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Record W1989324993 · doi:10.1593/neo.07106

Telomere Length on Chromosome 17q Shortens More than Global Telomere Length in the Development of Breast Cancer

2007· article· en· W1989324993 on OpenAlexafffund
Fariborz Rashid-Kolvear, Melania Pintiliet, Susan J. Done

Bibliographic record

VenueNeoplasia · 2007
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
FundersCure Brain Cancer FoundationCanadian Breast Cancer Research Alliance
KeywordsTelomereBiologyBreast cancerChromosomeChromosome instabilityDuctal carcinomaPathologyCancerGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

It is known that total telomere length is shorter in invasive breast cancer than in normal breast tissue but the status of individual telomere lengths has not been studied. Part of the difficulty is that usually telomere length in interphase cells is measured on all chromosomes together. In this study we compared normal breast epithelium, duct carcinoma in situ (DCIS), and invasive duct carcinoma (IDC) from 18 patients. Telomere length was specifically measured on chromosome 17q and was found to be shorter in DCIS and IDC than in normal breast epithelial cells, with more heterogeneity in telomere length in DCIS associated with IDC than in DCIS alone. More importantly, we found that the shortening of telomere on chromosome 17q is greater than the average shortening of all telomeres. This finding indicates that telomere shortening is not simply the result of the end replication problem; otherwise, all telomeres should be subjected to the same rate of telomere shortening. It seems there are mechanisms that preferentially erode some telomeres more than others or preferentially protect some chromosome ends. Our results suggest that the increased level of telomere shortening on 17q may be involved in chromosome instability and the progression of DCIS.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.308
Teacher spread0.289 · 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 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

Citations21
Published2007
Admission routes2
Has abstractyes

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