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Record W1963995522 · doi:10.1158/1538-7445.am2013-4206

Abstract 4206: Impact of nuclear telomere architecture in the transition of myelodysplastic syndromes to acute myeloid leukemias.

2013· article· en· W1963995522 on OpenAlexaff
Macoura Gadji, Fábio Morato de Oliveira, Sabine Mai

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsMyelodysplastic syndromesTelomereMyeloid leukemiaDiseaseGenome instabilityEpigeneticsOncologyBiologyMedicineInternal medicineBioinformaticsCancer researchGeneticsBone marrowGene

Abstract

fetched live from OpenAlex

Abstract Myelodysplastic Syndromes (MDS) are a group of disorders characterized by cytopenias, with a propensity for evolution into Acute Myeloid Leukemias (AML). This transformation is driven by genomic instability and epigenetic events. However, how genomic instability occurs in this disease remains unknown. Telomere dysfunction might be the generator of genomic instability leading to cytopenias and disease progression. Despite several studies demonstrating the role of telomere dysfunction in the occurrence of hematopoietic malignancies, little is known about their role in the evolution of MDS to AML/MDS. Our preliminary data allowed us to define three-dimensional nuclear telomeric profiles on the basis of telomere numbers, telomeric aggregates, telomere signal intensities, nuclear volumes, and nuclear telomere distribution. Using these parameters, we blindly subdivided the MDS patients into nine subgroups and the AML patients into six subgroups. We showed distinct telomeric profiles specific to patients with MDS, AML, and suggested for the first time a chronological and evolutionary process of telomere dysfunction in both diseases and the transformation of MDS to AML. To validate the clinical significance of 3D telomere profiling of MDS-patients and AML-patients, we are studying new patient cohorts and patients progressing from MDS to AML over time. Such a longitudinal study will allow for precise 3D telomeric profiling during disease progression. Since transformation of MDS to AML can occur over a long period of time, we are also studying disease progression from MDS to AML using a mouse model (C57BL/6-Tg(Vava1-NUP98/HOXD13)G2Apla/J). These mice will transform their MDS-disease to AML disease in a time period of in between 4-14 months. Therefore, we are following the mice monthly up to 14 months. Our study will allow us to gain an understanding of the 3D telomeric signatures that predict and accompany the transition of MDS to AML. In addition, we will define the molecular profiles at disease transition to improve, once validated, treatment strategies for patients with MDS/AML. Citation Format: Macoura Gadji, Fábio Morato de Oliveira, Sabine Mai. Impact of nuclear telomere architecture in the transition of myelodysplastic syndromes to acute myeloid leukemias. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4206. doi:10.1158/1538-7445.AM2013-4206

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: Observational · Consensus signal: none
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)0.0020.000

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 head0.044
GPT teacher head0.377
Teacher spread0.334 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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