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Record W7065019532

3D Bioprinted Respiratory Tissue Scaffolds for Disease Modelling Applications

2024· dissertation· en· W7065019532 on OpenAlexfundno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaTechnische Universität BerlinInnovation SaskatchewanUniversity of Saskatchewan
KeywordsTissue engineeringBiomaterialRegenerative medicine3D bioprintingSelf-healing hydrogelsRegeneration (biology)
DOInot available

Abstract

fetched live from OpenAlex

Respiratory tissue engineering (RTE) aims to develop functional tissue constructs for regenerative or modelling applications by using engineering approaches. Among these approaches, the recently emerging technique of bioprinting is promising as it allows for the repeatable creation of hierarchical cell-containing structures, thus providing the ability to create functional tissue constructs/ models. However, there are still challenges in the use of this approach in RTE, primarily related to generating physiologically relevant constructs that recapitulate the complexity of native tissues. Aspects including biomaterial selection, incorporating accurate biomechanical stimuli, and providing natural biochemical signals are all different facets requiring consideration in increasing the physiological relevance of bioprinted respiratory tissues. Based on the promise of RTE, this thesis aims at developing novel in vitro respiratory tissue constructs by means of bioprinting. To address research issues in the field of RTE, four specific objectives are set in this thesis including, (1) synthesis and characterization of an optimal bioink, (2) incorporation of biomechanical stimuli mimicking the native respiratory environment, (3) incorporation of biochemical stimuli through use of a nanoparticle-controlled release system, and (4) proof of concept application of the developed constructs in disease modelling. Objective (1) involves the investigation and synthesis of bioinks from hydrogels and characterization of the bioinks in terms of mechanical properties, printability, and biocompatibility. Alginate was selected as the base material due to its lack of biotoxicity and its ability to undergo ionic cross-linking, which allows for a high degree of printability; however, alginate expresses negligible cell-adhesion motifs. As collagen type I is the primary protein found throughout the connective tissue of the respiratory tract, its addition increases biocompatibility and cell adhesion. After synthesis, rheological characterization was used to inform selection of printing parameters and printability was assessed to ensure consistent structures that closely recapitulated the design could be created. Bulk compression testing was carried out to determine the compressive modulus, while tensile testing of printed scaffolds was used for determination of the 3D printed lattice properties. These mechanical properties were compared to that of native respiratory tissues to determine similitude. Finally, human pulmonary fibroblast proliferation and viability within the materials was assessed to ensure biocompatibility. The cumulation of all of these results was then used to select the most promising alginate/collagen biomaterial for further use in creation of a respiratory tissue construct. Work then continued in Objectives (2) and (3) to increase the physiological relevance of the engineered construct through two different pathways. First, a bioreactor mimicking the pressure changes and airflow conditions of the human lung was developed and tested to determine the effect that biomechanical stimulus had on cell growth within the construct. Conditions recapitulating shallow, normal, and heavy breathing were tested to determine the effect on degradation, tensile properties, and human pulmonary fibroblast and bronchial epithelial cell proliferation and viability. These experiments provided insight into the influence of mechanical stimulus on cell growth and ECM production, with normal breathing conditions leading to an increase in cell proliferation. Second, a nanoparticle system for controlled release of growth factor was developed and tested to determine the effect of including relevant biochemical stimulus had on cell development within the bioprinted construct. For investigation into biochemical stimulus, a chitosan-coated alginate nanoparticle system was synthesized using an emulsion technique. These particles were loaded with growth factor aimed at stimulating epithelial growth. Initially, release kinetics of the particle system were tested comparing coated/uncoated and static/dynamic conditions. Rheology and printability of the bioink containing the loaded particles was tested along with tensile properties of the printed scaffolds. Finally, the bioactivity of the loaded nanoparticles was assessed to determine the functionality of the controlled release system. Although cell proliferation appeared unaffected, confocal imaging demonstrated an increase in the formation of an epithelial barrier layer. Finally, in Objectives (4) the application of the designed constructs, including both biomechanical and biochemical stimulus, in disease modelling was then investigated. The bioink used was varied slightly through the addition of gelatin and characterized accordingly in terms of rheology, mechanical properties, printability, and biological properties. Following this, structures containing human pulmonary fibroblasts and monocytes were printed before seeding with human bronchial epithelial cells. These structures were cultured at an air-liquid interface before being infected with an influenza A virus. Cell viability, metabolism, and chemokine release were measured to determine the ability of these constructs to function as a disease model. This thesis presents comprehensive work on the creation of bioprinted respiratory tissue scaffolds for disease modelling applications. This work may pave the way to improving disease modelling and therapeutic screening pathways by providing a humanized intermediary between 2D and animal models.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.200
Teacher spread0.189 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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