Multiscale Image Analysis of Calcium Dynamics in Cardiac Myocytes
Bibliographic record
Abstract
Cardiac myocytes are the muscle cells that build up heart tissue and provide the mechanical action to pump blood by synchronously contracting at every heartbeat.Heart muscle contraction is regulated by intracellular calcium concentration which exhibits a complex spatio-temporal dynamical behavior at the molecular, cellular and tissue levels.Details of such dynamical patterns are closely related to the mechanisms responsible for cardiovascular diseases , the single largest cause of death in the developed countries.The emerging field of translational cardiology focuses on the study of how such mechanisms connect and influence each other across spatial and temporal scales, eventually yielding to a certain clinical condition.To study such calcium dynamics in cardiac myocytes, we benefit from the recent advances in the field of experimental cell physiology.Fluorescence microscopy allows us to observe the distribution of calcium in the cell with a spatial resolution below one micron and a frame rate around one millisecond, thus providing a very accurate monitoring of calcium fluxes in the cell.The aim of the thesis summarized in this paper, was to develop image processing computational techniques for extracting quantitative data of physiological relevance from fluorescence confocal microscopy images at different scales.The two main subjects covered in the thesis were image segmentation and classification methods applied to fluorescence microscopy imaging of cardiac myocytes and calcium imaging.These methods were applied to a variety of problems involving different space and time scales, such as the localization of molecular receptors, the detection and characterization of spontaneous calcium-release events, and the propagation of calcium waves across a culture of cardiac cells.The following is a summary of the thesis as a consequence of having been awarded the 7 th Justiniano Casas Award accessit by the Sociedad Española de Óptica.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".