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Record W4368617378 · doi:10.1523/eneuro.0341-22.2023

Evaluation and Validation of Commercially Available Dopamine Transporter Antibodies

2023· article· en· W4368617378 on OpenAlexafffund
Emma E. Russo, Lola E. Zovko, Reza Nazari, Hendrik W. Steenland, Amy J. Ramsey, Ali Salahpour

Bibliographic record

VenueeNeuro · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsDopamine transporterDopamineAntibodyTransporterComputational biologyNeuroscienceMedicineChemistryBiologyImmunologyDopaminergicBiochemistry

Abstract

fetched live from OpenAlex

With a wide variety of dopamine transporter (DAT) antibodies available commercially, it is important to validate which antibodies provide sufficient immunodetection for reproducibility purpose and for accurate analysis of DAT levels and/or location. Commercially available DAT antibodies that are commonly used were tested in western blotting (WB) on wild-type (WT) and DAT-knock-out (DAT-KO) brain tissue and with immunohistology (IH) techniques against coronal slices of unilaterally lesioned 6-OHDA rats, in addition to wild-type and DAT-knock-out mice. DAT-KO mice and unilateral 6-OHDA lesions in rats were used as a negative control for DAT antibody specificity. Antibodies were tested at various concentrations and rated based on signal detection varying from no signal to optimal signal detection. Commonly used antibodies, including AB2231 and PT-22 524-1-AP, did not provide specific DAT signals in WB and IH. Although certain antibodies provided a good DAT signal, such as SC-32258, D6944, and MA5-24796, they also presented nonspecific bands in WB. Many DAT antibodies did not detect the DAT as advertised, and this characterization of DAT antibodies may provide a guide for immunodetection of DAT for molecular studies.

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 score0.042
Threshold uncertainty score0.449

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 studies0.0000.000
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 head0.087
GPT teacher head0.329
Teacher spread0.242 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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