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Record W4402309396 · doi:10.1021/acsnano.4c09342

Colloidal Hydrogel with Staged Sequestration and Release of Molecules Undergoing Competitive Binding

2024· article· en· W4402309396 on OpenAlexafffund
Yuhang Huang, Nashmia Zia, Yingshan Ma, Terek Li, Gilbert C. Walker, Hani E. Naguib, Eugenia Kumacheva

Bibliographic record

VenueACS Nano · 2024
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsMoleculeBiophysicsDiffusionColloidChemistryNanotechnologyChemical engineeringControlled releaseMaterials scienceOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Competitive binding of distinct molecules in the hydrogel interior can facilitate dynamic exchange between the hydrogel and the surrounding environment. The ability to control the rates of sequestration and release of these molecules would enhance the hydrogel's functionality and enable targeting of a specific task. Here, we report the design of a colloidal hydrogel with two distinct pore dimensions to achieve staged, diffusion-controlled scavenging and release dynamics of molecules undergoing competitive binding. The staged scavenging and release strategy was shown for CpG oligodeoxynucleotide (ODN) and human epidermal growth factor (hEGF), two molecules exhibiting different affinities to the quaternary ammonium groups of the hydrogel. Fast ODN scavenging from the ambient environment occurred via diffusion through submicrometer-size hydrogel pores, while delayed hEGF release from the hydrogel was governed by its diffusion through nanometer-size pores. The results of the experiments were in agreement with simulation results. The significance of staged ODN-hEGF exchange was highlighted by the dual anti-inflammation and tissue proliferation hydrogel performance.

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 score0.025
Threshold uncertainty score0.260

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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
Published2024
Admission routes2
Has abstractyes

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