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Record W3048532146 · doi:10.1002/btm2.10180

In situ forming microporous gelatin methacryloyl hydrogel scaffolds from thermostable microgels for tissue engineering

2020· article· en· W3048532146 on OpenAlexfundno aff
Nicole Zoratto, Donatella Di Lisa, Joseph de Rutte, Md. Nurus Sakib, Angelo Roncalli Alves e Silva, Ali Tamayol, Dino Di Carlo, Ali Khademhosseini, Amir Sheikhi

Bibliographic record

VenueBioengineering & Translational Medicine · 2020
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institutes of HealthUniversidade de FortalezaCanadian Institutes of Health ResearchNational Institute of General Medical SciencesSapienza Università di RomaPennsylvania State University
KeywordsSelf-healing hydrogelsGelatinMicroporous materialTissue engineeringMaterials scienceScaffoldMicroscale chemistryChemical engineeringPolymerNanotechnologyBiomedical engineeringPolymer chemistryChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Converting biopolymers to extracellular matrix (ECM)‐mimetic hydrogel‐based scaffolds has provided invaluable opportunities to design in vitro models of tissues/diseases and develop regenerative therapies for damaged tissues. Among biopolymers, gelatin and its crosslinkable derivatives, such as gelatin methacryloyl (GelMA), have gained significant importance for biomedical applications due to their ECM‐mimetic properties. Recently, we have developed the first class of in situ forming GelMA microporous hydrogels based on the chemical annealing of physically crosslinked GelMA microscale beads (microgels), which addressed several key shortcomings of bulk (nanoporous) GelMA scaffolds, including lack of interconnected micron‐sized pores to support on‐demand three‐dimensional‐cell seeding and cell–cell interactions. Here, we address one of the limitations of in situ forming microporous GelMA hydrogels, that is, the thermal instability (melting) of their physically crosslinked building blocks at physiological temperature, resulting in compromised microporosity. To overcome this challenge, we developed a two‐step fabrication strategy in which thermostable GelMA microbeads were produced via semi‐photocrosslinking, followed by photo‐annealing to form stable microporous scaffolds. We show that the semi‐photocrosslinking step (exposure time up to 90 s at an intensity of ~100 mW/cm2 and a wavelength of ~365 nm) increases the thermostability of GelMA microgels while decreasing their scaffold forming (annealing) capability. Hinging on the tradeoff between microgel and scaffold stabilities, we identify the optimal crosslinking condition (exposure time ~60 s) that enables the formation of stable annealed microgel scaffolds. This work is a step forward in engineering in situ forming microporous hydrogels made up from thermostable GelMA microgels for in vitro and in vivo applications at physiological temperature well above the gelatin melting point.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.025
GPT teacher head0.271
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations62
Published2020
Admission routes1
Has abstractyes

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