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Record W4386050341 · doi:10.1002/inmd.20230008

Engineering multifunctional adhesive hydrogel patches for biomedical applications

2023· article· en· W4386050341 on OpenAlexaff
Aishik Chakraborty, Shana Alexander, Wei Luo, Narisse Al‐Salam, Mia Van Oirschot, Sudhir H. Ranganath, Subrata Chakrabarti, Arghya Paul

Bibliographic record

VenueInterdisciplinary medicine · 2023
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsWestern University
Fundersnot available
KeywordsSelf-healing hydrogelsTransdermalAdhesiveNanotechnologyMaterials scienceDrug deliveryTissue engineeringTransdermal patchBiomedical engineeringPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Traditional patches, such as sticking plaster or acrylic adhesives used for over a hundred years, lack functionality. To address this issue of poor functionality, adhesive hydrogel patches have emerged as an efficient bioactive multifunctional alternative. Hydrogels are three‐dimensional, water‐swellable, and polymeric materials closely resembling the native tissue architecture. The physicochemical properties of hydrogels can be modified easily, allowing them to be suitable for various biomedical applications. Moreover, adhesive properties can be imparted to hydrogels through physicochemical manipulations, making them ideal candidates for supplementing or replacing traditional sticking plaster. As a result, sticky hydrogel patches are widely used for transdermal drug delivery and have even found commercial purposes. Beyond transdermal delivery, such hydrogel patches have also found applications in cardiac therapy, cancer research, and biosensing, among other applications. In this mini‐review, we critically discuss the challenges of fabricating multifunctional adhesive hydrogel patches. Furthermore, we introduce some of the chemical strategies involved with fabricating the patches. We also review their emerging biomedical applications. Finally, we explore their potential future in the flourishing field of tissue engineering and drug delivery.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.030
GPT teacher head0.332
Teacher spread0.303 · 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 designSimulation or modeling
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

Citations62
Published2023
Admission routes1
Has abstractyes

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