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Record W3097471624

3D-Bioprinted Aptamer-Functionalized Bio-inks for Spatiotemporally Controlled Growth Factor Delivery

2020· article· en· W3097471624 on OpenAlexfundno aff
Deepti Rana, Vasileios D. Trikalitis, Vincent R. Rangel, Ajoy Kandar, N. Salehi Nik, Jeroen Rouwkema

Bibliographic record

VenueUniversity of Twente Research Information · 2020
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNational Institute of Environmental Health SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesBiotechnology Industry Research Assistance CouncilNIHR Oxford Biomedical Research CentreBiomedical Research CouncilAgencia Estatal de InvestigaciónNational Physical LaboratoryJapan Society for the Promotion of ScienceNational Medical Research CouncilNational Heart, Lung, and Blood InstituteBiotechnology and Biological Sciences Research CouncilMedical Research CouncilHorizon 2020 Framework ProgrammeNational Institutes of HealthEuropean Regional Development FundDutch Arthritis AssociationAdvanced Materials and Bioengineering ResearchAnimal Free Research UKRWTH Aachen UniversityFundação Luso-Americana para o DesenvolvimentoÖterreichisches Exzellenzzentrum für TribologieEngineering and Physical Sciences Research CouncilLeverhulme TrustEuropean CommissionUniversitat Jaume IMinistry of Science and Technology, TaiwanUniversity of SurreyAmt der NÖ LandesregierungEuskal Herriko UnibertsitateaChina Scholarship CouncilMinisterio de Economía y CompetitividadArthritis SocietyDeutsche ForschungsgemeinschaftNederlandse Organisatie voor Wetenschappelijk OnderzoekFondation pour la Recherche MédicaleParticulate Fluids Processing Centre, University of MelbourneRussian Foundation for Basic ResearchCentro de Investigação em Materiais Cerâmicos e CompósitosAO FoundationNational Institute for Health and Care ResearchCommonwealth Scientific and Industrial Research OrganisationNewcastle UniversityRoyal Academy of EngineeringEusko JaurlaritzaDutch Arthritis SocietyInselspital, Universitätsspital BernMemorial Sloan-Kettering Cancer CenterWellcome TrustCanadian Institutes of Health ResearchUniversity of SouthamptonRussian Science FoundationMinisterio de Ciencia, Innovación y UniversidadesTürkiye Bilimsel ve Teknolojik Araştırma KurumuAgence Nationale de la RechercheAustralian GovernmentAgency for Science, Technology and ResearchLeids Universitair Medisch CentrumScience Foundation IrelandAustralian National Fabrication FacilityImperial College LondonNational Science FoundationCincinnati Children's Hospital Medical CenterCleveland State UniversityRoyal College of Surgeons of EdinburghCampus FranceZonMwFundação para a Ciência e a TecnologiaBill and Melinda Gates FoundationMonash University
KeywordsAptamerSelf-healing hydrogelsGrowth factorVascular endothelial growth factorNanotechnologyChemistryTissue engineeringNanogelMaterials scienceBioconjugationBiophysicsDrug deliveryBiomedical engineeringBiochemistryBiologyMolecular biologyPolymer chemistryVEGF receptors
DOInot available

Abstract

fetched live from OpenAlex

Introduction Spatiotemporally controlled growth factors delivering systems are crucial for tissue engineering. However, most of the current strategies for growth factors delivery often focuses on the immobilization or coupling of growth factors within the engineered matrices (hydrogel) via various linker proteins or peptides. These systems provide passive release rates and growth factor delivery on demand, but fail to adapt their release rates in accordance with the tissue development. To overcome this limitation, the present study employed nucleic acid based aptamers for achieving spatiotemporally controlled growth factor delivery. Aptamers are affinity ligands selected from DNA/RNA libraries to recognize proteins with high affinity and specificity.1 Aptamer based growth factor delivery systems are able to load/release multiple growth factors on demand with high specificity. In the present study, the authors have 3D-bioprinted aptamer-functionalized bio-inks to evaluate their potential for growth factor sequestering, programmable release and for studying their effect on vascular network formation. Methods The aptamer-functionalized hydrogels were prepared via photo-polymerization of gelatin methacryloyl (GelMA) and acrydite functionalized aptamers having sequence specific for binding to vascular endothelial growth factor (VEGF165). Visible light photoinitiator, tris(2,2′-bipyridyl)dichloro-ruthenium(II) hexahydrate with sodium persulfate was used. The 3D-bioprinting experiments were carried out using Rokit Invivo 3D printer. The viscoelastic properties of the bio-inks were evaluated and compared with control GelMA bio-ink. To study the programmable growth factor release efficiency, VEGF antibody immunostaining was used. For studying the effect of triggered growth factor release on vascular network formation, human umbilical vein endothelial cells (HUVECs) and mesenchymal stem cells (MSCs) were encapsulated within the bio-inks. Results & Discussion The results obtained from VEGF antibody immunostainings confirmed the sequestration and triggered release of VEGF in response to complementary sequence addition from the 3D bioprinted construct after 5 days of culture. The bioprinted construct showed high cellular viability. The F-Actin/DAPI staining showed cellular sprouting and vascular network formation within the 3D printing aptamer functionalized bio-ink regions. In addition, the endothelial cells showed variations in cellular organization based on the VEGF bound aptamer availability within the bioprinted construct. These observations altogether confirms the bioactivity of VEGF bound aptamers within the printed constructs. Conclusions The present study shows the vasculogenic potential of 3D bioprinted aptamer-functionalized bio-inks via spatiotemporally controlling VEGF availability within the hydrogel system. Acknowledgements: This work is supported by an ERC Consolidator Grant under grant agreement no 724469. References 1. M.R. Battig, et. al., J. Am. Chem. Soc. 134 (2012) 12410-12413.

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)0.001
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 score0.698
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.269
Teacher spread0.221 · 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".

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Citations1
Published2020
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