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In the current battle against antibiotic resistance, the resilience of Gram-negative bacteria against traditional antibiotics is due not only to their protective outer membranes but also to mechanisms like efflux pumps and enzymatic degradation of drugs, underscores the urgent need for innovative antimicrobial tactics. Herein, this study presents an innovative method involving the synthesis of three furoxan derivatives engineered to self-assemble into nitric oxide (NO) donor nanoparticles (FuNPs). These FuNPs, notably supplied together with polymyxin B (PMB), achieve markedly enhanced bactericidal efficacy against a wide spectrum of bacterial phenotypes at considerably lower NO concentrations (0.1-2.8 µg mL -1 ), which is at least ten times lower than the reported data for NO donors (≥200 µg mL -1 ). The bactericidal mechanism is elucidated using confocal, scanning, and transmission electron microscopy techniques. Neutron reflectometry confirms that FuNPs initiate membrane disruption by specifically engaging with the polysaccharides on bacterial surfaces, causing structural perturbations. Subsequently, PMB binds to lipid A on the outer membrane, enhancing permeability and resulting in a synergistic bactericidal action with FuNPs. This pioneering strategy underscores the utility of self-assembly in NO delivery as a groundbreaking paradigm to circumvent traditional antibiotic resistance barriers, marking a significant leap forward in the development of next-generation antimicrobial agents., (© 2024 The Author(s). Advanced Healthcare Materials published by Wiley‐VCH GmbH.)

In this study, we report the design of a new guanylated, cholic-acid-based monomer (GM) to combat antimicrobial resistance. The microbial activity stems from the interfacial amphiphilicity of cholic acid, while guanidine shows a strong association with phosphate, which promotes binding to membrane phospholipids. The monomer showed strong antimicrobial activity; however, surprisingly, homopolymers synthesized by photoiniferter reversible addition-fragmentation chain-transfer (RAFT) polymerization of GM completely lost their activity likely due to the conformation of the polymer. In contrast, the design of GM copolymers with poly(ethylene glycol) methyl ether methacrylate (PEGMA) or 2-hydroxyethyl methacrylate (HEMA) allowed recovery of their antimicrobial activity. Due to the existence of cholesterol in cell membranes, hemolysis was highly dependent on the content of GM incorporated. This study highlights the unique and intriguing properties of this novel amphiphilic monomer and its polymers, providing valuable insights into the development of more potent antimicrobial materials., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Secondary ion mass spectrometry (SIMS) is a powerful analytical technique for characterizing the molecular and elemental composition of surfaces. Individual mass spectra can provide information about the mean surface composition, while spatial mapping can elucidate the spatial distributions of molecular species in 2D and 3D with no prior labeling of molecular targets. The data sets produced by SIMS techniques are large and inherently complex, often containing subtle relationships between spatial and molecular features. Machine learning algorithms are well suited to exploring this complexity, making them ideal for data analysis, interpretation, and visualization of SIMS data sets. One such algorithm, the self-organizing map (SOM), is particularly well suited to clustering similar samples and reducing the dimensionality of hyperspectral data sets. Here, we present an introduction to the SOM, a concise mathematical description, and recent examples of its use in SIMS and other related mass spectrometry techniques. These examples demonstrate how SOMs may be used to interpret high volumes of individual mass spectra, imaging, or depth profiling data sets. This review will be useful for specialists in SIMS and other mass spectral techniques seeking to explore self-organizing maps for data analysis.

Wound healing is a complex biological process involving close crosstalk between various cell types. Dysregulation in any of these processes, such as in diabetic wounds, results in chronic nonhealing wounds. Fibroblasts are a critical cell type involved in the formation of granulation tissue, essential for effective wound healing. 315 different polymer surfaces are screened to identify candidates which actively drive fibroblasts toward either pro- or antiproliferative functional phenotypes. Fibroblast-instructive chemistries are identified, which are synthesized into surfactants to fabricate easy to administer microparticles for direct application to diabetic wounds. The pro-proliferative microfluidic derived particles are able to successfully promote neovascularization, granulation tissue formation, and wound closure after a single application to the wound bed. These active novel bio-instructive microparticles show great potential as a route to reducing the burden of chronic wounds., (© 2022 The Authors. Advanced Materials published by Wiley‐VCH GmbH.)

Synthetic polymers are of paramount importance in modern life - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol -1 ) using reversible addition-fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures., (© 2024 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH.)

Supervised and unsupervised machine learning algorithms are routinely applied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts., (© 2024 The Authors. Small Methods published by Wiley‐VCH GmbH.)

Multivariate statistical tools and machine learning (ML) techniques can deconvolute hyperspectral data and control the disparity between the number of samples and features in materials science. Nevertheless, the importance of generating sufficient high-quality sample replicates in training data cannot be overlooked, as it fundamentally affects the performance of ML models. Here, we present a quantitative analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of a simple microarray system of two food dyes using partial least-squares (PLS, linear) and random forest (RF, nonlinear) algorithms. This microarray was generated by a high-throughput sample preparation and analysis workflow for fast and efficient acquisition of quality and reproducible spectra via ToF-SIMS. We drew insights from the bias-variance trade-off, investigated the performances of PLS and RF regression models as a function of training data size, and inferred the amount of data needed to construct accurate and reliable regression models. In addition, we found that the spectral concatenation of positive and negative ToF-SIMS spectra improved the model performances. This study provides an empirical basis for future design of high-throughput microarrays and multicomponent systems, for the purpose of analysis with ToF-SIMS and ML.

