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Record W2016548691 · doi:10.1182/blood.v97.12.3902

MCL1 transgenic mice exhibit a high incidence of B-cell lymphoma manifested as a spectrum of histologic subtypes

2001· article· en· W2016548691 on OpenAlexaff
Ping Zhou, Haiyi Xie, Liping Qian, Chi‐Yu Gregory Lee, Randy D. Gascoyne, Ruth W. Craig

Bibliographic record

VenueBlood · 2001
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Cancer Institute
KeywordsMCL1LymphomaTransgeneCarcinogenesisBiologyCancer researchFollicular lymphomaGenetically modified mouseCancerImmunologyGeneGeneticsDownregulation and upregulation

Abstract

fetched live from OpenAlex

Viability-promoting genes such as BCL2 play an important role in human cancer but do not directly cause aggressive tumors. BCL2 transgenic mice develop lymphoma at low frequency, hindering studies of tumorigenesis and its inhibition in the presence of such gene products. MCL1 is a member of the BCL2 family that is highly regulated endogenously and that promotes cell viability and immortalization when introduced exogenously. Mice expressing an MCL1 transgene in hematolymphoid tissues have now been monitored for an extended period and were found to develop lymphoma with long latency and at high probability (more than 85% over 2 years). In most cases, the disease was widely disseminated and of clonal B-cell origin. A variety of histologic subtypes were seen, prominently follicular lymphoma and diffuse large-cell lymphoma. MCL1 thus sets the stage for the development of lymphoma as does BCL2, disease occurring with high probability and recapitulating a spectrum of subtypes as seen in human patients. These findings with the transgene underscore the importance of the normal, highly regulated pattern of MCL1 expression, in addition to providing a model for studying tumorigenesis and its inhibition in the presence of a viability promoting BCL2 family member. (Blood. 2001;97:3902-3909)

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 designObservational
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

Citations185
Published2001
Admission routes1
Has abstractyes

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