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Record W7115030525

Integrative cardiac mechanobiology: Tools for probing electrophysiology and mechanotransduction in heart health and disease

2025· dissertation· en· W7115030525 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaMcGill University
KeywordsDiseaseElectrophysiologyMechanotransductionCardiac electrophysiologyElectrical conduction system of the heart
DOInot available

Abstract

fetched live from OpenAlex

Cardiac function emerges from an integrated loop of electrical activity, calcium signaling, and mechanical contraction. Yet, most in vitro studies continue to treat these processes as distinct, limiting our ability to model and understand key regulatory phenomena such as mechano-electric feedback. In this thesis, I address this challenge by studying how substrate stiffness and mechanical signaling influence electromechanical behavior in human iPSC-derived cardiomyocytes and cardiac fibroblasts.The thesis begins with a comprehensive review of cardiac mechanobiology, emphasizing the interplay between stiffness and the excitation-contraction coupling. This review identifies discrepancies in how stiffness is measured and modeled across studies, highlighting the need for standardized tools and frameworks in the field.To study how mechanical cues affect electrophysiology, the first requirement is to reliably quantify the electrophysiological response of cardiac cells. Calcium imaging serves as the most widely used proxy for this purpose, offering insight into the timing and magnitude of cellular excitation. To this end, I developed the “Heart-on-a-Miniscope,” a compact, low-cost optical platform that integrates calcium imaging and electrical stimulation in a single, modular, and adjustable unit. Designed to overcome the complexity and cost of traditional imaging setups, this system enabled functional recordings in 3D cardiac organoids and supported the development of a custom analysis pipeline for automated extraction of calcium transient parameters.Having established a platform to measure cellular response, the next challenge was to deliver mechanical input in a physiologically relevant and tunable manner. To address this, I developed a dual-crosslinked PDMS substrate with photopatternable stiffness control across a cardiac-relevant range (3–35 kPa). This system allowed spatial control over mechanical cues and revealed that localized stiffness gradients could induce differential cytoskeletal organization, nuclear deformation, and YAP localization in cardiac fibroblasts.With both sensing and stimulation platforms in place, I next examined how mechanical input affects cellular output in an integrated system. By combining calcium imaging and traction force microscopy in hiPSC-CMs cultured on mechanically-defined substrates, I found that substrate stiffness modulates not only contractile force but also calcium transient amplitude, duration, and kinetics. These findings highlight that mechanical feedback actively shapes electrophysiological behavior, and that inhibiting force generation pharmacologically may mask critical features of cellular response.Together, these studies contribute novel tools and mechanistic insight into how the physical microenvironment governs cardiac function. The findings underscore the importance of mechanical context in cardiac modeling and lay the groundwork for more predictive in vitro platforms for disease modeling and drug screening

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)0.002
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.004
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 head0.013
GPT teacher head0.271
Teacher spread0.258 · 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
GenreMethods

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
Published2025
Admission routes1
Has abstractyes

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