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Record W3002370920 · doi:10.1242/jcs.240390

What biologists want from their chloride reporters – a conversation between chemists and biologists

2020· review· en· W3002370920 on OpenAlexaff
Matthew Zajac, Kasturi Chakraborty, Sonali Saha, Vivek Mahadevan, Daniel T. Infield, Alessio Accardi, Zhaozhu Qiu, Yamuna Krishnan

Bibliographic record

VenueJournal of Cell Science · 2020
Typereview
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsUniversity of Toronto
FundersNational Institutes of HealthThe Wellcome Trust DBT India AllianceDepartment of Biotechnology, Ministry of Science and Technology, IndiaWellcome Trust
KeywordsChlorideContext (archaeology)BiologyChloride channelConversationTransporterComputational biologyBiophysicsBiochemistryChemistryCommunicationGeneOrganic chemistry

Abstract

fetched live from OpenAlex

Impaired chloride transport affects diverse processes ranging from neuron excitability to water secretion, which underlie epilepsy and cystic fibrosis, respectively. The ability to image chloride fluxes with fluorescent probes has been essential for the investigation of the roles of chloride channels and transporters in health and disease. Therefore, developing effective fluorescent chloride reporters is critical to characterizing chloride transporters and discovering new ones. However, each chloride channel or transporter has a unique functional context that demands a suite of chloride probes with appropriate sensing characteristics. This Review seeks to juxtapose the biology of chloride transport with the chemistries underlying chloride sensors by exploring the various biological roles of chloride and highlighting the insights delivered by studies using chloride reporters. We then delineate the evolution of small-molecule sensors and genetically encoded chloride reporters. Finally, we analyze discussions with chloride biologists to identify the advantages and limitations of sensors in each biological context, as well as to recognize the key design challenges that must be overcome for developing the next generation of chloride sensors.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.044
GPT teacher head0.295
Teacher spread0.251 · 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 designOther design
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

Citations45
Published2020
Admission routes1
Has abstractyes

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