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Record W4401941486 · doi:10.1021/acs.biomac.4c00690

Stimuli-Responsive Polymers at the Interface with Biology

2024· review· en· W4401941486 on OpenAlexafffund
Nathan R. B. Boase, Elizabeth R. Gillies, Rubayn Goh, Roxanne E. Kieltyka, John B. Matson, Fenghua Meng, Amitav Sanyal, Ondřej Sedláček

Bibliographic record

VenueBiomacromolecules · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsWestern University
FundersDivision of Materials ResearchGrantová Agentura České RepublikyEuropean Research CouncilNational Natural Science Foundation of ChinaCanada Research Chairs
KeywordsNanotechnologySelf-healing hydrogelsSmall moleculeComputer scienceVariety (cybernetics)ChemistryBiochemical engineeringMaterials scienceEngineeringBiochemistryArtificial intelligenceOrganic chemistry

Abstract

fetched live from OpenAlex

There has been growing interest in polymeric systems that break down or undergo property changes in response to stimuli. Such polymers can play important roles in biological systems, where they can be used to control the release of therapeutics, modulate imaging signals, actuate movement, or direct the growth of cells. In this Perspective, after discussing the most important stimuli relevant to biological applications, we will present a selection of recent exciting developments. The growing importance of stimuli-responsive polysaccharides will be discussed, followed by a variety of stimuli-responsive polymeric systems for the delivery of small molecule drugs and nucleic acids. Switchable polymers for the emerging area of therapeutic response measurement in theranostics will be described. Then, the diverse functions that can be achieved using hydrogels cross-linked covalently, as well as by various dynamic approaches will be presented. Finally, we will discuss some of the challenges and future perspectives for the field.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.031
GPT teacher head0.333
Teacher spread0.301 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations48
Published2024
Admission routes2
Has abstractyes

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