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Record W1964483374 · doi:10.1139/o02-079

The promyelocytic leukemia nuclear body: sites of activity?

2002· review· en· W1964483374 on OpenAlexafffundvenue
Christopher H. Eskiw, David P. Bazett‐Jones

Bibliographic record

VenueBiochemistry and Cell Biology · 2002
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsHospital for Sick Children
FundersCanada Research ChairsCancer Research Society
KeywordsPromyelocytic leukemia proteinAcute promyelocytic leukemiaBiologyNuclear proteinTranscription factorCell biologyLeukemiaCell nucleusRetinoic acidNucleusGeneGenetics

Abstract

fetched live from OpenAlex

The promyelocytic leukemia (PML) nuclear body is one of many subnuclear domains in the eukaryotic cell nucleus. It has received much attention in the past few years because it accumulates the promyelocytic leukemia protein called PML. This protein is implicated in many nuclear events and is found as a fusion with the retinoic acid receptor RARalpha in leukemic cells. The importance of PML bodies in cell differentiation and growth is implicated in acute promyelocitic leukemia cells, which do not contain PML bodies. Treatment of patients with drugs that reverse the disease phenotype also causes PML bodies to reform. In this review, we discuss the structure, composition, and dynamics that may provide insights into the function of PML bodies. We also discuss the repsonse of PML bodies to cellular stresses, such as virus infection and heat shock. We interpret the changes that occur as evidence for a role of these structures in gene transcription. We also examine the role of the posttranslational modification. SUMO-1 addition, in directing proteins to this nuclear body. Characterization of the mobility of PML body associated proteins further supports a role in specific nuclear events, rather than the bodies resulting from random accumulations of proteins.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.260
Teacher spread0.245 · 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 teacher head, not a consensus.

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

Citations53
Published2002
Admission routes3
Has abstractyes

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