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Record W2316455777 · doi:10.15273/dmj.vol33no1.4139

Expression of Death Receptors and Associated Regulatory Proteins in Pediatric Acute Lymphoblastic Leukemia: Preliminary Results

2005· article· en· W2316455777 on OpenAlexaffvenue
Bruce L Musgrave, Dorothy Barnard, David W. Hoskin

Bibliographic record

VenueDalhousie Medical Journal · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell death mechanisms and regulation
Canadian institutionsDalhousie University
Fundersnot available
KeywordsApoptosisReceptorFas ligandLeukemiaBiologyCell biologyFas receptorProgrammed cell deathCancer researchLymphoblastic LeukemiaInhibitor of apoptosis domainImmunologyGeneticsCaspase

Abstract

fetched live from OpenAlex

Altered regulation of the cell cycle and/or apoptosis are well established events in the process of neoplastic transformation. Fas-mediated apoptosis is regulated by several inhibitory proteins in normal lymphocytes and leukemic cells. Some of these protein molecules may also play a role in the induction of apoptosis through death receptors for a similar molecule, TNF-related apoptosis inducing ligand (TRAIL). The involvement of these anti-apoptotic molecules in leukemogenesis is uncertain. Our preliminary findings indicate decreased expression of pro-apoptotic TRAIL-DR4 receptors in leukemic T cells relative to untransformed T cells. This decrease in expression may represent a potential determinant of poor acute lymphoblastic leukemia (ALL) prognosis, as it may result in a favorable biological situation for escape from apoptosis. Our data also demonstrate decreased levels of expression of pro-apoptotic Fas protein molecules in pediatric patient samples, relative to ALL cell lines. This difference suggests that circulating leukemic cells differ in their susceptibility to Fas-mediated apoptosis. Taken together, our data suggest that ALL cell lines and circulating leukemic cells from pediatric patients possess different regulatory mechanisms in order to attenuate Fas-mediated signaling

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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.

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

Citations0
Published2005
Admission routes2
Has abstractyes

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