Glucagon‐like peptide‐1 is specifically involved in sweet taste transmission
Bibliographic record
Abstract
Five fundamental taste qualities (sweet, bitter, salty, sour, umami) are sensed by dedicated taste cells (TCs) that relay quality information to gustatory nerve fibers. In peripheral taste signaling pathways, ATP has been identified as a functional neurotransmitter, but it remains to be determined how specificity of different taste qualities is maintained across synapses. Recent studies demonstrated that some gut peptides are released from taste buds by prolonged application of particular taste stimuli, suggesting their potential involvement in taste information coding. In this study, we focused on the function of glucagon-like peptide-1 (GLP-1) in initial responses to taste stimulation. GLP-1 receptor (GLP-1R) null mice had reduced neural and behavioral responses specifically to sweet compounds compared to wild-type (WT) mice. Some sweet responsive TCs expressed GLP-1 and its receptors were expressed in gustatory neurons. GLP-1 was released immediately from taste bud cells in response to sweet compounds but not to other taste stimuli. Intravenous administration of GLP-1 elicited transient responses in a subset of sweet-sensitive gustatory nerve fibers but did not affect other types of fibers, and this response was suppressed by pre-administration of the GLP-1R antagonist Exendin-4(3-39). Thus GLP-1 may be involved in normal sweet taste signal transmission in mice.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".