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Record W1969762073 · doi:10.1182/blood-2002-03-0894

Monoglycerides induce apoptosis in human leukemic cells while sparing normal peripheral blood mononuclear cells

2002· article· en· W1969762073 on OpenAlexaff
Fabianne Philippoussis, Chantal Arguin, Véronique Mateo, Ann‐Muriel Steff, Patrice Hugo

Bibliographic record

VenueBlood · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsMcGill UniversityCentre Hospitalier de l’Université de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsPeripheral blood mononuclear cellApoptosisLeukemiaCytotoxic T cellChronic lymphocytic leukemiaCancer researchCancer cellK562 cellsImmunologyProgrammed cell deathBiologyMedicineCancerIn vitroBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

A major drawback of the current antineoplastic treatments is their lack of specificity toward cancer cells, because they are most often cytotoxic to normal cells, thus creating related side effects. Hence, the identification of new apoptosis-inducing agents, specifically targeting malignant cells while sparing their normal counterparts, is of crucial interest. We show here that monoglycerides, a family of lipids consisting of a single fatty acid attached to a glycerol backbone, induce cell death in several human leukemic cell lines. Importantly, treatment of primary leukemic cells, obtained from B-cell chronic lymphocytic leukemia patients, resulted in rapid apoptosis. In striking contrast, resting or activated human peripheral blood mononuclear cells from healthy individuals were resistant to the same treatment. Therefore, these compounds could represent potential antileukemic drugs or could allow for the design of novel therapeutic agents applied to leukemia.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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)0.0020.001

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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 designBench or experimental
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

Citations9
Published2002
Admission routes1
Has abstractyes

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