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Referee report. For: The identification of high-performing antibodies for TDP-43 for use in Western Blot, immunoprecipitation and immunofluorescence [version 1; peer review: 2 approved]

2023· article· en· W4416610106 on OpenAlexfundno aff
Soichi Watanabe

Bibliographic record

VenueFaculty of 1000 Research Ltd · 2023
Typearticle
Languageen
FieldMedicine
TopicAutoimmune Bullous Skin Diseases
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchGenentechGovernment of CanadaOntario Genomics InstituteEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAMitacsOntario GenomicsGenome CanadaBayerMotor Neurone Disease AssociationPfizerBristol-Myers Squibb
KeywordsImmunoprecipitationImmunofluorescenceIdentification (biology)AntibodyIndirect immunofluorescenceMonoclonal antibody

Abstract

fetched live from OpenAlex

TAR DNA-binding protein 43 (TDP-43) is a DNA/RNA binding protein playing a critical role in the regulation of transcription, splicing and RNA stability. Mutations in TARDBP leading to aggregation, are suspected to be a characteristic feature of various neurogenerative diseases. The lack of well-characterized anti- TDP-43 antibodies acts as a barrier to establish reproducible TDP-43 research. In this study, we characterized eighteen TDP-43 commercial antibodies for Western blot, immunoprecipitation, and immunofluorescence using a standardized experimental protocol based on comparing read-outs in knockout cell lines and isogenic parental controls. We identified many well-performing antibodies and encourage readers to use this report as a guide to select the most appropriate antibody for their specific needs.

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)0.007
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.444
Threshold uncertainty score0.794

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.102
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.4440.248

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 head0.080
GPT teacher head0.402
Teacher spread0.323 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractno

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