The rapid increase and spread of Gram-negative bacteria resistant to many or all existing treatments threaten a return to the preantibiotic era. The presence of bacterial polysaccharides that impede the penetration of many antimicrobials and protect them from the innate immune system contributes to resistance and pathogenicity. No currently approved antibiotics target the polysaccharide regions of microbes. Here, describe monolaurin-based niosomes, the first lipid nanoparticles that can eliminate bacterial polysaccharides from hypervirulent Klebsiella pneumoniae, are described. Their combination with polymyxin B shows no cytotoxicity in vitro and is highly effective in combating K. pneumoniae infection in vivo. Comprehensive mechanistic studies have revealed that antimicrobial activity proceeds via a multimodal mechanism. Initially, lipid nanoparticles disrupt polysaccharides, then outer and inner membranes are destabilized and destroyed by polymyxin B, resulting in synergistic cell lysis. This novel lipidic nanoparticle system shows tremendous promise as a highly effective antimicrobial treatment targeting multidrug-resistant Gram-negative pathogens., (© 2023 The Authors. Small published by Wiley-VCH GmbH.)

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.

Extracellular vesicles (EVs) are potentially useful biomarkers for disease detection and monitoring. Development of a label-free technique for imaging and distinguishing small volumes of EVs from different cell types and cell states would be of great value. Here, we have designed a method to explore the chemical changes in EVs associated with neuroinflammation using Time-of-Flight Secondary Ion Mass spectrometry (ToF-SIMS) and machine learning (ML). Mass spectral imaging was able to identify and differentiate EVs released by microglia following lipopolysaccharide (LPS) stimulation compared to a control group. This process requires a much smaller sample size (1 µL) than other molecular analysis methods (up to 50 µL). Conspicuously, we saw a reduction in free cysteine thiols (a marker of cellular oxidative stress associated with neuroinflammation) in EVs from microglial cells treated with LPS, consistent with the reduced cellular free thiol levels measured experimentally. This validates the synergistic combination of ToF-SIMS and ML as a sensitive and valuable technique for collecting and analysing molecular data from EVs at high resolution., Competing Interests: The authors have no conflicts of interest to disclose., (© 2023 The Authors. Journal of Extracellular Biology published by Wiley Periodicals, LLC on behalf of the International Society for Extracellular Vesicles.)

The self-organizing map with relational perspective mapping (SOM-RPM) is an unsupervised machine learning method that can be used to visualize and interpret high-dimensional hyperspectral data. We have previously used SOM-RPM for the analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) hyperspectral images and three-dimensional (3D) depth profiles. This provides insightful visualization of features and trends of 3D depth profile data, using a slice-by-slice view, which can be useful for highlighting structural flaws including molecular characteristics and transport of contaminants to a buried interface and characterization of spectra. Here, we apply SOM-RPM to stitched ToF-SIMS data sets, whereby the stitched data are used to train the same model to provide a direct comparison in both 2D and 3D. We conduct an analysis of spin-coated polyaniline (PANI) films on indium tin oxide-coated glass slides that were subjected to heat treatment under atmospheric conditions to model PANI as a conformal aerospace industry coating. Replicates were shown to be precisely equivalent, both spatially and by composition, indicating a clear threshold for annealing of the film. Quantitative assessment was performed on the chemical breakdown trends accompanying annealing based on peak ratios, while spectral analysis alone shows only very subtle differences which are difficult to evaluate quantitatively. The SOM-RPM method considers data sets in their totality and highlights subtle differences between samples often simply differences in peak intensity ratios.

Infections caused by multidrug-resistant (MDR) bacteria are becoming a serious threat to public health worldwide. With an ever-reducing pipeline of last-resort drugs further complicating the current dire situation arising due to antibiotic resistance, there has never been a greater urgency to attempt to discover potential new antibiotics. The use of nanotechnology, encompassing a broad range of organic and inorganic nanomaterials, offers promising solutions. Organic nanomaterials, including lipid-, polymer-, and carbon-based nanomaterials, have inherent antibacterial activity or can act as nanocarriers in delivering antibacterial agents. Nanocarriers, owing to the protection and enhanced bioavailability of the encapsulated drugs, have the ability to enable an increased concentration of a drug to be delivered to an infected site and reduce the associated toxicity elsewhere. On the other hand, inorganic metal-based nanomaterials exhibit multivalent antibacterial mechanisms that combat MDR bacteria effectively and reduce the occurrence of bacterial resistance. These nanomaterials have great potential for the prevention and treatment of MDR bacterial infection. Recent advances in the field of nanotechnology are enabling researchers to utilize nanomaterial building blocks in intriguing ways to create multi-functional nanocomposite materials. These nanocomposite materials, formed by lipid-, polymer-, carbon-, and metal-based nanomaterial building blocks, have opened a new avenue for researchers due to the unprecedented physiochemical properties and enhanced antibacterial activities being observed when compared to their mono-constituent parts. This review covers the latest advances of nanotechnologies used in the design and development of nano- and nanocomposite materials to fight MDR bacteria with different purposes. Our aim is to discuss and summarize these recently established nanomaterials and the respective nanocomposites, their current application, and challenges for use in applications treating MDR bacteria. In addition, we discuss the prospects for antimicrobial nanomaterials and look forward to further develop these materials, emphasizing their potential for clinical translation.

Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms-that is, algorithms that do not require ground truth labels-that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

A depleted antimicrobial drug pipeline combined with an increasing prevalence of Gram-negative 'superbugs' has increased interest in nano therapies to treat antibiotic resistance. As cubosomes and polymyxins disrupt the outer membrane of Gram-negative bacteria via different mechanisms, we herein examine the antimicrobial activity of polymyxin-loaded cubosomes and explore an alternative strategy via the polytherapy treatment of pathogens with cubosomes in combination with polymyxin. The polytherapy treatment substantially increases antimicrobial activity compared to polymyxin B-loaded cubosomes or polymyxin and cubosomes alone. Confocal microscopy and neutron reflectometry suggest the superior polytherapy activity is achieved via a two-step process. Firstly, electrostatic interactions between polymyxin and lipid A initially destabilize the outer membrane. Subsequently, an influx of cubosomes results in further membrane disruption via a lipid exchange process. These findings demonstrate that nanoparticle-based polytherapy treatments may potentially serve as improved alternatives to the conventional use of drug-loaded lipid nanoparticles for the treatment of "superbugs"., (© 2022. The Author(s).)

