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Record W2444320521 · doi:10.1021/jacs.5b05538

A Small-Molecule Photoactivatable Optical Sensor of Transmembrane Potential

2015· article· en· W2444320521 on OpenAlexfundno aff
Vincent Grenier, Alison Walker, Evan W. Miller

Bibliographic record

VenueJournal of the American Chemical Society · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of Canada
KeywordsChemistryMoleculeSmall moleculeNanotechnologyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This paper discloses the design, synthesis, and imaging applications of the first member of a new class of SPOTs, small-molecule photoactivatable optical sensors of transmembrane potential. SPOT2.1.Cl features an established voltage-sensitive dye, VoltageFluor2.1.Cl--or VF--capped with a dimethoxy-o-nitrobenzyl (DMNB) caging group to effectively diminish fluorescence of the VF dye prior to uncaging. SPOT2.1.Cl localizes to cell membranes and displays weak fluorescence until photoactivated. Illumination generates the parent VF dye which then optically reports on changes in the membrane voltage. After photoactivation with spatially restricted light, SPOT2.1.Cl-loaded cells display bright, voltage-sensitive fluorescence associated with the plasma membrane, while neighboring cells remain dark. Activated SPOT reports on action potentials in single trials. SPOT can be activated in neuron cell bodies or uncaged in dendrites to enable structural tracing via "backfilling" of the dye to the soma, followed by functional imaging in the labeled cell. The combination of cellular specificity achieved through spatially defined patterns of illumination, coupled with the fast, sensitive, and noncapacitive voltage sensing characteristics of VF dyes makes SPOT2.1.Cl a useful tool for interrogating both structure and function of neuronal systems.

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 score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.240
Teacher spread0.222 · 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

Citations49
Published2015
Admission routes1
Has abstractyes

Explore more

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