MétaCan
Menu
Back to cohort
Record W2943973024 · doi:10.1016/j.celrep.2019.04.040

SatB2-Expressing Neurons in the Parabrachial Nucleus Encode Sweet Taste

2019· article· en· W2943973024 on OpenAlexfundno aff
Ou Fu, Yuu Iwai, Kunio Kondoh, Takumi Misaka, Yasuhiko Minokoshi, Ken‐ichiro Nakajima

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsnot available
FundersMinistry of Education, Culture, Sports, Science and TechnologyUniversity of TokyoSwine Innovation Porc
KeywordsParabrachial NucleusNeuroscienceTasteThalamusOptogeneticsPonsCalcium imagingNucleusBiologyMedicineInternal medicineCalcium

Abstract

fetched live from OpenAlex

The gustatory system plays an important role in sensing appetitive and aversive tastes for evaluating food quality. In mice, taste signals are relayed by multiple brain regions, including the parabrachial nucleus (PBN) of the pons, before reaching the gustatory cortex via the gustatory thalamus. Recent studies show that taste information at the periphery is encoded in a labeled-line manner, such that each taste modality has its own receptors and neuronal pathway. In contrast, the molecular identity of gustatory neurons in the CNS remains unknown. Here, we show that SatB2-expressing neurons in the PBN play a pivotal role in sweet taste transduction. With cell ablation, in vivo calcium imaging, and optogenetics, we reveal that SatB2 PBN neurons encode positive valance and selectively transmit sweet taste signals to the gustatory thalamus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.012
GPT teacher head0.241
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCell ReportsSame topicBiochemical Analysis and Sensing TechniquesFrench-language works237,207