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Record W4319723445 · doi:10.31224/2824

Hutchinson-Gilford progeria syndrome as a model for vascular aging

2023· preprint· en· W4319723445 on OpenAlexafffund
Jonathan A. Brassard, Natalie Fekete, Alain Garnier, Corinne A. Hoesli

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsMcGill UniversityUniversité Laval
FundersCanadian Institutes of Health ResearchPfizer CanadaStem Cell NetworkUniversity of OttawaUniversité de MontréalUniversité LavalCentre québécois sur les matériaux fonctionnelsOttawa Hospital Research InstitutePfizer
KeywordsProgeriaMedicineBiologyGenetics

Abstract

fetched live from OpenAlex

Hutchinsion-Gilford progeria syndrome (HGPS) is a premature aging disorder caused by a de novo genetic mutation that leads to the accumulation of a splicing isoform of lamin A termed progerin.Progerin expression alters the organization of the nuclear lamina and chromatin.The life expectancy of HGPS patients is severely reduced due to critical cardiovascular defects.Progerin also accumulates in an age-dependent manner in the vascular cells of adults that do not carry genetic mutations associated with HGPS.The molecular mechanisms that lead to vascular dysfunction in HGPS may therefore also play a role in vascular aging.The vascular phenotypic and molecular changes observed in HGPS are strikingly similar to those seen with age, including increased senescence, altered mechanotransduction and stem cell exhaustion.This article discusses the similarities and differences between age-dependent and HGPSrelated vascular aging to highlight the relevance of HGPS as a model for vascular aging.Induced pluripotent stem cells derived from HGPS patients are suggested as an attractive model to study vascular aging in order to develop novel approaches to treat cardiovascular disease.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.024
GPT teacher head0.276
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations2
Published2023
Admission routes2
Has abstractyes

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