MétaCan
Menu
Back to cohort
Record W4398132661 · doi:10.1002/adfm.202404934

Calmodulin‐Based Dynamic Protein Hydrogels with Three Distinct Mechanical Stiffness

2024· article· en· W4398132661 on OpenAlexafffund
Qingyuan Bian, Na Kong, Sena Arslan, Hongbin Li

Bibliographic record

VenueAdvanced Functional Materials · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceSelf-healing hydrogelsStiffnessCalmodulinComposite materialBiophysicsNanotechnologyCalciumPolymer chemistry

Abstract

fetched live from OpenAlex

Abstract Stimuli‐responsive hydrogels that leverage protein conformational changes are of significant interest in the design of dynamic materials applicable in a myriad of fields, such as drug delivery, actuators, biosensors, and microfluidics. The small calcium binding protein calmodulin (CaM), which undergoes three‐stage conformational changes upon successive binding with Ca 2+ and specific ligands, offers a mechanism to create dynamic hydrogels with three distinct physical states. In this work, a CaM‐based recombinant protein hydrogel is engineered using [Ru(bpy) 3 ] 2+ ‐mediated photo‐crosslinking. This hydrogel displays the ability to reversibly increase its Young's modulus by 1.5‐fold and ∼7‐fold, respectively, upon binding with Ca 2+ and subsequent interaction with trifluoperazine. The magnitude of stiffness changes is tunable by adjusting the length proportion of dynamic and static domains and modifying protein content. This tunable and reversible control over hydrogel mechanics is further utilized to engineer shape‐morphing materials, highlighting the versatile potential of this CaM‐based protein hydrogel for diverse applications.

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 score0.084
Threshold uncertainty score0.851

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations15
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Functional MaterialsSame topicHydrogels: synthesis, properties, applicationsFrench-language works237,207