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Record W2909531635 · doi:10.1002/jbm.b.34307

Synthetic fluorinated polyamides as efficient gene vectors

2019· article· en· W2909531635 on OpenAlexaff
Mian Wang, Han Xue, Min Gao, Qingli Wang, Hai‐Jie Yang

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsHudbay Minerals (Canada)
FundersXinxiang Medical UniversityFoundation of Henan Educational CommitteeNational Natural Science Foundation of China
KeywordsPolyethyleniminePolyamideTransfectionCationic polymerizationCytotoxicityMaterials sciencePolymerHEK 293 cellsGene deliveryCell cultureBiophysicsCombinatorial chemistryGenePolymer chemistryChemical engineeringBiologyChemistryBiochemistryGeneticsIn vitroComposite material

Abstract

fetched live from OpenAlex

Linear fluorinated polyamides with reversible cationic charges are feasibly prepared to be used as highly efficient gene vectors in HEK293 cell line. Due to the uniform polymer structure, the relationship between the physicochemical properties and transfection efficiency could be unambiguously investigated. The different efficiency in the application of gene delivery between the parent polyethylenimine (PEI) and the polyamides is directly associated with the differences in chemical and physical properties between secondary amines and fluorinated amides. We found that fluorination not only increases the cellular uptake of polymer/DNA polyplexes, but it also decreases cytotoxicity in terms of inducing lower concentrations of proinflammatory cytokine TNF-α. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 2132-2139, 2019.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.019
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.318
Teacher spread0.297 · 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; both teacher heads agree on what is shown here.

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
Published2019
Admission routes1
Has abstractyes

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