MétaCan
Menu
Back to cohort
Record W3134528205 · doi:10.1073/pnas.2101566118

Loss of expression of both miR-15/16 loci in CML transition to blast crisis

2021· article· en· W3134528205 on OpenAlexaff
Francesca Lovat, Pierluigi Gasparini, Giovanni Nigita, Karilyn Larkin, John C. Byrd, Mark D. Minden, Michael Andreeff, Bing Z. Carter, Carlo M. Croce

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Toronto
FundersNational Cancer Institute
KeywordsBlast CrisisExpression (computer science)Transition (genetics)GeneticsPolitical scienceBiologyComputer scienceGene

Abstract

fetched live from OpenAlex

Despite advances that have improved the treatment of chronic myeloid leukemia (CML) patients in chronic phase, the mechanisms of the transition from chronic phase CML to blast crisis (BC) are not fully understood. Considering the key role of miR-15/16 loci in the pathogenesis of myeloid and lymphocytic leukemia, here we aimed to correlate the expression of miR-15a/16 and miR-15b/16 to progression of CML from chronic phase to BC. We analyzed the expression of the two miR-15/16 clusters in 17 CML patients in chronic phase and 22 patients in BC and in 11 paired chronic phase and BC CML patients. BC CMLs show a significant reduction of the expression of miR-15a/-15b/16 compared to CMLs in chronic phase. Moreover, BC CMLs showed an overexpression of miR-15/16 direct targets such as Bmi-1, ROR1, and Bcl-2 compared to CMLs in chronic phase. This study highlights the loss of both miR-15/16 clusters as a potential oncogenic driver in the transition from chronic phase to BC in CML patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.287
Teacher spread0.269 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations8
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicMicroRNA in disease regulationFrench-language works237,207