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Molecular and Cellular Mechanisms of Salt Taste

2022· review· en· W4308254075 on OpenAlexaff
Akiyuki Taruno, Michael D. Gordon

Bibliographic record

VenueAnnual Review of Physiology · 2022
Typereview
Languageen
FieldNursing
TopicBiochemical Analysis and Sensing Techniques
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTasteSalt (chemistry)SodiumAttractionUmamiChemistrySignal transductionBiophysicsFood scienceBiochemistryBiology

Abstract

fetched live from OpenAlex

Salt taste, the taste of sodium chloride (NaCl), is mechanistically one of the most complex and puzzling among basic tastes. Sodium has essential functions in the body but causes harm in excess. Thus, animals use salt taste to ingest the right amount of salt, which fluctuates by physiological needs: typically, attraction to low salt concentrations and rejection of high salt. This concentration-valence relationship is universally observed in terrestrial animals, and research has revealed complex peripheral codes for NaCl involving multiple taste pathways of opposing valence. Sodium-dependent and -independent pathways mediate attraction and aversion to NaCl, respectively. Gustatory sensors and cells that transduce NaCl have been uncovered, along with downstream signal transduction and neurotransmission mechanisms. However, much remains unknown. This article reviews classical and recent advances in our understanding of the molecular and cellular mechanisms underlying salt taste in mammals and insects and discusses perspectives on human salt taste.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.940
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.313
Teacher spread0.294 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2022
Admission routes1
Has abstractyes

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