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Record W7030156718

Macroporous Hydrogels for Tissue Engineering and Wound Care

2023· dissertation· en· W7030156718 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMcMaster UniversityCalifornia HIV/AIDS Research Program
KeywordsSelf-healing hydrogelsWound careFusidic acidWound healingWound dressingAntimicrobialDrug deliveryContext (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Hydrogels are three-dimension networks of water-soluble polymer chains and have attracted interest in biomedical engineering, targeted drug delivery, tissue engineering, and regenerative medicine due to their ability to retain water coupled with their highly tunable physicochemical and biological properties. In the specific context of wound care, hydrogels can both maintain high wound hydration as well as absorb and manage wound exudate, both of which are major challenges in wound care. Hydrogel wound dressings can simultaneously deliver medication directly to the wound to suppress or treat infections, including antibiotic-resistant strains such as Methicillin-resistant S. aureus (MRSA). This thesis develops two wound care products that can address challenges in the selection and delivery of drugs to treat antibiotic-resistant strain infections: (1) in situ-gelling poly(oligoethylene glycol methacrylate) (POEGMA) hydrogel wound dressings containing self-assembled nanoparticles encapsulated with fusidic acid; and (2) an in situ calcium-crosslinked alginate scaffold produced using pressurized gas expanded liquids (PGX) technology impregnated with fusidic acid or tigecycline using supercritical adsorptive precipitation (sc-AP). The POEGMA hydrogel wound dressings helped supress MRSA infection and prevent systemic infection during the course of treatment, facilitating a 1-2 fold decrease in bacterial load in the wound bed. The sc-AP technology was shown to be compatible with loading clinically-relevant doses of both antimicrobial compounds, while the resulting wound dressings were effective in treating MRSA wound infections. In case of tigecycline loaded alginate scaffolds, the infection was completely cleared. In tissue engineering applications, injectable macroporous hydrogels are particularly limited by two factors: (1) their need for invasive administration, typically implantation; and (2) their generally weak mechanics. In the first case, reports of injectable hydrogels often involve toxic compounds or by-products that result in loss of cell viability. This thesis addresses this challenge by design and development of a POEGMA-based macroporous hydrogel scaffold based on a novel, non-cytotoxic pore forming emulsion based on perfluorocarbons. Use of the pore-forming emulsion significantly improved cell viability in vitro 14 days after injection and was well tolerated in vivo with minimal to no inflammatory response. In the second case, an interpenetrating “hard-soft” nanofibrous hydrogel network was fabricated by co-electrospinning POEGMA with poly(caprolactone) (PCL). The PCL phase significantly enhanced the mechanical properties of the electrospun POEGMA hydrogel scaffold making handling and manipulating the scaffolds possible, while the presence of the POEGMA phase significantly improved the biological properties of PCL scaffolds in terms of supporting significantly enhanced cell proliferation and delayed bacterial adhesion. Collectively, the advances made in this work address key challenges in the application of hydrogels in tissue engineering and wound care, with future potential to be applied to solve practical clinical challenges.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.013
GPT teacher head0.247
Teacher spread0.233 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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