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Record W3158573488 · doi:10.15173/sciential.vi6.2723

Selective Targeting of Unique Metabolic Properties of Leukemic Stem Cells in Acute Myeloid Leukemia

2021· article· en· W3158573488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStem cellMyeloid leukemiaCancer stem cellCancer researchLeukemiaMedicineDiseaseMyeloidRadiation therapyCancerMinimal residual diseaseImmunologyBiologyInternal medicineCell biology

Abstract

fetched live from OpenAlex

Current therapeutic options in the treatment of acute myeloid leukemia often succumb to high instances of relapse and subsequent mortality. Chemotherapy and radiotherapy have long been used as the standard treatment for this disease, remaining stagnant over the past few decades. Recently, a small self-renewing population of leukemic stem cells have been identified as drivers of cancer relapse and progression due to their increased resistance to anticancer therapeutics. This enables these cells to maintain a minimal residual disease and results in downstream differentiation, leading to relapse. Targeting these cells may lead to effective therapies that reduce relapse and mortality. Recently, the metabolic properties of leukemic stem cells have begun to be elucidated. Here, we discuss recent discoveries regarding the metabolism of leukemic stem cells and approaches to targeting their unique metabolic properties.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.276
Teacher spread0.254 · 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