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Record W3029909430

Mucoadhesive Nanocomposite Derived from Cellulose Nanocrystal and Chitosan for the Delivery of Hydrophobic Compounds

2020· dissertation· en· W3029909430 on OpenAlexfundaboutno aff
Dae Sung Kim

Bibliographic record

VenueUWSpace (University of Waterloo) · 2020
Typedissertation
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
FundersUniversity of Waterloo
KeywordsChitosanNanocompositeNanocrystalCelluloseMaterials scienceChemical engineeringNanotechnologyChemistryOrganic chemistryEngineering
DOInot available

Abstract

fetched live from OpenAlex

The sea lice are a major ectoparasite of salmon aquaculture that anchor to host fish’s mucus membrane, epidermis, and vascular system, thereby compromising the fish immunity. The surging amount of sea lice has caused enormous financial damage to the global salmon farming industries. Current sea lice treatment has relied on conventional drug delivery system that reduces drug efficacy that poses an environmental risk due to the excessive use of toxic chemical compounds. Nanomedicine and nano delivery system are rapidly developing to serve as therapeutic agents for targeting specific sites in a controlled manner, thereby enhancing the therapeutic effects at lower drug dosages. The objective of this research is to develop novel mucoadhesive nano drug delivery platforms that can encapsulate hydrophobic compounds, thereby enhancing the pharmaceutical effects of various applications including biomedical and agricultural fields. The thesis describes various mucoadhesive drug delivery platforms comprising of cellulose nanocrystals (CNC) and chitosan (CS) with different moieties. The scope of the research focuses on the development of mucoadhesive nanocomposite using green chemistry and facile synthesis methods through electrostatic gelation. To improve the functionality of the nanocomposite, colloidal behavior and mucoadhesive properties, various chemical modification techniques were employed to modify the functional groups and to decorate different moieties using nano-polysaccharide based materials. \nThrough this study, we found that the particle size CNC/CS nanoparticles was in the range of 200 nm to 2 μm, depending on the mass ratio of CNC and CS. The optimal mass ratio was 10:1 (CNC:CS w/w) yielding the smallest average particle size (~200 nm), highest zeta potentials (+40 mV), and highest drug loading efficiency. It was confirmed that polyvinylpyrrolidone (PVP) enhanced the colloidal stability of hydrophobic compounds by making the system hydrophilic. Chitosan coating enhanced colloidal stability and drug encapsulation efficiency via electrostatic repulsion. The loading and encapsulation efficiency of CNC/CS nanocomposite was 11.6 and 65.6 %, respectively. CNC/CS nanoparticle exhibited good antifungal properties against S. cerevisiae and mucoadhesive studies confirmed that nanoparticles could bind to mucus surface of zebrafish. CNC/CS modified with quaternary ammonium groups (Gch) exhibited permanent positive charge at all pH values, resulting in enhanced solubility of CS. The optimal mass ratio was 1:4 (CNC:Gch w/w), and the shape of CNC/CS based nanocomposite depended on the synthesis order, reaction time, and sonication power. To produce nanocomplexes with a homogenous structure, polymeric CS solution should be added to the CNC to coat the surface. Finally, the optimal mass ratio of CNC/CS nanoparticles modified with catechol groups (cat) was 7:1 (CNC:CS-cat w/w). After functionalizing with poly(diallyldimethylammonium chloride) (PDADMAC), the colloidal stability was enhanced yielding a particle size of ~150 nm and a zeta potential of +50 mV. Mucoadhesive studies using confocal microscopy confirmed that CNC/CS nanoparticles modified catechol groups could bind to the zebrafish mucus after 30 mins exposure, as the fluorescence signals were significantly enhanced compared to control study without modification. \nThe significant discovery of this research are: (1) a facile and reproducible method to prepare CNC/CS based nanocomposite that encapsulate large amount of hydrophobic drugs via electrostatic gelation was developed, (2) the colloidal behavior and stabilization effect of CNC/CS based nanocomposites were elucidated, (3) mucoadhesive nano-drug delivery systems by incorporating bio-inspired active compounds were produced, and (4) the potentials of mucoadhesive CNC/CS based nanocomposite for the treatment of livestock’s parasitic \ndiseases by demonstrating mucoadhesive capabilities of prepared nanoparticles on zebrafish was demonstrated. \nWith these findings, it is expected that CNC/CS based nanoparticles can serve as targeted drug delivery agents for the delivery of hydrophobic molecules, increasing colloidal stability, drug efficacy, and bioavailability. Furthermore, CNC/CS based nanocomposite will be applied for the treatment of various mucosal infections in agricultural and biomedical fields. This research establishes the foundation for the design and development of mucoadhesive delivery systems for the treatment of sea lice and other parasites found in fish farms in Canada.

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 score0.005

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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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