Novel Analysis Method for Beating Cells Videomicroscopy Data: Functional Characterization of Culture Samples
Bibliographic record
Abstract
Cell culture of cardiac tissue analog is becoming increasingly interesting for regenerative medicine (cell therapy and tissue engineering) and is widely used for high throughput cardiotoxicity. As a cost-effective approach to rapidly discard new compounds with high toxicity risks, cardiotoxicity evaluation is firstly done in vitro requiring cells/tissue with physiological/pathological characteristics (close to in vivo properties). Studying multicellular electrophysiological and contractile properties is needed to assess drug effects. Techniques favoring process automation which could help in simplifying screening drug candidates are thus of central importance. A lot of effort has been made to ameliorate in vitro models including several in vitro platforms for engineering neonatal rat cardiac tissues. However, most of the initial evaluation is done by studying the rate of activity. In this study, we present new approaches that use the videomicroscopy video of monolayer activity to study contractile properties of beating cells in culture. Two new variables are proposed which are linked to the contraction dynamics and are dependent on the rhythm of activity. Methods for evaluation of regional synchronicity within the image field of view are also presented that can rapidly determine regions with abnormal activity or heterogeneity in contraction dynamics.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".