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Record W2949222337 · doi:10.1002/bit.27084

Construction of a sensitive pyrogen‐testing cell model by site‐specific knock‐in of multiple genes

2019· article· en· W2949222337 on OpenAlexafffund
Ruobi Hu, Hui Li, Zhen Lei, Qing Han, Xiuyan Yu, Na Zhou, Xuehui Zhang, Yiqing Mao, Xi Wang, David M. Irwin, Gang Niu, Huanran Tan

Bibliographic record

VenueBiotechnology and Bioengineering · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsTLR4CD14ChemistryCell biologyMolecular biologyBiologySignal transductionFlow cytometry

Abstract

fetched live from OpenAlex

A pyrogen test is crucial for evaluating the safety of drugs and medical equipment, especially those involved in injections. As existing pyrogen tests, including the rabbit pyrogen test, the limulus amoebocyte lysate (LAL) test and the monocyte activation test have limitations, development of new models for pyrogen testing is necessary. Here we develop a sensitive cell model for pyrogen test based on the lipopolysaccharides (LPS) signal pathway. TLR4, MD2, and CD14 play key roles in the LPS-mediated pyrogen reaction. We established a new TLR4/MD2/CD14-specific overexpressing knock-in cell model using the CRISPR/CAS9 technology and homologous recombination to detect LPS. Stimulation of our TLR4/CD14/MD2 knock-in cell line model with LPS leads to the release of the cytokines IL-6 and TNF-alpha, with a detection limit of 0.005 EU/ml, which is greatly lower than the lower limit of 0.015 EU/ml detected by the Tachypleus amebocyte lysate (TAL) assay.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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