MétaCan
Menu
Back to cohort
Record W4404759888 · doi:10.1021/jacs.4c10917

High-Performance Chemigenetic Potassium Ion Indicator

2024· article· en· W4404759888 on OpenAlexaff
Dazhou Cheng, Zhenlin Ouyang, Xiaoyu He, Yusuke Nasu, Yurong Wen, Takuya Terai, Robert E. Campbell

Bibliographic record

VenueJournal of the American Chemical Society · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversité Laval
FundersJapan Society for the Promotion of ScienceTokuyama Science FoundationSupport for Pioneering Research Initiated by the Next GenerationNational Natural Science Foundation of ChinaAsahi Glass FoundationKonica Minolta Imaging Science Foundation
KeywordsChemistryPotassiumIonOrganic chemistry

Abstract

fetched live from OpenAlex

Potassium ion (K + ) is the most abundant metal ion in cells and plays an indispensable role in practically all biological systems. Although there have been reports of both synthetic and genetically encoded fluorescent K + indicators, there remains a need for an indicator that is genetically targetable, has high specificity for K + versus Na +, and has a high fluorescent response in the red to far-red wavelength range. Here, we introduce a series of chemigenetic K + indicators, designated as the HaloKbp1 series, based on the bacterial K + -binding protein (Kbp) inserted into HaloTag7 self-labeled with environmentally sensitive rhodamine derivatives. This series of high-performance indicators features high brightness in the red to far-red region, large intensiometric fluorescence changes, and a range of K d values. We demonstrate that they are suitable for the detection of physiologically relevant K + concentration changes such as those that result from the Ca 2+ -dependent activation of the BK potassium channel.

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 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 score0.009
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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.

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

Citations9
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicAnalytical Chemistry and SensorsFrench-language works237,207