We report the first successful combination of three distinct high-throughput techniques to deliver the accelerated design, synthesis, and property screening of a library of novel, bio-instructive, polymeric, comb-graft surfactants. These three-dimensional, surface-active materials were successfully used to control the surface properties of particles by forming a unimolecular deep layer on the surface of the particles via microfluidic processing. This strategy deliberately utilizes the surfactant to both create the stable particles and deliver a desired cell-instructive behavior. Therefore, these specifically designed, highly functional surfactants are critical to promoting a desired cell response. This library contained surfactants constructed from 20 molecularly distinct (meth)acrylic monomers, which had been pre-identified by HT screening to exhibit specific, varied, and desirable bacterial biofilm inhibitory responses. The surfactant's self-assembly properties in water were assessed by developing a novel, fully automated, HT method to determine the critical aggregation concentration. These values were used as the input data to a computational-based evaluation of the key molecular descriptors that dictated aggregation behavior. Thus, this combination of HT techniques facilitated the rapid design, generation, and evaluation of further novel, highly functional, cell-instructive surfaces by application of designed surfactants possessing complex molecular architectures.

The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox ™ multilayer standard sample was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled sample, in which the mass spectral similarity of individual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and sample degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.

We have previously reported on a novel nanoparticle formulation that was effective at killing Staphylococcus aureus in vitro . Here, we report for the first time, the antibacterial effects of a lipidic nano-carrier containing rifampicin (NanoRIF) which can be used to successfully treat Methicillin-Resistant S. aureus (MRSA) infection at a reduced antibiotic dosage compared to the free drug in a skin wound model in mice. The formulation used contains the lipid monoolein, a cationic lipid N -[1-(2,3-dioleoyloxy)propyl]- N , N , N -trimethylammonium methyl-sulfate (DOTAP) and the antibiotic. We have shown that rifampicin-loaded nanoparticles are more effective at treating infection in the skin wound model than the antibiotic alone. Cryo-TEM was used to capture for the first time, interactions of the formed nanoparticles with the cell wall of an individual bacterium. Our data strongly indicate enhanced binding of these charged nanoparticles with the negatively charged bacterial membrane. The efficacy we have now observed in vivo is of significant importance for the continued development of nanomedicine-based strategies to combat antibiotic resistant bacterial skin infections., Competing Interests: The authors declare no conflict of interest., (This journal is © The Royal Society of Chemistry.)

We present an optimization of the toroidal self-organizing map (SOM) algorithm for the accurate visualization of hyperspectral data. This represents a significant advancement on our previous work, in which we demonstrated the use of toroidal SOMs for the visualization of time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. We have previously shown that the toroidal SOM can be used, unsupervised, to produce a multicolor similarity map of the analysis area, in which pixels with similar mass spectra are assigned a similar color. Here, we use an additional algorithm, relational perspective mapping (RPM), to produce more accurate visualizations of hyperspectral data. The SOM output is used as an input for the RPM algorithm, which is a nonlinear dimensionality reduction technique designed to produce a two-dimensional map of high-dimensional data. Using the topological information provided by the SOM, RPM provides complementary distance information. The result is a color scheme that more accurately reflects the local spectral distances between pixels in the data. We exemplify SOM-RPM using ToF-SIMS imaging data from a mouse tumor tissue section. The similarity maps produced are compared with those produced by two leading hyperspectral visualization techniques in the field of mass spectrometry imaging: t-distributed stochastic neighborhood embedding (t-SNE) and uniform manifold approximation and projection (UMAP). We evaluate the performance of each technique both qualitatively and quantitatively, investigating the correlations between distances in the models and distances in the data. SOM-RPM is demonstrably highly competitive with t-SNE and UMAP, according to our evaluations. Furthermore, the use of a neural network offers distinct advantages in data characterization, which we discuss. We also show how spectra extracted from regions of interest identified by SOM-RPM can be further analyzed using linear discriminant analysis for the validation and characterization of the surface chemistry.

Staphylococcus capitis is an opportunistic pathogen often implicated in bloodstream infections in the neonatal intensive care unit (NICU). This is assisted by its ability to form biofilms on indwelling central venous catheters (CVC), which are highly resistant to antibiotics and the immune system. We sought to understand the fundamentals of biofilm formation by S. capitis in the NICU, using seventeen clinical isolates including the endemic NRCS-A clone and assessing nine commercial and two modified polystyrene surfaces. S. capitis clinical isolates from the NICU initiated biofilm formation only in response to hyperosmotic conditions, followed by a developmental progression driven by icaADBC expression to establish mature biofilms, with polysaccharide being their major extracellular polymer substance (EPS) matrix component. Physicochemical features of the biomaterial surface, and in particular the level of the element oxygen present on the surface, significantly influenced biofilm development of S. capitis . A lack of highly oxidized carbon species on the surface prevented the immobilization of S. capitis EPS and the formation of mature biofilms. This information provides guidance in regard to the preparation of hyperosmolar total parenteral nutrition and the engineering of CVC surfaces that can minimize the risk of catheter-related bloodstream infections caused by S. capitis in the NICU., (Copyright © 2020 Qu, Li, Cameron, Easton, Zhu, Zhu, Salwiczek, Muir, Thissen, Daley, Forsythe, Peleg and Lithgow.)

Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties-such as surface chemistry and properties like cell attachment or protein adsorption-in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the data set.

Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological samples. Previously, the authors demonstrated that segmentation (between 1 and 0.005 m/z mass bins) of the mass spectral axis can be used to differentiate between polymeric materials with both very similar and dissimilar molecular compositions. Here, the same approach is applied for the analysis of proteins on surfaces, focusing on the effect of binding and orientation of an antibody on the resulting ToF-SIMS spectrum. Due to the complex nature of the samples that contain combinations of only 20 amino acids differing in sequence, it is enormously challenging and prohibitively time-consuming to distinguish the minute variances presented in each dataset through manual analysis alone. Herein, the authors describe how to apply the newly developed rapid data analysis workflow to previously published ToF-SIMS data for complex biological materials, immobilized antibodies. This automated method reduced the analysis time by two orders of magnitudes while enhancing data quality and allows the removal of any user bias. The authors used mass segmentation at 0.005 m/z over a 1-300 mass range to generate 60 000 variables. In contrast to the previous manual binning approach, this method captures the entire mass range of the spectrum resulting in an information-rich dataset rather than specifically selected mass spectral peaks. This work constitutes an additional proof of concept that rapid and automated data analyses involving mass-segmented ToF-SIMS spectra can efficiently and robustly analyze a broader range of complex materials, ranging from generic polymers to complicated biological samples. This automated analysis method is also ideally positioned to provide data to train machine learning models of surface-property relationships that can greatly enhance the understanding of how the surface interacts with biology and provides more accurate and robust quantitative predictions of the biological properties of new materials.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface characterization technique capable of producing high spatial resolution hyperspectral images, in which each pixel comprises an entire mass spectrum. Such images can provide insight into the chemical composition across a surface. However, issues arise due to the size and complexity of the data produced. Data are particularly complicated for biological samples, primarily due to overlapping spectra produced by similar components. The traditional approach of selecting individual ion peaks as representative of particular components is insufficient for such complex data sets. Multivariate analysis (MVA) can help to overcome this significant hurdle. We demonstrate that Kohonen self-organizing maps (SOMs) with a toroidal topology can be used to analyze a ToF-SIMS hyperspectral imaging data set and identify spectral similarities between pixels. We present a method for color-tagging the toroidal SOM output, which reduces the entire data set to a single RGB image in which similar pixels-based on their associated mass spectra-are assigned a similar color. This method was exemplified using a ToF-SIMS image of dried large multilamellar vesicles (LMVs), loaded with the antibiotic cefditoren pivoxil (CP). We successfully identified CP-loaded and empty LMVs without the need for any prior knowledge of the sample, despite their highly similar spectra. We also identified which specific ion peaks were most important in differentiating the two LMV populations. This approach is entirely unsupervised and requires minimal experimenter input. It was developed with the aim of providing a user-friendly yet sophisticated workflow for understanding complex biological samples using ToF-SIMS images.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, providing instruments with growing capabilities and resolution. The data sets generated by these instruments are likewise increasing dramatically in size and complexity. Paradoxically, methods for efficient analysis of these large, rich data sets have not improved at the same rate. Clearly, more effective computational methods for analysis of ToF-SIMS data are becoming essential. Several research groups are customizing standard multivariate analytical tools to decrease computational demands, provide user-friendly interfaces, and simplify identification of trends and features in large ToF-SIMS data sets. We previously applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. Self-organizing maps (SOMs), a type of artificial neural network (ANN), classified the polymers based on their molecular composition and primary ion probe type more effectively than simple PCA. The effectiveness of this approach led us to question whether it would be useful in distinguishing polymers that were very similar. How sensitive is the technique to changes in polymer chemical structure and composition? To address this question, we generated ToF-SIMS ion peak signatures for seven nylon polymers with similar chemistries and used our up-binning and SOM approach to classify and cluster the polymers. The widely used linear PCA method failed to separate the samples. Supervised and unsupervised training of SOMs using positive or negative ion mass spectra resulted in effective classification and separation of the seven nylon polymers. Our SOM classification method has proven to be tolerant of minor sample irregularities, sample-to-sample variations, and inherent data limitations including spectral resolution and noise. We have demonstrated the potential of machine learning methods to analyze ToF-SIMS data more effectively than traditional methods. Such methods are critically important for future complex data analysis and provide a pipeline for rapid classification and identification of features and similarities in large data sets.

The fight against infection in an era of emerging antibiotic resistant bacteria is one of the grandest scientific challenges facing society today. Nano-carriers show great promise in improving the antibacterial activity of antibiotics as they are able to enhance their solubility, provide sustained release and reduce toxic side effects via specifically targeting infection sites. Here, we investigate the antibacterial effect of two lipidic nano-carriers that contain the poorly soluble antibiotic rifampicin in their bilayers. One nanoparticle is assembled solely from the lipid monoolein, thus is neutral at physiological pH and the other contains a mixture of monoolein and the cationic lipid N-[1-(2,3-Dioleoyloxy)propyl]-N,N,N-trimethylammonium methyl-sulfate (DOTAP), thus is positively charged. Our results show that rifampicin-loaded nanoparticles reduce the minimum inhibitory concentration against Staphylococcus aureus compared to rifampicin alone, however this reduction was most pronounced for the positively charged nanoparticles. Fluorescent microscopy revealed binding of all nanoparticles to the bacteria and enhanced binding was observed for the charged nanoparticles. This suggests that the cationic lipids promote electrostatic interactions with the negatively charged bacterial membrane. Förster resonance energy transfer demonstrated that the cationic charged nanoparticles were able to fuse with bacterial membranes whilst atomic force microscopy and transmission electron microscopy revealed structural damage to the bacterial membranes caused by the nanoparticles. Significantly, we identified a concentration window in which the nanoparticles exhibited antibacterial activity while not affecting HeLa and CHO cell viability. This ability to improve the efficacy of antibiotics without affecting their eukaryotic cytotoxicity is of significant importance for future development of nanomedicine based strategies to combat infections., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Determination of a limit of detection (LoD) for surface bound antibodies is crucial for the development and deployment of sensitive bioassays. The measurement of very low concentrations of surface bound antibodies is also important in the manufacturing of pharmaceutical products such as antibody-conjugated pharmaceuticals. Low concentrations are required to avoid an immune response from the target host. Enzyme-linked immunosorbent assay (ELISA), x-ray photoelectron spectroscopy (XPS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were used to determine the LoD for the surface bound antibody (antiepidermal growth factor receptor antibody) on silicon substrates. Antibody solution concentrations between 10 μg/ml and 1 ng/ml and a control (antibody-free buffer solution) were employed, and the detection performance of each technique was compared. For this system, the ELISA LoD was 100 ng/ml and the XPS LoD was 1 μg/ml, corresponding to an estimated surface concentration of 49  ± 7 ng/cm 2 using a 1 μg/ml solution. Due to the multivariate complexity of ToF-SIMS data, analysis was carried out using three different methods, peak ratio calculations, principal component analysis, and artificial neural network analysis. The use of multivariate analysis with this dataset offers an unbiased analytical approach based on the peaks selected from ToF-SIMS data. The results estimate a ToF-SIMS LoD between applied antibody concentrations of 10 and 100 ng/mL. For surface bound antibodies on a silicon substrate, the LoD is below an estimated surface concentration of 49 ng/cm 2 . The authors have determined the LoD for this system using ELISA, XPS, and ToF-SIMS with multivariate analyses, with ToF-SIMS offering an order of magnitude better detection over ELISA and 2 orders of magnitude better detection over XPS.

