Bibliographic record
Abstract
University of British Columbia, Canada T introduction of molecularly targeted drugs in the 21st century marks a new and exciting era for cancer therapy. One of these new drugs is Imatinib (IM, Gleevec), a selective tyrosine kinase inhibitor that blocks the catalytic activity of the BCRABL oncoprotein. IM therapy has revolutionized the treatment of chronic myeloid leukemia (CML) worldwide. Nevertheless, early relapses and IM-resistant disease occur in a signifi cant proportion of patients. Our recent studies indicate that CML stem cells are less responsive to IM and other tyrosine kinase inhibitors and are critical target population for IM resistance. Improved treatment approaches to prevent the development of resistant subclones by targeting other key molecular elements active in CML stem cells are thus clearly needed. One candidate is a complex we recently discovered that forms in CML stem/progenitor cells between the oncoproteins encoded by AHI-1 (Abelson helper integration site 1), BCR-ABL and the JAK2 kinase. Th is complex contributes to the transforming activity of BCR-ABL both in vitro and in vivo and also plays a critical role in the IM response/resistance of primary CML stem/ progenitor cells. Interestingly, treatment with IM or dasatinib (DA) in combination with a new JAK2 inhibitor (TG101209) resulted in greater inhibition of CD34+ CML stem/progenitor cells from IM nonresponders, compared to the same cells treated with a combination of IM and DA, as measured by colony-forming cell assays and longterm culture-initiating cell assays. Th ese results suggest that targeting both BCR-ABL and JAK2 activities may be a potential therapeutic option for IM resistant patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".