Screening of cardiomyocyte fluorescence during cell contraction by multi-dimensional TCSPC
Bibliographic record
Abstract
Autofluorescence is one of the most versatile non-invasive tools for mapping the metabolic state of living tissues, such as the heart. We present a new approach to the investigation of changes in endogenous fluorescence during cardiomyocyte contraction - by spectrally-resolved, time correlated, single photon counting (TCSPC). Cell contraction is stimulated by external platinum electrodes, incorporated in a home-made bath and triggered by a pulse generator at a frequency of 0.5 Hz (to stabilize sarcoplasmic reticulum loading), or 5 Hz (the rat heart rate). Cell illumination by the laser is synchronized with cell contraction, using TTL logic pulses operated by a stimulator and delayed to study mitochondrial metabolism at maximum contraction (10-110 ms) and/or at steady state (1000-1100 ms at 0.5 Hz). To test the setup, we recorded calcium transients in cells loaded with the Fluo-3 fluorescent probe (excited by 475 nm pulsed picosecond diode laser). We then evaluated recordings of flavin AF (excited by 438 nm pulsed laser) at room and physiological temperatures. Application of the presented approach will shed new insight into metabolic changes in living, contracting myocytes and, therefore, regulation of excitation-contraction coupling and/or ionic homeostasis and, thus, heart excitability.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".