Bibliographic record
Abstract
Abstract Description Acute myeloid leukemia (AML) is an aggressive blood cancer featuring clonal expansion of malignant myeloid progenitors. Current treatment of AML remains a combination of cytarabine and anthracycline that causes dsDNA breaks. Although this regimen initially reduces leukemic cells, almost 65% of patients suffer a relapse. Thus, a better understanding of the molecular pathogenesis of AML is needed to devise more effective therapeutic strategies. cGAS functions in immune responses by sensing double-stranded DNA or mitochondrial DNA in the cytosol of a damaged cell. cGAS binding to such DNA triggers a cell cycle-dependent response in which “Stimulator of Interferon Genes” (STING) mediates the expression of inflammatory genes, including IFN-1, IL6 and TNFα. Hematopoietic stem cells in this inflammatory environment are biased to undergo myeloid differentiation. Our preliminary data indicating that, in an adoptive transfer mouse model of AML, animals that received cGAS-/-AML cells didn’t develop full leukemia even at 1yr post-transplant. Thus, leukemic cells may need cell-autonomous cGAS to expand, and cGAS may be pro-tumorigenic in AML. Moreover, mRNA levels of cGAS, STING, IL-6 and TNFα are altered in samples from AML patients, suggesting cGAS-related inflammation may be persistent in human leukemias.Indeed, most AML cells exhibit elevated DNA damage or mutations, which could lead to ds/mt DNA release and excessive cGAS activation. cGAS may be a novel therapeutic target for AML. Funding Sources Pre-clinical AstraZeneca Grant Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".