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Record W2012491953 · doi:10.1051/medsci/200622121101

Métabolisme des rétinoïdes et cancer

2006· review· fr· W2012491953 on OpenAlexaff
Maxime Parisotto, Hélène Brodeur, Pangala V. Bhat, Sylvie Mader

Bibliographic record

Venuemédecine/sciences · 2006
Typereview
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRetinoidRetinoic acidAcute promyelocytic leukemiaCancer researchDifferentiation therapyCarcinogenesisCancerApoptosisLeukemiaEnzymeTretinoinBiologyMedicineImmunologyInternal medicineBiochemistryGene

Abstract

fetched live from OpenAlex

Retinoids play important roles in cell differentiation and apoptosis, notably in epithelial tissues. Their utility in cancer therapy has been demonstrated in specific cancer types. Use of retinoic acid (RA) in the treatment of acute promyelocytic leukemia was the first successful example of retinoid-based differentiation therapy. RA has since been evaluated for treatment of other cancers, revealing variable effectiveness. The observation that expression of enzymes involved in RA biosynthesis is suppressed during tumorigenesis suggests that intra-tumor depletion in RA levels may contribute to tumor development and argues for the use of retinoids in cancer treatment. However, the induction of RA-inactivating enzymes is one of the mechanisms that may limit the efficacy of retinoid therapy and contribute to acquired resistance to RA treatment, suggesting that retinoic acid metabolism blocking agents may be effective agents in differentiation therapy.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.006

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 head0.050
GPT teacher head0.367
Teacher spread0.317 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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