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Record W2999318389 · doi:10.3791/60317

A Macrophage Reporter Cell Assay to Examine Toll-Like Receptor-Mediated NF-kB/AP-1 Signaling on Adsorbed Protein Layers on Polymeric Surfaces

2020· article· en· W2999318389 on OpenAlexafffund
Laura A. McKiel, Kimberly A. Woodhouse, Lindsay E. Fitzpatrick

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsBiomaterialCell biologyProtein adsorptionMacrophageInnate immune systemForeign-body giant cellChemistryBiophysicsImmune systemMaterials scienceIn vitroReceptorBiologyBiochemistryAdsorptionImmunologyNanotechnologyMedicinePathology

Abstract

fetched live from OpenAlex

The persistent inflammatory host response to an implanted biomaterial, known as the foreign body reaction, is a significant challenge in the development and implementation of biomedical devices and tissue engineering constructs. Macrophages, an innate immune cell, are key players in the foreign body reaction because they remain at the implant site for the lifetime of the device, and are commonly studied to gain an understanding of this detrimental host response. Many biomaterials researchers have shown that adsorbed protein layers on implanted materials influence macrophage behavior, and subsequently impact the host response. The methods in this paper describe an in vitro model using adsorbed protein layers containing cellular damage molecules on polymer biomaterial surfaces to assess macrophage responses. An NF-кB/AP-1 reporter macrophage cell line and the associated colorimetric alkaline phosphatase assay were used as a rapid method to indirectly examine NF-кB/AP-1 transcription factor activity in response to complex adsorbed protein layers containing blood proteins and damage-associated molecular patterns, as a model of the complex adsorbed protein layers formed on biomaterial surfaces in vivo.

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 categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

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 head0.026
GPT teacher head0.345
Teacher spread0.320 · 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.

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
Published2020
Admission routes2
Has abstractyes

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