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Abstract 15510: Generation of the Cardiac Valve Lineage From Human Pluripotent Stem Cells

2023· article· en· W4389944637 on OpenAlexaff
Amine Mazine, Alexander Mikryukov, Ian Fernandes, Clifford Z. Liu, Soheil Jahangiri, Marcy Martin, Eric K. N. Gähwiler, Juliana Gomez, Neda Latifi, Michael A. Laflamme, Craig A. Simmons, Simon P. Hoerstrup, Maximilian Y. Emmert, Bruce D. Gelb, Mingxia Gu, Gordon Keller

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInduced pluripotent stem cellStem cellCell biologyPopulationMesenchymal stem cellProgenitor cellMedicineBiologyEmbryonic stem cellGenetics

Abstract

fetched live from OpenAlex

Background: Heart valves are living structures whose sophisticated functions are mediated by a specialized population of valvular interstitial cells (VICs). Given their central role in valve homeostasis, VICs represent a promising cell population for studying valve diseases and developing novel therapies to treat them. Herein, we describe the generation of VIC-like cells from human pluripotent stem cells (hPSCs). Methods and Results: Using a previously established protocol, we first generated endocardial cells from cardiovascular mesoderm. Within this endocardial population, we identified a subset of cells — marked by the expression of PDGFRβ — that express valvular endocardial cell (VEC) markers and demonstrate VEC-like functional properties, namely the ability to generate calcium transients in response to ATP and the ability to undergo endothelial-to-mesenchymal transition (EndoMT). Through stage-specific manipulation of developmental signaling pathways, we established a protocol that promotes the development of VIC-like cells from these hPSC-derived VEC progenitors (Figure 1A). The cells thus generated transcriptionally matched primary human fetal VICs by scRNAseq. When embedded in a tissue-engineered scaffold, hPSC-VICs secreted collagen and glycosaminoglycans, and demonstrated compact tissue organization analogous to that of native valve leaflets (Figure 1B, C). Finally, we show that VICs generated from an hPSC line with a Noonan syndrome mutation displayed excessive endocardial cell proliferation, diminished EndoMT, and dysregulated pERK activity, recapitulating key aspects of the disease. Conclusions: Together, the findings presented in this report provide a reproducible method for the scaled generation of bona fide VICs from hPSCs. The generation of hPSC-VICs addresses an important gap in the field and provides a platform to study valvulogenesis and heart valve disease, as well as a novel avenue for heart valve tissue engineering.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

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)0.0040.002

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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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