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Record W4380422444 · doi:10.1002/adma.202303301

Mechanoresponsive Drug Release from a Flexible, Tissue‐Adherent, Hybrid Hydrogel Actuator

2023· article· en· W4380422444 on OpenAlexaff
Keegan Mendez, William Whyte, Benjamin R. Freedman, Yiling Fan, Claudia E. Varela, Manisha Singh, Juan C. Cintron-Cruz, Sandra E. Rothenbücher, Jianyu Li, David Mooney, Ellen T. Roche

Bibliographic record

VenueAdvanced Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial ResearchNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institutes of HealthMassachusetts Institute of Technology
KeywordsActuatorMaterials scienceDrug deliverySoft roboticsBiomedical engineeringSelf-healing hydrogelsNanotechnologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Soft robotic technologies for therapeutic biomedical applications require conformal and atraumatic tissue coupling that is amenable to dynamic loading for effective drug delivery or tissue stimulation. This intimate and sustained contact offers vast therapeutic opportunities for localized drug release. Herein, a new class of hybrid hydrogel actuator (HHA) that facilitates enhanced drug delivery is introduced. The multi-material soft actuator can elicit a tunable mechanoresponsive release of charged drug from its alginate/acrylamide hydrogel layer with temporal control. Dosing control parameters include actuation magnitude, frequency, and duration. The actuator can safely adhere to tissue via a flexible, drug-permeable adhesive bond that can withstand dynamic device actuation. Conformal adhesion of the hybrid hydrogel actuator to tissue leads to improved mechanoresponsive spatial delivery of the drug. Future integration of this hybrid hydrogel actuator with other soft robotic assistive technologies can enable a synergistic, multi-pronged treatment approach for the treatment of disease.

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 categoriesInsufficient payload (model declined to judge)
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 score0.022
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.002

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 head0.238
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

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

Citations57
Published2023
Admission routes1
Has abstractyes

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