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Record W2172033645 · doi:10.1155/2008/146871

Stem Cell Biomarkers in Chronic Myeloid Leukemia

2008· review· en· W2172033645 on OpenAlexafffund
Xiaoyan Jiang, Yun Zhao, Donna L. Forrest, Clayton A. Smith, Allen Eaves, Connie J. Eaves

Bibliographic record

VenueDisease Markers · 2008
Typereview
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Cancer InstituteMichael Smith Health Research BCStem Cell NetworkTerry Fox FoundationCancer Research Society
KeywordsStem cellImatinib mesylateMyeloid leukemiaHaematopoiesisCancer researchImatinibBiologyMyeloidLeukemiaImmunologyCancer stem cellCell biology

Abstract

fetched live from OpenAlex

Chronic myeloid leukemia (CML) is a clonal multi-step myeloproliferative disease that is initially produced and ultimately sustained by a rare subpopulation of BCR-ABL+ cells with multi-lineage stem cell properties. These BCR-ABL+ CML stem cells are phenotypically similar to normal hematopoietic stem cells which are also maintained throughout the course of the disease at varying levels in different patients. Defining the unique properties of the leukemic stem cells that produce the chronic phase of CML has therefore had to rely heavily on access to samples from rare patients in which the stem cell compartment is dominated by leukemic elements. Here we review past and ongoing approaches using such samples to identify biologically and clinically relevant biomarkers of BCR-ABL+ stem cells that explain their unusual biology and that may help to design, or at least predict, improved treatment responses in CML patients. These studies are of particular interest in light of recent evidence that chronic phase CML stem cells are not only innately resistant to imatinib mesylate and other drugs that target the BCR-ABL oncoprotein, but are also genetically unstable.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.027
GPT teacher head0.295
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations14
Published2008
Admission routes2
Has abstractyes

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