Self-Organizing Maps for Secondary Ion Mass Spectrometry.

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.

Enhanced Nitric Oxide Delivery Through Self-Assembling Nanoparticles for Eradicating Gram-Negative Bacteria.

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.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.

Microglial activation induces nitric oxide signalling and alters protein S-nitrosylation patterns in extracellular vesicles.

Neuroinflammation is an underlying feature of neurodegenerative conditions, often appearing early in the aetiology of a disease. Microglial activation, a prominent initiator of neuroinflammation, can be induced through lipopolysaccharide (LPS) treatment resulting in expression of the inducible form of nitric oxide synthase (iNOS), which produces nitric oxide (NO). NO post-translationally modifies cysteine thiols through S-nitrosylation, which can alter function of the target protein. Furthermore, packaging of these NO-modified proteins into extracellular vesicles (EVs) allows for the exertion of NO signalling in distant locations, resulting in further propagation of the neuroinflammatory phenotype. Despite this, the NO-modified proteome of activated microglial EVs has not been investigated. This study aimed to identify the protein post-translational modifications NO signalling induces in neuroinflammation. EVs isolated from LPS-treated microglia underwent mass spectral surface imaging using time of flight-secondary ion mass spectrometry (ToF-SIMS), in addition to iodolabelling and comparative proteomic analysis to identify post-translation S-nitrosylation modifications. ToF-SIMS imaging successfully identified cysteine thiol side chains modified through NO signalling in the LPS treated microglial-derived EV proteins. In addition, the iodolabelling proteomic analysis revealed that the EVs from LPS-treated microglia carried S-nitrosylated proteins indicative of neuroinflammation. These included known NO-modified proteins and those associated with LPS-induced microglial activation that may play an essential role in neuroinflammatory communication. Together, these results show activated microglia can exert broad NO signalling changes through the selective packaging of EVs during neuroinflammation.

Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning.

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.

Controllable hierarchical self-assembly: systematic study forming metallosupramolecular frameworks on the basis of helical beta-oligoamides.

Self-assembly is a key guiding principle for the design of complex nanostructures. Substituted beta oligoamides offer versatile building blocks that can have inherent folding characteristics, offering geometrically defined functionalities that can specifically bind and assemble with predefined morphological characteristics. In this work hierarchical self-assembly is implemented based on metal coordinating helical beta-oligoamides crosslinked with transition metals selected for their favourable coordination geometries, Fe2+, Cu2+, Ni2+, Co2+, Zn2+, and two metalates, MoO42-, and WO42-. The oligoamide Ac-ß3Aß3Vß3S-αHαHαH-ß3Aß3Vß3A (3H) was designed to allow crosslinking via three distinct faces of the helical unit, with a possibility of forming three dimensional framework structures. Atomic force microscopy (AFM) confirmed the formation of specific morphologies that differ characteristically with each metal. X-Ray photoelectron spectroscopy (XPS) results reveal that the metal centres can be reduced in the final structures, confirming strong chemical interaction. Time of flight secondary ion mass spectrometry (ToF-SIMS) confirmed the spatial distribution of metals within the self-assembled networks, also revealing molecular fragments that confirm coordination to histidine and carboxyl moieties. The metalates MoO42- and WO42- were also able to induce the formation of specific superstructure morphologies. It was observed that assembly with either of nickel, copper, and molybdate form thin films, while cobalt, zinc, and tungstate produced specific three dimensional networks of oligoamides. Iron was found to form both a thin film and a complex hierarchical assembly with the 3H simultaneously. The design of the 3H substituted beta oligoamide to readily form metallosupramolecular frameworks was demonstrated with a range of metals and metalates with a degree of control over layer thicknesses as a function of the metal/metalate. The results validate and broaden the metallosupramolecular framework concept and establish a platform technology for the design of functional thin layer materials.

Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data.

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.

High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning.

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.

Large Area Ultrathin InN and Tin Doped InN Nanosheets Featuring 2D Electron Gases.

Indium nitride (InN) has been of significant interest for creating and studying two-dimensional electron gases (2DEG). Herein we demonstrate the formation of 2DEGs in ultrathin doped and undoped 2D InN nanosheets featuring high carrier mobilities at room temperature. The synthesis is carried out via a two-step liquid metal-based printing method followed by a microwave plasma-enhanced nitridation reaction. Ultrathin InN nanosheets with a thickness of ∼2 ± 0.2 nm were isolated over large areas with lateral dimensions exceeding centimeter scale. Room temperature Hall effect measurements reveal carrier mobilities of ∼216 and ∼148 cm2 V-1 s-1 for undoped and doped InN, respectively. Further analysis suggests the presence of defined quantized states in these ultrathin nitride nanosheets that can be attributed to a 2D electron gas forming due to strong out-of-plane confinement. Overall, the combination of electronic and plasmonic features in undoped and doped ultrathin 2D InN holds promise for creating advanced optoelectronic devices and functional 2D heterostructures.

Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix.

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.

Endotracheal tubes and fluid aspiration: an in vitro evaluation of new cuff technologies.

