Combining tissue engineering and optical imaging approaches to explore interactions along the neuro-cardiac axis
Bibliographic record
Abstract
Interactions along the neuro-cardiac axis are being explored with regard to their involvement in cardiac diseases, including catecholaminergic polymorphic ventricular tachycardia, hypertension, atrial fibrillation, long QT syndrome and sudden death in epilepsy. Interrogation of the pathophysiology and pathogenesis of neuro-cardiac diseases in animal models present challenges resulting from species differences, phenotypic variation, developmental effects and limited availability of data relevant at both the tissue and cellular level. By contrast, tissue-engineered models containing cardiomyocytes and peripheral sympathetic and parasympathetic neurons afford characterization of cellular- and tissue-level behaviours while maintaining precise control over developmental conditions, cellular genotype and phenotype. Such approaches are uniquely suited to long-term, high-throughput characterization using optical recording techniques with the potential for increased translational benefit compared to more established techniques. Furthermore, tissue-engineered constructs provide an intermediary between whole animal/tissue experiments and in silico models. This paper reviews the advantages of tissue engineering methods of multiple cell types and optical imaging techniques for the characterization of neuro-cardiac diseases.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".