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Record W2889508731 · doi:10.1073/pnas.1714538115

Telomere shortening is a hallmark of genetic cardiomyopathies

2018· article· en· W2889508731 on OpenAlexfundno aff
Alex Chia Yu Chang, Andrew Chia Hao Chang, Anna Kirillova, Koki Sasagawa, Willis Su, Gerhard Weber, Jue Lin, Vittavat Termglinchan, Ioannis Karakikes, Timon Seeger, Alexandra Dainis, J. Travis Hinson, Jonathan G. Seidman, Christine E. Seidman, Euan A. Ashley, Joseph C. Wu, Helen M. Blau

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Heart, Lung, and Blood InstituteNational Institute on AgingCanadian Institutes of Health ResearchNational Institutes of HealthGovernment of CanadaBurroughs Wellcome FundNational Institute of General Medical SciencesAmerican Heart AssociationHoward Hughes Medical Institute
KeywordsTelomereGeneticsCardiomyopathyBiologyMedicineInternal medicineDNAHeart failure

Abstract

fetched live from OpenAlex

Quantitative FISH (Q-FISH) of single cells revealed that telomeres were significantly reduced by 26% in HCM and 40% in DCM patient CMs in fixed tissue sections compared with CMs from age- and sex-matched healthy controls. In the cardiac tissues of the same patients, telomere shortening was not evident in vascular smooth muscle cells that do not express or require the contractile proteins, an important control. Telomere shortening was recapitulated in DCM and HCM CMs differentiated from patient-derived human-induced pluripotent stem cells (hiPSCs) measured by two independent assays. This study reveals telomere shortening as a hallmark of genetic HCM and DCM and demonstrates that this shortening can be modeled in vitro by using the hiPSC platform, enabling drug discovery.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.315
Teacher spread0.270 · 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

Citations103
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicTelomeres, Telomerase, and SenescenceFrench-language works237,207