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Identification of high-performing antibodies for amyloid-beta precursor protein for use in Western Blot, immunoprecipitation and immunofluorescence

2023· preprint· en· W4385700889 on OpenAlexafffund
Riham Ayoubi, Maryam Fotouhi, Donovan Worrall, Kathleen Southern, Carl Laflamme

Bibliographic record

VenueF1000Research · 2023
Typepreprint
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersGenentechCanadian Institutes of Health ResearchInnovative Medicines InitiativeGovernment of CanadaOntario Genomics InstituteEmory UniversityEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAMitacsOntario GenomicsGenome CanadaBristol-Myers SquibbBayerPfizer
KeywordsImmunoprecipitationWestern blotAntibodyAmyloid precursor proteinImmunofluorescenceAmyloid betaMolecular biologyAmyloid (mycology)Transmembrane proteinBETA (programming language)BlotBiologyChemistryAlzheimer's diseaseImmunologyBiochemistryPathologyMedicineDiseaseGeneComputer scienceReceptor

Abstract

fetched live from OpenAlex

The amyloid-beta precursor protein is a transmembrane protein expressed in many tissues and highly concentrated in the brain. The protein is of significant interest due to its involvement in the generation of amyloidogenic β-amyloid peptides, prone to plaque formation that is characteristic of Alzheimer’s Disease. The scientific community would benefit from the availability of high-quality anti-amyloid-beta precursor protein antibodies to enhance reproducible research on this target. In this study, we characterized eleven amyloid-beta precursor protein 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 high-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)0.003
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.021
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0210.025

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 head0.387
Teacher spread0.301 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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