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Record W4225471014 · doi:10.1038/s41467-021-27332-2

A genetically encoded fluorescent biosensor for extracellular l-lactate

2021· article· en· W4225471014 on OpenAlexafffund
Yusuke Nasu, Ciaran Murphy‐Royal, Yurong Wen, Jordan N. Haidey, Rosana S. Molina, Abhi Aggarwal, Shuce Zhang, Yuki Kamijo, Marie-Ève Paquet, Kaspar Podgorski, Mikhail Drobizhev, Jaideep S. Bains, M. Joanne Lemieux, Grant R. Gordon, Robert E. Campbell

Bibliographic record

VenueNature Communications · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversité LavalCentre Hospitalier de l’Université de MontréalUniversity of AlbertaUniversity of Calgary
FundersNational Institute of Neurological Disorders and StrokeToyota Physical and Chemical Research InstituteJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyUniversity of AlbertaNatural Sciences and Engineering Research Council of CanadaUniversity of TokyoNational Cancer InstituteGovernment of CanadaCanadian Institutes of Health ResearchPrecise Measurement Technology Promotion Foundation
KeywordsExtracellularBiosensorIntracellularFluorescenceCell biologyBiochemistryGreen fluorescent proteinChemistryBiologyBiophysicsGene

Abstract

fetched live from OpenAlex

L-Lactate, traditionally considered a metabolic waste product, is increasingly recognized as an important intercellular energy currency in mammals. To enable investigations of the emerging roles of intercellular shuttling of L-lactate, we now report an intensiometric green fluorescent genetically encoded biosensor for extracellular L-lactate. This biosensor, designated eLACCO1.1, enables cellular resolution imaging of extracellular L-lactate in cultured mammalian cells and brain tissue.

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)0.001
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.392
Teacher spread0.307 · 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

Citations108
Published2021
Admission routes2
Has abstractyes

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