Chromium solutions have been used as wet chemical modifiers for polymer microtitre plates used in improving immunoassay performance. However, polypropylene has been excluded from the list of potentially modifiable substrates (AnteoTechnologies, 2015). Here we show that untreated polypropylene microtitre plates can indeed be modified using a [Cr(OH) 6 ] 3- complex. Compared to unmodified polypropylene, the chromium modified surfaces demonstrate an up to 4-fold improvement in both assay sensitivity and signal intensity in an antigen capture ELISA. Atomic force microscope (AFM) images indicate that the chromium complex facilitates dispersion of the antibody, reducing aggregation., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Antibody orientation at solid phase interfaces plays a critical role in the sensitive detection of biomolecules during immunoassays. Correctly oriented antibodies with solution-facing antigen binding regions have improved antigen capture as compared to their randomly oriented counterparts. Direct characterization of oriented proteins with surface analysis methods still remains a challenge however surface sensitive techniques such as Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) provide information-rich data that can be used to probe antibody orientation. Diethylene glycol dimethyl ether plasma polymers (DGpp) functionalized with chromium (DGpp+Cr) have improved immunoassay performance that is indicative of preferential antibody orientation. Herein, ToF-SIMS data from proteolytic fragments of anti-EGFR antibody bound to DGpp and DGpp+Cr are used to construct artificial neural network (ANN) and principal component analysis (PCA) models indicative of correctly oriented systems. Whole antibody samples (IgG) test against each of the models indicated preferential antibody orientation on DGpp+Cr. Cross-reference between ANN and PCA models yield 20 mass fragments associated with F(ab') 2 region representing correct orientation, and 23 mass fragments associated with the Fc region representing incorrect orientation. Mass fragments were then compared to amino acid fragments and amino acid composition in F(ab') 2 and Fc regions. A ratio of the sum of the ToF-SIMS ion intensities from the F(ab') 2 fragments to the Fc fragments demonstrated a 50% increase in intensity for IgG on DGpp+Cr as compared to DGpp. The systematic data analysis methodology employed herein offers a new approach for the investigation of antibody orientation applicable to a range of substrates., Statement of Significance: Controlled orientation of antibodies at solid phases is critical for maximizing antigen detection in biosensors and immunoassays. Surface-sensitive techniques (such as ToF-SIMS), capable of direct characterization of surface immobilized and oriented antibodies, are under-utilized in current practice. Selection of a small number of mass fragments for analysis, typically pertaining to amino acids, is commonplace in literature, leaving the majority of the information-rich spectra unanalyzed. The novelty of this work is the utilization of a comprehensive, unbiased mass fragment list and the employment of principal component analysis (PCA) and artificial neural network (ANN) models in a unique methodology to prove antibody orientation. This methodology is of significant and broad interest to the scientific community as it is applicable to a range of substrates and allows for direct, label-free characterization of surface bound proteins., (Copyright © 2017 Acta Materialia Inc. All rights reserved.)

Biophysical studies were undertaken to investigate the binding and release of short interfering ribonucleic acid (siRNA) from lyotropic liquid crystalline lipid nanoparticles (LNPs) by using a quartz crystal microbalance (QCM). These carriers are based on phytantriol (Phy) and the cationic lipid DOTAP (1,2-dioleoyloxy-3-(trimethylammonium)propane). The nonlamellar phase LNPs were tethered to the surface of the QCM chip for analysis based on biotin-neutravidin binding, which enabled the controlled deposition of siRNA-LNP complexes with different lipid/siRNA charge ratios on a QCM-D crystal sensor. The binding and release of biomolecules such as siRNA from LNPs was demonstrated to be reliably characterised by this technique. Essential physicochemical parameters of the cationic LNP/siRNA lipoplexes-such as particle size, lyotropic phase behaviour, cytotoxicity, gene silencing and uptake efficiency-were also assessed. The SAXS data show that when the pH was lowered to 5.5 the structure of the lipoplexes did not change, thus indicating that the acidic conditions of the endosome were not a significant factor in the release of siRNA from the cationic lipidic carriers., (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Orientation of surface immobilized capture proteins, such as antibodies, plays a critical role in the performance of immunoassays. The sensitivity of immunodiagnostic procedures is dependent on presentation of the antibody, with optimum performance requiring the antigen binding sites be directed toward the solution phase. This review describes the most recent methods for oriented antibody immobilization and the characterization techniques employed for investigation of the antibody state. The introduction describes the importance of oriented antibodies for maximizing biosensor capabilities. Methods for improving antibody binding are discussed, including surface modification and design (with sections on surface treatments, three-dimensional substrates, self-assembled monolayers, and molecular imprinting), covalent attachment (including targeting amine, carboxyl, thiol and carbohydrates, as well as "click" chemistries), and (bio)affinity techniques (with sections on material binding peptides, biotin-streptavidin interaction, DNA directed immobilization, Protein A and G, Fc binding peptides, aptamers, and metal affinity). Characterization techniques for investigating antibody orientation are discussed, including x-ray photoelectron spectroscopy, spectroscopic ellipsometry, dual polarization interferometry, neutron reflectometry, atomic force microscopy, and time-of-flight secondary-ion mass spectrometry. Future perspectives and recommendations are offered in conclusion.

