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Record W3167734692 · doi:10.1021/acs.biomac.1c00378

Bio-Orthogonal Nanogels for Multiresponsive Release

2021· article· en· W3167734692 on OpenAlexaff
Mohammad Shafee Alkanawati, Marina Machtakova, Katharina Landfester, Héloïse Thérien‐Aubin

Bibliographic record

VenueBiomacromolecules · 2021
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsMemorial University of Newfoundland
FundersMax-Planck-GesellschaftDeutsche Forschungsgemeinschaft
KeywordsNanogelChemistryOxidizing agentControlled releaseDrug deliveryCombinatorial chemistryHydrazoneAqueous solutionBiophysicsNanotechnologyMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Responsive nanogel systems are interesting for the drug delivery of bioactive molecules due to their high stability in aqueous media. The development of nanogels that are able to respond to biochemical cues and compatible with the encapsulation and the release of large and sensitive payloads remains challenging. Here, multistimuli-responsive nanogels were synthesized using a bio-orthogonal and reversible reaction and were designed for the selective release of encapsulated cargos in a spatiotemporally controlled manner. The nanogels were composed of a functionalized polysaccharide cross-linked with pH-responsive hydrazone linkages. The effect of the pH value of the environment on the nanogels was fully reversible, leading to a reversible control of the release of the payloads and a "stop-and-go" release profile. In addition to the pH-sensitive nature of the hydrazone network, the dextran backbone can be degraded through enzymatic cleavage. Furthermore, the cross-linkers were designed to be responsive to oxidoreductive cues. Disulfide groups, responsive to reducing environments, and thioketal groups, responsive to oxidative environments, were integrated into the nanogel network. The release of model payloads was investigated in response to changes in the pH value of the environment or to the presence of reducing or oxidizing agents.

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)0.001
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 score0.013
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.001

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 head0.015
GPT teacher head0.259
Teacher spread0.243 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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