Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes
Bibliographic record
Abstract
Colocalization of dihydropyridine (DHPR) and ryanodine (RyR) receptors, a key determinant of Ca(2+)-induced Ca2+ release, was previously estimated in 3-, 6-, 10-, and 20-day-old rabbit ventricular myocytes by immunocytochemistry and confocal microscopy. We now report on the effects of deconvolution (using a maximum-likelihood estimation algorithm) on the calculation of colocalization indexes. Clusters of DHPR and RyR can be accurately represented as point sources of fluorescence, which enables a model of their relative distributions to be constructed using images of point spread functions to simulate their fluorescence inside a cell. This model was used to investigate the effects of deconvolution on colocalization as a function of separation distance. Deconvolution resulted in significant improvements in both axial and transverse resolutions, producing significant increases in clarity. Comparisons of intensity profiles (full-width half-maximum) pre- and postdeconvolution showed decreased dispersion of the fluorescent signal and a corresponding decrease in false colocalization as determined by fluorescence modeling. This hypothesis was extended to physiological data previously collected. The number of colocalized voxels was quantified after deconvolution, and the degree of colocalization of DHPR with RyR decreased significantly after deconvolution in all age groups: 3 days (62 +/- 2% before deconvolution, 43 +/- 3 after deconvolution) to 20 days old (79 +/- 1% before deconvolution, 63 +/- 2% after deconvolution). The data demonstrate that confocal images should be deconvolved before any quantitative analysis, such as colocalization index determination, to minimize the detrimental effects of out-of-focus light in coincident voxels.
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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 | 0.000 | 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".