ArticleFrontiers in bioinformatics2026
BlinkFusion: modular platform quantifying labeling efficiency and photophysics in regular and super-resolution fluorescence microscopy.
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative analysis in fluorescence microscopy presents challenges: most open-source Single Molecule Localization Microscopy toolkits emphasize visualization over downstream metrics, and practitioners must iteratively juggle sample preparation variables (e.g., labeling density) with acquisition parameters (e.g., photoswitching conditions) once imaging is underway, obscuring cause effect and slowing optimization. We address this gap with BlinkFusion, a modular, open-source Python platform that unifies filament labeling efficiency and STORM photophysics in a single, reproducible workflow. The system ingests image stacks, extracts metadata, and runs two complementary pipelines: (i) a confocal/filament branch that applies ridge guided ROI selection and Stretching Open Active Contours (SOACs) to quantify degree of labeling (DOL) and morphometrics, providing pre-STORM feedback on staining quality; and (ii) a STORM branch that merges localizations into molecules and computes duty cycle, survival fraction, photon yields, and switching cycles within a quasi-equilibrium window for fair cross dataset comparison. An interactive dashboard enables side by side dataset review, rapid parameter sweeps, and immediate reprocessing. On nanobody labeled tubulin, the filament pipeline automatically captures expected trends in continuity, contrast, and intensity across preparation and illumination settings; on Cy5 benchmarks, the STORM pipeline reproduces literature photophysics within 20% under matched conditions, while reducing peak CPU/heap demand and manual effort. A streamlined DOL workflow cuts processing time versus prior manual practice. BlinkFusion therefore links structural and photophysical readouts to deliver immediate, quantitative feedback and a practical path towards real time experimental optimization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.