulting in abnormal hematopoiesis that is reported to be associated with alterations in the bone marrow microenvironment (BME). Current treatments for this heterogeneous disease, which target the leukemic cells but not the BME, are largely unsuccessful for the majority of AML subtypes. By better understanding the mechanisms by which the BME contributes to leukemogenesis, it may be possible to introduce more effective treatments for AML. Mesenchymal stem cells (MSCs) are an essential component of the BME that have been shown to support normal hematopoiesis. Therefore, MSCs may have several roles in the alteration of the BME, leukemogenesis, and AML relapse and can provide an excellent model for studying the BME in vitro. While some studies have characterized AML-derived MSCs (AML-MSCs), their exact role in the disease remains unclear. Our RNAseq analysis of AML-MSCs (n=30), and healthy donor MSCs (HD-MSCs) (n=8) identified that, among 7655 genes, 21 genes were significantly differentially expressed in AML-MSCs. Through in silico analysis of this gene set, genes of interest were identified as having the potential to directly alter the BME and affect AML pathogenesis through BMP/TGF-β pathways. Current work is focusing on investigation of the effects of selected genes with biological relevance on MSCs intrinsic and extrinsic functional properties. This study will improve our understanding of the role of MSCs in AML BME and help in the discovery of new therapeutic targets. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".