Engineered nanoparticles with multiple complementary imaging modalities are of great benefit to the rapid treatment and diagnosis of disease in various organs. Herein, we report the formulation of cubosomes and hexosomes that carry multiple amphiphilic imaging contrast agents in their self-assembled lipid bilayers. This is the first report of the use of both near infrared fluorescent (NIRF) imaging and gadolinium lipid based magnetic resonance (MR) imaging modalities in cubosomes and hexosomes. High-throughput screening was used to rapidly optimize formulations with desirable nano-architectures and low in vitro cytotoxicity. The dual-modal imaging nanoparticles in vivo biodistribution and organ specific contrast enhancement were then studied. The NIRF in vivo imaging results indicated accumulation of both cubosomes and hexosomes in the liver and spleen of mice up to 20h post-injection. Remarkably, the biodistribution of the nanoparticle formulations was affected by the mesophase (i.e. cubic or hexagonal), a finding of significant importance for the future use of these compounds, with hexosomes showing higher accumulation in the spleen than the liver compared to cubosomes. Furthermore, in vivo MRI data of animals injected with either type of lyotropic liquid crystal nanoparticle displayed enhanced contrast in the liver and spleen., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Ensuring the optimum orientation, conformation, and density of substrate-bound antibodies is critical for the success of sandwich enzyme-linked immunosorbent assays (ELISAs). In this work, the authors utilize a diethylene glycol dimethyl ether plasma polymer (DGpp) coating, functionalized with chromium within a 96 well plate for the enhanced immobilization of a capture antibody. For an equivalent amount of bound antibody, a tenfold improvement in the ELISA signal intensity is obtained on the DGpp after incubation with chromium, indicative of improved orientation on this surface. Time-of-flight secondary-ion-mass-spectrometry (ToF-SIMS) and principal component analysis were used to probe the molecular species at the surface and showed ion fragments related to lysine, methionine, histidine, and arginine coupled to chromium indicating candidate antibody binding sites. A combined x-ray photoelectron spectroscopy and ToF-SIMS analysis provided a surface molecular characterization that demonstrates antibody binding via the chromium complex. The DGpp+Cr surface treatment holds great promise for improving the efficacy of ELISAs.

Enzyme linked immunosorbent assays (ELISAs) are employed for the detection and quantification of antigens from biological sources such as serum and cell culture media. A sandwich ELISA is dependent on the immobilization of a capture antibody, or antibody fragment, and the effective presentation of its antigen binding sites. Immobilization to common microtitre plates relies on non-specific interactions of the capture protein with a surface that may result in unfavourable orientation and conformation, compromising ELISA signal strength and performance. We have developed a wet chemical surface activation method that utilizes a chromium (III) solution to immobilize native, non-tagged, capture antibodies on commercially available microtitre plates. Antibodies captured by this method had increased antigen binding, particularly from dilute antibody solutions, relative to antibodies adsorbed directly to the plate surface. A variety of monoclonal antibodies with complementary antigen systems were used to demonstrate improvements in ELISA signal and reproducibility. The simplicity and versatility of this method should enable ELISA enhancement in assays where chemiluminescence is used as the detection method., (Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.)

Antibody denaturation at solid-liquid interfaces plays an important role in the sensitivity of protein assays such as enzyme-linked immunosorbent assays (ELISAs). Surface immobilized antibodies must maintain their native state, with their antigen binding (Fab) region intact, to capture antigens from biological samples and permit disease detection. In this work, two identical sample sets were prepared with whole antibody IgG, F(ab') 2 and Fc fragments, immobilized to either a silicon wafer or a diethylene glycol dimethyl ether plasma polymer surface. Analysis was conducted on one sample set at day 0, and the second sample set after 14 days in vacuum, with time-of-flight secondary ion mass spectrometry (ToF-SIMS) for molecular species representative of denaturation. A 1003 mass fragment peak list was compiled from ToF-SIMS data and compared to a 35 amino acid mass fragment peak list using principal component analysis. Several ToF-SIMS secondary ions, pertaining to disulfide and thiol species, were identified in the 14 day (presumably denatured) samples. A substrate and primary ion independent marker for denaturation (aging) was then produced using a ratio of mass peak intensities according to denaturation ratio: [I 61.9534 + I 62.9846 + I 122.9547 + I 84.9609 + I 120.9461 ]/[I 30.9979 + I 42.9991 + I 73.0660 + I 147.0780 ]. The ratio successfully identifies denaturation on both the silicon and plasma polymer substrates and for spectra generated with Mn + , Bi + , and Bi 3 + primary ions. We believe this ratio could be employed to as a marker of denaturation of antibodies on a plethora of substrates.

A robot-assisted high-throughput methodology was employed to produce chromium(III) complexes suitable for the surface modification of the commercially available PerkinElmer Optiplate96 well plate for use in enzyme-linked immunosorbent assays (ELISAs). The complexes were immobilized to the native functionality of the well plate and first screened using a horseradish-peroxidase-tagged (HRP) mouse antibody to quantify binding. The top "hits" were further assessed for their ability to present the antibody in a functional state using an ELISA. "Hits" from the second screen yielded four complexes capable of improving the signal intensity of the ELISA by greater than 500%. The metal/base ratio of these complexes was also investigated, and we isolated the most stable and reproducible candidate, [Cr(OH) 6 ] 3- , which was formed from chromium(III) perchlorate and pH adjusted with ethylenediamine. This chromium solution was employed in a clinically relevant setting for the detection of bovine TNFα producing up to a 200% increase in signal intensity.

Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality of large data sets and highlight key characteristics. Herein, we discuss the strengths and weaknesses of PCA and ANNs as methods for investigation and interpretation of a complex multivariate sample set. Using time-of-flight secondary ion mass spectrometry (ToF-SIMS) we acquired spectra from an antibody and its proteolysis fragments with three primary-ion sources to obtain a panel of 72 spectra and a characteristic peak list of 775 fragment ions. We describe the use of ANNs as a means to interpret the ToF-SIMS spectral data, highlight the optimal neural network design and computational parameters, and discuss the technique limitations. Further, employing Bi3(+) as the primary-ion source, ANNs can accurately classify antibody fragments from the parent antibody based on ToF-SIMS spectra.

