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Record W2512104978 · doi:10.1002/anie.201606603

Profiling Metal Oxides with Lipids: Magnetic Liposomal Nanoparticles Displaying DNA and Proteins

2016· article· en· W2512104978 on OpenAlexafffund
Feng Wang, Xiaohan Zhang, Yibo Liu, Zhi Yuan Lin, Biwu Liu, Juewen Liu

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsLiposomeCalceinLipid bilayerChemistryNanoparticleBiomoleculeBiosensorNanomedicineMembraneChemical engineeringNanotechnologyMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

Metal oxides include many important materials with various surface properties. For biomedical and analytical applications, it is desirable to engineer their biocompatible interfaces. Herein, a phosphocholine liposome (DOPC) and its headgroup dipole flipped counterpart (DOCP) were mixed with ten common oxides. Using the calcein leakage assay, cryo-TEM, and ζ-potential measurement, these oxides were grouped into three types. The type 1 oxides (Fe3 O4 , TiO2 , ZrO2 , Y2 O3 , ITO, In2 O3 , and Mn2 O3 ) form supported bilayers only with DOCP. Type 2 (SiO2 ) forms supported bilayers only with DOPC; type 3 (ZnO and NiO) are cationic and damage lipid membranes. Magnetic Fe3 O4 nanoparticles were further studied for conjugation of fluorophores, proteins, and DNA to the supported DOCP bilayers via lipid headgroup labeling, covalent linking, or lipid insertion. Delivery of the conjugates to cells and selective DNA hybridization were demonstrated. This work provides a general solution for coating the type 1 oxides with a simple mixing in water, facilitating applications in biosensing, separation, and nanomedicine.

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 score0.004
Threshold uncertainty score0.333

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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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

Citations54
Published2016
Admission routes2
Has abstractyes

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