ReviewChemical senses2025
Opponent coding mechanisms in Drosophila taste neurons.
Review in Chemical senses, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The ability to discern nutritious food from harmful substances is critical for animal survival, with the gustatory system playing a pivotal role. In insects, including the genetic model Drosophila melanogaster, taste perception involves more than the established valence-based labeled line model, where distinct populations of taste neurons encode appetitive versus aversive stimuli and their activation promotes either attraction or rejection behaviors. A growing body of evidence reveals that sophisticated opponent coding mechanisms operate at multiple levels to modulate responses of taste neurons, enabling dynamic integration between competing taste modalities. These processes significantly expand the informational capacity and behavioral flexibility of the taste system, allowing animals to make appropriate feeding decisions in complex chemical environments where tastants of multiple modalities are simultaneously present.
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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.