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Record W2058689898 · doi:10.3109/10428190109097663

Lovastatin Induced Control of Blast Cell Growth in an Elderly Patient with Acute Myeloblastic Leukemia

2001· article· en· W2058689898 on OpenAlexaff
Mark D. Minden, Jim Dimitroulakos, Dana Nohynek, Linda Z. Penn

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer Research
Fundersnot available
KeywordsLovastatinMedicineAcute myeloblastic leukemiaChemotherapyIn vitroPharmacologyCell cultureLeukemiaCancer researchInternal medicineImmunologyOncologyCholesterolBiologyBiochemistry

Abstract

fetched live from OpenAlex

We recently reported that AML cells derived either from cell lines or from patients undergo apoptosis in response to lovastatin, an agent used extensively in the treatment of hypercholesterolemia. The concentration of lovastatin required to achieve this in culture varies from patient to patient, however, the in vitro concentrations required to kill AML cells, can be attained clinically. While in vitro studies assessing responsiveness of leukemic cells to lovastatin were being performed, a 72 year old female presented with relapsed AML. The patient did not desire any further induction therapy. As the patient's cells proved to be sensitive in culture to lovastatin, the patient was offered this drug. In this brief report we describe a case in which there was apparent control of the patient's leukemic blast cells by lovastatin at a dose double the usual recommended dose for hypercholesterolemia. This case illustrates the potential for lovastatin to provide a novel means of controlling leukemic cell growth in AML 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.205
Teacher spread0.200 · 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 designCase report
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

Citations55
Published2001
Admission routes1
Has abstractyes

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