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Record W2921207188 · doi:10.1080/14756366.2019.1587416

CRISPR/Cas9-based liver-derived reporter cells for screening of mPGES-1 inhibitors

2019· article· en· W2921207188 on OpenAlexaff
Zhanfei Chen, Xiaoling Cai, Man Li, Linlin Yan, Luxi Wu, Xiaoqian Wang, Nanhong Tang

Bibliographic record

VenueJournal of Enzyme Inhibition and Medicinal Chemistry · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsMinistry of Education and Child Care
FundersMedical Innovation Project of Fujian ProvinceNatural Science Foundation of Fujian Province
KeywordsFlow cytometryCRISPRCas9InflammationReporter geneImmunofluorescenceHigh-throughput screeningFluorescenceMolecular biologyBiologyChemistryCancer researchCell biologyBiochemistryGeneGene expressionImmunologyAntibody

Abstract

fetched live from OpenAlex

mPGES-1 is a terminal rate-limiting enzyme responsible for inflammation-induced PGE2 production. The inhibition of mPGES-1 has been considered as a safe and effective target for the treatment of inflammation and cancer. However, a specific, efficient, and simple method for high-throughput screening of mPGES-1 inhibitors is still lacking. In this study, we developed a fluorescence imaging strategy to monitor the expression of mPGES-1 via CRISPR/Cas9 knock-in system. Immunofluorescence colocalisation, Sanger sequencing, RNAi, and IL-1β treatment all confirmed the successful construction of mPGES-1 reporter cells. The fluorescence signal intensity of the reporter cells treated with four conventional mPGES-1 inhibitors was considerably attenuated via flow cytometry and fluorescent microplate reader, demonstrating that the reporter cells can be used as an efficient and convenient means for screening and optimising mPGES-1 inhibitors. Moreover, it provides a new technical support for the development of targeted small molecule compounds for anti-inflammatory and tumour therapy.

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 score0.031
Threshold uncertainty score0.491

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 head0.009
GPT teacher head0.273
Teacher spread0.264 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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