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Record W7030246495

Multifunctional Liquid-Infused Surface Coatings to Prevent Implant-Associated-Infections

2023· dissertation· en· W7030246495 on OpenAlexfundno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMcMaster University
KeywordsBiofilmImplantDebridement (dental)Medical deviceAntibioticsLayer (electronics)Extracellular matrix
DOInot available

Abstract

fetched live from OpenAlex

Medical implants constitute an essential advancement in modern medicine, often restoring or replacing functionality to failed organs. Whether a medical implant is temporary or permanent, medical implants carry the risk of implant failure due to an infection. Implant-associated infections (IAI) are challenging to treat and often result in increased medical costs, prolonged hospital stays, implant failure, and, in some instances, severe infections that can lead to amputations, sepsis, or mortality. Eradicating an IAI can be challenging since bacteria can form biofilms on the implant’s surface. The biofilms comprise an extracellular matrix protecting the bacterial cells against systemic antibiotics and the host’s immune system. Treating an IAI usually entails a broad range of antibiotic treatment and surgical procedures for tissue debridement or implant replacement. For the reasons stated above, scientists and engineers continue to develop technologies to protect the surface of medical implants against infections. Amongst the new technologies, Liquid-Infused Surfaces (LIS) are renowned for their repellent and anti-fouling properties created by tethering a stable liquid layer onto the surface. However, many challenges remain to adopt this technology for implantable devices. For instance, the high repellent properties can hinder implant-tissue interaction and discourage proper integration with the body. Furthermore, the stable liquid layer is contingent on the surface properties of the coated material. In other words, the long-term stability of these coatings may be compromised if the surface chemistry is covered by biological processes such as biofilm formation from adherent bacteria. This thesis aims to expand on the applications of LIS coatings and enhance their properties for implantable materials. This thesis reviews different types of antibiotic surface coatings and further examines LIS technologies as a viable antibacterial coating for medical implants. Then, three novel multifunctional LIS coatings are presented. The first developed coating enhanced the antibacterial properties of the coating by adding bactericidal agents within the LIS coating. The developed antibiotic liquid-infused coating not only repelled bacteria but also lysed bacteria upon contact. The second coating was designed to promote tissue integration. This multifunctional coating promoted cell deposition and proliferation while remaining repellent toward bacteria, while the conventional LIS coating displayed poor cell availability. Lastly, a collagen-bacteriophage conjugated liquid-infused coating was developed to promote tissue integration while having a two-tier layer of antibacterial protection. This coating was tested in a mouse sepsis model and prevented mortality of all mice, with other groups as high as 90% mortality. These coatings constitute essential steppingstones to bring LIS technology to medical implants.

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 score0.004

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 integrity0.0010.001
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 head0.020
GPT teacher head0.205
Teacher spread0.185 · 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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