Spectral decomposition of NAD(P)H fluorescence components recorded by multi-wavelength fluorescence lifetime spectroscopy in living cardiac cells
Bibliographic record
Abstract
We report a novel analytical approach to identify individual components of a cell’s endogenous fluorescence, recorded by spectrally-resolved time-correlated single photon counting (TCSPC). Time-resolved area-normalized emission spectroscopy (TRANES) and principal component analysis (PCA) were applied to estimate the number of spectral components after metabolic modulation of cardiac cells following excitation with a 375 nm picosecond laser. Linear unmixing of TCSPC data spectrally decomposed individual components in living cells, while using characteristics of endogenously fluorescing molecules in solvents as a reference spectral database. Our data demonstrate the presence of three individual components, corresponding to the nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) in organic and inorganic solvents and to the residual flavoprotein fluorescence. The presented analytical approach offers a new alternative for the spectral separation of multi-wavelength fluorescence lifetime spectroscopy data to the conventional analysis, and opens a new possibility for the use of pattern recognition for fast resolution of components in 2D fluorescence lifetime microscopy images.
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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 | 0.000 |
| 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 | 0.000 | 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 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".