Effective use of genetically-encoded optical biosensors for profiling signalling signatures in iPSC-CMs derived from idiopathic dilated cardiomyopathy patients
Bibliographic record
Abstract
Abstract Dilated cardiomyopathy (DCM) is a cardiovascular condition that develops when the left ventricle of the heart enlarges, compromising its function and diminishing its capacity to pump oxygenated blood throughout the body. After patients are diagnosed with DCM, disease progression can lead to heart failure and the need for a heart transplantation. DCM is a complex disease where underlying causes can be idiopathic, genetic, or environmental. An incomplete molecular understanding of disease progression poses challenges for drug discovery efforts as effective therapeutics strategies remain elusive. Decades of research using primary cells or animal models have increased our understanding of DCM but has been hampered due to the inaccessibility of human cardiomyocytes, to model cardiac disease, in vitro , in a dish. Here, our goal is to leverage patient-derived hiPSC-CMs and to combine them with biosensors to understand how cellular signalling is altered in DCM. With high sensitivity and versatility, optical biosensors represent the ideal tools to dissect the molecular determinants of cardiovascular disease, in an unbiased manner and in real-time at the level of single cells. By characterizing the pathobiology of dilated cardiomyopathy in a patient-specific manner using high content biosensor-based assays, we aim to uncover personalized mechanisms for the occurrence and development of DCM and as a pathway to development of personalized therapeutics.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".