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Record W873129817

Synthesis and functionalisation of metal and metal oxide nanoparticles for theranostics

2013· dissertation· en· W873129817 on OpenAlexfundno aff
V. J. Mundell

Bibliographic record

VenueNottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2013
Typedissertation
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsnot available
FundersTrent UniversityNottingham Trent University
KeywordsMaterials scienceNanoparticleCoatingSuperparamagnetismChemical engineeringMetalIron oxide nanoparticlesNanotechnologyMetallurgyMagnetization
DOInot available

Abstract

fetched live from OpenAlex

Metal and metal oxide nanoparticles including calcium oxide, gold, and superparamagnetic iron oxide nanoparticles (SPIOs) were synthesised using a range of techniques including reduction, co-precipitation and spinning disc technology. SPIOs were primarily synthesised via a co-precipitation method using iron (II) chloride, iron (III) chloride and ammonia; a spinning disc reactor and gaseous ammonia were trialled successfully for scale up, producing spherical particles of 10-40 nm in diameter as analysed by TEM. Nanoparticles were coated via a novel solvent-free grinding process which was successful for drug molecules, immunogenic peptides and amino acids; the mode of binding theorised to be taking place via an electrostatic interaction between the SPIO and the carboxyl, amine or hydroxyl groups on the coating materials. Recrystallisation of the coating materials to form HCl salts, was found to increase the binding efficiency with no detrimental effects to the particles. These coated SPIOs were found to be stable in a range of buffered solutions as well as blood and cell culture media. Separation of particles by size exclusion chromatography (SEC), dialysis and magnetic separation was only effective for a small range of coatings, with high speed centrifugation at a speed of 60 000 rpm being confirmed as the only universally successful method. Imaging of citrate capped gold nanoparticles using a CT phantom revealed that gold concentrations of 3700 mg.l-1 were required for in vivo use and so this was not continued. Coated SPIOs however produced relaxation times comparable with Endorem®, as well as being non-toxic. SPIOs coated in this way were more stable than Endorem® and stayed in solution retaining their superparamagnetic properties for periods in excess of 72 days with only a negligible degree of degradation. Various peptides were synthesised using an optimised microwave assisted solid-phase peptide synthesis (SPPS) method using double the suggested coupling times, all of which were analysed for purity and structure using MALDI-TOF MS and HPLC. A cell-penetrating peptide (CPP) synthesised via this method was coated onto SPIOs and mixed into a gel for transdermal delivery using porcine skin. An NMR profile of the skin using a 0.25T NMR MOUSE® before and after application of the gel showed that after an incubation period of 2 hrs the CPP-SPIOs had penetrated the skin leading to a reduction in signal. This has potential applications for subcutaneous drug delivery and hyperthermia. Cell studies using U937 and BMDCs indicated by both ICP and fluorescence microscopy that SPIOs coated with fluorescently labelled peptides were successfully taken up into cells. SPIOs were further investigated as vectors for delivery of immunogenic peptides, namely p53(105) using female C57BL/6 mice. Results indicated that mice immunised with SPIO as a vector showed similar levels of immune response to the p53(105) following immunisation as when incomplete Freund’s adjuvant (IFA) was used. However, SPIO immunisations produced a significantly increased specific response compared to the condition using IFA. Results indicate that SPIO could be successful as a vector for cancer vaccines.

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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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
GenreMethods

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
Published2013
Admission routes1
Has abstractyes

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