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Record W2285785821 · doi:10.1021/acs.langmuir.5b04643

Chemisorption Mechanism of DNA on Mg/Fe Layered Double Hydroxide Nanoparticles: Insights into Engineering Effective SiRNA Delivery Systems

2016· article· en· W2285785821 on OpenAlexafffund
Mingsheng Lü, Zhi Shan, Kori A. Andrea, Bruce C. MacDonald, Stefanie Beale, Dennis Curry, Li Wang, Shujun Wang, Ken D. Oakes, Craig Bennett, Wenhui Wu, Xu Zhang

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldMaterials Science
TopicLayered Double Hydroxides Synthesis and Applications
Canadian institutionsUniversity of WaterlooAcadia UniversityCape Breton University
FundersCanadian Institutes of Health ResearchNova Scotia Health Research FoundationNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsHydroxideGene deliveryNanoparticleChemistryChemisorptionDNADrug deliveryNanotechnologyPolymerMaterials scienceInorganic chemistryGeneAdsorptionOrganic chemistryBiochemistryGenetic enhancement

Abstract

fetched live from OpenAlex

Layered double hydroxide nanoparticles (LDH NPs) have attracted interest as an effective gene delivery vehicle in biomedicine. Recent advances in clinic trials have demonstrated the efficacy of Mg/Fe LDHs for hyperphosphatemia treatment, but their feasibility for gene delivery has not been systematically evaluated. As a starting point, we aimed to study the interaction between oligo-DNA and Mg/Fe LDH NPs. Our investigation revealed the chemisorption mechanism of DNA on Mg/Fe LDH surfaces, wherein the phosphate backbone of the DNA polymer coordinates with the metal cations of the LDH lattice via the ligand-exchange process. This mechanistic insight may facilitate future gene delivery applications using Mg/Fe LDH NPs.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations31
Published2016
Admission routes2
Has abstractyes

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