Self-assembled lyotropic liquid crystalline lipid nanoparticles have been developed for a wide range of biomedical applications with an emerging focus for use as delivery vehicles for drugs, genes, and in vivo imaging agents. In this study, we report the generation of lipid nanoparticle libraries with information regarding mesophase and lattice parameter, which can aid the selection of formulation for a particular end-use application. In this study we elucidate the phase composition parameters that influence the internal structure of lipid nanoparticles produced from monoolein, monopalmitolein and phytantriol incorporating a variety of saturated fatty acids (FA) with different chain lengths at varying concentrations and temperatures. The material libraries were established using high throughput formulation and screening techniques, including synchrotron small-angle X-ray scattering. The results demonstrate the rich polymorphism of lipid nanoparticles with nonlamellar mesophases in the presence of saturated FAs. The inclusion of saturated FAs within the lipid nanoparticles promotes a gradual phase transition at all temperatures studied toward structures with higher negative surface curvatures (e.g., from inverse bicontinuous cubic phase to hexagonal phase and then emulsified microemulsion). The three partial phase diagrams produced are discussed in terms of the influence of FA chain length and concentration on nanoparticle internal mesophase structure and lattice parameters. The study also highlights a compositionally dependent coexistence of multiple mesophases, which may indicate the presence of multicompartment nanoparticles containing cubic/cubic and cubic/hexagonal mesophases.

Optical quality metal organic framework (MOF) thin films were integrated, for the first time, to the best of our knowledge, with structured optical fiber substrates to develop MOF-fiber sensors. The MOF-fiber structure, UiO-66 (Zr-based MOF is well known for its water stability), is a thin film that acts as an effective analyte collector. This provided a Fabry-Perot sensor in which concentrations of up to 15 mM Rhodamine-B were detected via wavelength shifts in the interference spectrum.

Lyotropic liquid crystalline nanoparticle dispersions are of interest as delivery vectors for biomedicine. Aqueous dispersions of liposomes, cubosomes, and hexosomes are commonly stabilized by nonionic amphiphilic block copolymers to prevent flocculation and phase separation. Pluronic stabilizers such as F127 are commonly used; however, there is increasing interest in using chemically reactive stabilizers for enhanced functionalization and specificity in therapeutic delivery applications. This study has explored the ability of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine conjugated with poly(ethylene glycol) (DSPE-PEGMW) (2000 Da ≤ MW ≤ 5000 Da) to engineer and stabilize phytantriol-based lyotropic liquid crystalline dispersions. The poly(ethylene glycol) (PEG) moiety provides a tunable handle to the headgroup hydrophilicity/hydrophobicity to allow access to a range of nanoarchitectures in these systems. Specifically, it was observed that increasing PEG molecular weight promotes greater interfacial curvature of the dispersions, with liposomes (Lα) present at lower PEG molecular weight (MW 2000 Da), and a propensity for cubosomes (QII(P) or QII(D) phase) at MW 3400 Da or 5000 Da. In comparison to Pluronic F127-stabilized cubosomes, those made using DSPE-PEG3400 or DSPE-PEG5000 had enlarged internal water channels. The toxicity of these cubosomes was assessed in vitro using A549 and CHO cell lines, with cubosomes prepared using DSPE-PEG5000 having reduced cytotoxicity relative to their Pluronic F127-stabilized analogues.

The use of medical imaging contrast agents may lead to improved patient prognosis by potentially enabling an earlier detection of diseases and therefore an earlier initiation of treatments. In this study, we fabricated superparamagnetic iron oxide (SPIO) nanoparticles within the inner cavity of multiwalled carbon nanotubes (MWCNTs) for the first time; thereby ensuring high mechanical stability of the nanoparticles. A simple, but effective, self-assembled coating with RAFT diblock copolymers ensured the SPIO-MWCNTs have a high dispersion stability under physiological conditions. In vivo acute tolerance testing in mice showed a high tolerance dose up to 100 mg kg(-1). Most importantly, after administration of the material a 55% increase in tumor to liver contrast ratio was observed with in vivo MRI measurements compared to the preinjection image enhancing the detection of the tumor.

Next generation drug delivery utilising nanoparticles incorporates active targeting to specific sites. In this work, we combined targeting with the inherent advantages of self-assembled lipid nanoparticles containing internal nano-structures. Epidermal growth factor receptor (EGFR)-targeting, PEGylated lipid nanoparticles using phytantriol and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-PEG-maleimide amphiphiles were created. The self-assembled lipid nanoparticles presented here have internal lyotropic liquid crystalline nano-structures, verified by synchrotron small angle X-ray scattering and cryo-transmission electron microscopy, that offer the potential of high drug loading and enhanced cell penetration. Anti-EGFR Fab' fragments were conjugated to the surface of nanoparticles via a maleimide-thiol reaction at a high conjugation efficiency and retained specificity following conjugation to the nanoparticles. The conjugated nanoparticles were demonstrated to have high affinity for an EGFR target in a ligand binding assay.

A high-throughput approach was developed in order to prepare and dry a series of protic ionic liquids (PILs) from 48 Brønsted acid-base combinations. Many combinations comprised an alkyl carboxylic acid paired with an alkyl amine. Visual screens were developed to identify which acid-base combinations formed PILs, and of those, which PILs were likely to have high surface tensions, low viscosities, and low melting points. The surface tension screen was validated through pendant drop surface tension measurements. Karl Fischer coulometric titration was used to obtain the water contents, and it was noted that there is a considerable difference in the drying rate throughout this series of PILs. It was observed that an octyl chain present on either the cation or anion was detrimental to the formation of a PIL with a low melting point, and instead increased the likelihood of a gel or solid forming. The nanostructure of the PILs was determined, using synchrotron small and wide angle X-ray scattering (SAXS/WAXS), to consist of polar and non-polar domains, with the alkyl chains on the cation and anion intercalating. The results indicate that both the alkyl chain on the cation and/or anion contribute to the correlation distance, for the intermediate range order, with the expectation that there is charge alternation of the ions in the polar region. The maximum correlation distance was observed when there was an alkyl chain present on only one ion. This correlation distance could be significantly reduced by varying the alkyl chain length present on the other ion, which was attributed to increased disorder and interdigitation of chains, and to toe-to-toe alignment of the chains. To the best of our knowledge this is the first PIL report into the effect of having an alkyl chain present on both the cation and the anion.