BACKGROUND: Aspiration of subglottic secretions past the endotracheal tube (ETT) cuff is a prerequisite for developing ventilator-associated pneumonia (VAP). Subglottic secretion drainage (SSD) ETTs reduce aspiration of subglottic secretions and have demonstrated lower VAP rates. We compared the performance of seven SSD ETTs against a non-SSD ETT in preventing aspiration below inflated cuffs. METHODS: ETTs were positioned vertically in 2 cm diameter cylinders. Four ml of a standard microbial suspension was added above inflated cuffs. After 1 h, aspiration was measured and ETTs demonstrating no leakage were subjected to rotational movement and evaluation over 24 h. Collected aspirated fluid was used to inoculate agar media and incubated aerobically at 37 °C for 24 h. The aspiration rate, volume and number of microorganisms that leaked past the cuff was measured. Experiments were repeated (×10) for each type of ETT, with new ETTs used for each repeat. Best performing ETTs were then tested in five different cylinder diameters (1.6, 1.8, 2.0, 2.2 and 2.4 cm). Experiments were repeated as above using sterile water. Volume and time taken for aspiration past the cuff was measured. Experiments were repeated (×10) for each type of ETT. Results were analysed using non-parametric tests for repeated measures. RESULTS: The PneuX ETT prevented aspiration past the cuff in all experiments. All other ETTs allowed aspiration, with considerable variability in performance. The PneuX ETT was statistically superior in reducing aspiration compared to the SealGuard (p < 0.009), KimVent (p < 0.002), TaperGuard (p < 0.004), Lanz (p < 0.001), ISIS (p < 0.001), SACETT (p < 0.001) and Soft Seal (p < 0.001) ETTs. Of the 4 ETTs tested in differing cylinder sizes, the PneuX significantly reduced aspiration across the range of diameters compared to the SealGuard (p < 0.0001), TaperGuard (p < 0.0001) and KimVent (p < 0.0001) ETTs. CONCLUSIONS: ETTs showed substantial variation in fluid aspiration, relating to cuff material and design. Variability in performance was likely due to the random manner in which involutional folds form in the inflated ETT cuff. The PneuX ETT was the only ETT able to consistently prevent aspiration past the cuff in all experiments.

Convenient syntheses of cyanuric chloride-derived NHC ligands, their Ag(I) and Au(I) complexes and antimicrobial activity.

Convenient syntheses of mono- and bis-imidazolium 1,3,5-triazine derivatives bearing piperidine and morpholine substituents are reported. In situ deprotonation of the mono-imidazolium salts and reaction with Ag2O or Au(tht)Cl (tht = tetrahydrothiophene) precursors affords the corresponding Ag(NHC)Cl and Au(NHC)Cl carbene complexes. In the presence of Ag(I) or Au(I) salts the bis-imidazolium pincers eliminate the imidazolium group to afford -OMe or -NMe2 substituted triazines depending on the solvent used. In solution, the Ag(I) and Au(I) complexes show a barrier to rotation about the Ctriazine-Namine bonds, with calculated ΔG(≠) barriers in the region of 70 kJ mol(-1). Single crystal X-ray structures of several of the proligands and their corresponding Ag(I) and Au(I) complexes were obtained. These universally reveal an extended, rigidly planar π-conjugated network between the triazine core, imidazolium/imidazolylidene substituents and exocyclic amine functions, to which the origin of the rotational barrier observed in solution is attributed. Only very weak Ntriazine-metal interactions are observed in the solid state, as indicated by small deviations of the CNHC-Ag-Cl bond angles from 180° and also supported by DFT calculations on the Ag(NHC)Cl complex (NHC = 4,6-dipiperidinyl-2-methylimidazolylidene triazine). Preliminary antimicrobial susceptibility studies against five microorganisms (methicillin resistant Staphylococcus aureus NCTC 13277, S. aureus NCTC 6571, Pseudomonas aeruginosa NCTC 10662, Proteus mirabilis NCTC 11938 and Candida albicans ATCC 90028) show that the above triazine-based Ag-NHC complexes are active antimicrobial and antifungal agents.

Highly purified lipopolysaccharides from Burkholderia cepacia complex clinical isolates induce inflammatory cytokine responses via TLR4-mediated MAPK signalling pathways and activation of NFkappaB.

In cystic fibrosis (CF), bacteria of the Burkholderia cepacia complex (Bcc) can induce a fulminant inflammation with pneumonitis and sepsis. Lipopolysaccharide (LPS) may be an important virulence factor associated with this decline but little is known about the molecular pathogenesis of Bcc LPS. In this study we have investigated the inflammatory response to highly purified LPS from different Bcc clinical isolates and the cellular signalling pathways employed. The inflammatory response (TNFalpha, IL-6) was measured in human MonoMac 6 monocytes and inhibition experiments were used to investigate the Toll-like receptors and associated adaptor molecules and pathways utilized. LPS from all clinical Bcc isolates induced significant pro-inflammatory cytokines and utilized TLR4 and CD14 to mediate activation of mitogen-activated protein kinase pathways, IkappaB-alpha degradation and NFkappaB activation. However, LPS from different clinical isolates of the same clonal strain of Burkholderia cenocepacia were found to induce a varied inflammatory response. LPS from clinical isolates of Burkholderia multivorans was found to activate the inflammatory response via MyD88-independent pathways. This study suggests that LPS alone from clinical isolates of Bcc is an important virulence factor in CF and utilizes TLR4-mediated signalling pathways to induce a significant inflammatory response.