ReviewJournal of neurochemistry2026
Illuminating the Future of Catecholamine Detection.
Review in Journal of neurochemistry, 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
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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.
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0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
3 authors.
Funding
Abstract
Catecholamines such as dopamine (DA) and norepinephrine (NE) are critical neuromodulators which influence a wide range of physiological and behavioral processes. Optical sensors/probes are powerful tools to detect catecholamines with high spatial and temporal resolution, enabling real-time imaging in complex biological environments. In this review, we highlight recent advances in single-walled carbon nanotube (SWCNT) sensors and genetically encoded sensors for fluorescence-based catecholamine detection. We illustrate how these technologies have enabled new biological discoveries by allowing the spatiotemporal mapping of catecholamine release dynamics with unprecedented resolution. We discuss the complementary features of these techniques and remaining key challenges including molecular selectivity, tailored kinetics, and enhanced tissue penetration. Finally, we outline future directions and opportunities for integrating these technologies into neuroscience research, aiming to expand our understanding of catecholaminergic signaling across scales.
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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.