The purpose of this work was to synthesize and screen, for their effectiveness to act as T1-enhancing magnetic resonance imaging (MRI) contrast agents, a small library of nitroxide lipids incorporated into cubic-phase lipid nanoparticles (cubosomes). The most effective nitroxide lipid was then formulated into lower-toxicity lipid nanoparticles (hexosomes), and effective MR contrast was observed in the aorta and spleen of live rats in vivo. This new class of lower-toxicity lipid nanoparticles allowed for higher relaxivities on the order of those of clinically used gadolinium complexes. The new hexosome formulation presented herein was significantly lower in toxicity and higher in relaxivity than cubosome formulations previously reported by us.

Redox-cleavable mikto-arm star polymers are prepared by an "arm-first" approach involving copolymerization of a dimethacrylate mediated by a mixture of macroRAFT agents. Thus, RAFT copolymerization of the monomers BMA, DMAEMA, and OEGMA, with the disulfide dimethacrylate cross-linker (DSDMA), bis(2-methacryloyl)oxyethyl disulfide, mediated by a 1:1:1 mixture of three macroRAFT agents with markedly different properties [hydrophilic, poly[oligo(ethylene glycol) methacrylate]-P(OEGMA)8-9 ; cationizable, poly[2-(dimethylamino)ethyl methacrylate]-P(DMAEMA); hydrophobic, poly(n-butyl methacrylate)-P(BMA)] provides low dispersity mikto-arm star polymers. Good control (Đ < 1.3) is observed for the target P(DMAEMA)/P(OEGMA)/P(BMA) (3:3:1) mikto-arm star, a double hydrophilic P(DMAEMA)/P(OEGMA) (3:3) mikto-arm star and a hydrophobic P(BMA) homo-arm star. However, Đ for the target mikto-arm stars increases with an increase in either the ratio [DSDMA]:[total macroRAFT] or the fraction of hydrophobic P(BMA) macroRAFT agent. The quaternized mikto-arm star in dilute aqueous solution shows a monomodal particle size distribution and an average size of ≈145 nm., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

An efficient MRI T2-weighted contrast agent incorporating a potential liver targeting functionality was synthesized via the combination of superparamagnetic iron oxide (SPIO) nanoparticles with multiwalled carbon nanotubes (MWCNTs). Poly(diallyldimethylammonium chloride) (PDDA) was coated on the surface of acid treated MWCNTs via electrostatic interactions and SPIO nanoparticles modified with a potential targeting agent, lactose-glycine adduct (Lac-Gly), were subsequently immobilized on the surface of the PDDA-MWCNTs. A narrow magnetic hysteresis loop indicated that the product displayed superparamagnetism at room temperature which was further confirmed by ZFC (zero field cooling)/FC (field cooling) curves measured by SQUID. The multifunctional MWCNT-based magnetic nanocomposites showed low cytotoxicity in vitro to HEK293 and Huh7 cell lines. Enhanced T2 relaxivities were observed for the hybrid material (186 mM(-1) s(-1)) in comparison with the pure magnetic nanoparticles (92 mM(-1) s(-1)) due to the capacity of the MWCNTs to "carry" more nanoparticles as clusters. More importantly, after administration of the composite material to an in vivo liver cancer model in mice, a significant increase in tumor to liver contrast ratio (277%) was observed in T2 weighted magnetic resonance images., (Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.)

We show for the first time the possibility of using networks of amyloid fibrils, adsorbed to solid supports and with plasma polymer coatings, for the fabrication of chemically homogeneous surfaces with well-defined nanoscale surface features reminiscent of the topography of the extracellular matrix. The robust nature of the fibrils allows them to withstand the plasma polymer deposition conditions used with no obvious deleterious effect, thus enabling the underlying fibril topography to be replicated at the polymer surface. This effect was seen despite the polymer coating thickness being an order of magnitude greater than the fibril network. The in vitro culture of fibroblast cells on these surfaces resulted in increased attachment and spreading compared to flat plasma polymer films with the same chemical composition. The demonstrated technique allows for the rapid and reproducible fabrication of substrates with nanoscale fibrous topography that we believe will have applications in the development of new biomaterials allowing, for example, the investigation of the effect of extracellular matrix mimicking nanoscale morphology on cellular phenotype.

The mesoporous structure of sol-gel prepared gadolinium and europium doped silicate nanoparticles has been found to be highly dependent on the formulated composition, with synthesised samples displaying both disordered and hexagonally ordered mesoporous packing symmetry. The degree of pore ordering within the nanoparticles has a strong correlation with the total lanthanide (Gd(3+) and Eu(3+) ) concentration. The gadolinosilicates are excellent magnetic resonance imaging (MRI) longitudinal (T1 ) agents. The longitudinal relaxivity (r1 ) and transverse (r2 ) relaxivity, a measure of MRI contrast agent efficiency, were up to four times higher than the clinically employed Omniscan (gadodiamide); with r1 up to 20.6 s(-1) mM(-1) and r2 of 66.2 s(-1) mM(-1) compared to 5.53 and 4.64 s(-1) mM(-1) , respectively, for Omniscan. In addition, the europium content of all the samples studied is below the self-quenching limit, which results in a strong luminescence response from the nanoparticles on excitation at 250 nm. The Eu-Gd silicate nanoparticles act as bimodal imaging agents for MRI and luminescence. These mesoporous nanoparticles also have the potential to serve as encapsulation and controlled release matrices for pharmaceuticals. They are therefore a promising multimodal theranostic platform., (Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)