ArticleNature communications2024
Deep-prior ODEs augment fluorescence imaging with chemical sensors.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Illuminating the Future of Catecholamine Detection.Journal of neurochemistry · 2026Review
- Deep-prior ODEs augment fluorescence imaging with chemical sensors.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
To study biological signalling, great effort goes into designing sensors whose fluorescence follows the concentration of chemical messengers as closely as possible. However, the binding kinetics of the sensors are often overlooked when interpreting cell signals from the resulting fluorescence measurements. We propose a method to reconstruct the spatiotemporal concentration of the underlying chemical messengers in consideration of the binding process. Our method fits fluorescence data under the constraint of the corresponding chemical reactions and with the help of a deep-neural-network prior. We test it on several GCaMP calcium sensors. The recovered concentrations concur in a common temporal waveform regardless of the sensor kinetics, whereas assuming equilibrium introduces artifacts. We also show that our method can reveal distinct spatiotemporal events in the calcium distribution of single neurons. Our work augments current chemical sensors and highlights the importance of incorporating physical constraints in computational imaging.
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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.