Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network

Background and Purpose: The risk of recurrent stroke following a transient ischemic attack (TIA) or minor stroke is high, despite of a significant reduction in the past decade. In this study, we investigated the feasibility of using artificial neural network (ANN) for risk stratification of TIA or minor stroke patients.Methods: Consecutive patients with acute TIA or minor ischemic stroke presenting at a tertiary hospital during a 2-year period were recruited. We collected demographics, clinical and imaging data at baseline. The primary outcome was recurrent ischemic stroke within 1 year. We developed ANN models to predict the primary outcome. We randomly down-sampled patients without a primary outcome to 1:1 match with those with a primary outcome to mitigate data imbalance. We used a 5-fold cross-validation approach to train and test the ANN models to avoid overfitting. We employed 19 independent variables at baseline as the input neurons in the ANN models, using a learning algorithm based on backpropagation to minimize the loss function. We obtained the sensitivity, specificity, accuracy and the c statistic of each ANN model from the 5 rounds of cross-validation and compared that of support vector machine (SVM) and Naïve Bayes classifier in risk stratification of the patients.Results: A total of 451 acute TIA or minor stroke patients were enrolled. Forty (8.9%) patients had a recurrent ischemic stroke within 1 year. Another 40 patients were randomly selected from those with no recurrent stroke, so that data from 80 patients in total were used for 5 rounds of training and testing of ANN models. The median sensitivity, specificity, accuracy and c statistic of the ANN models to predict recurrent stroke at 1 year was 75%, 75%, 75%, and 0.77, respectively. ANN model outperformed SVM and Naïve Bayes classifier in our dataset for predicting relapse after TIA or minor stroke.Conclusion: This pilot study indicated that ANN may yield a novel and effective method in risk stratification of TIA and minor stroke. Further studies are warranted for verification and improvement of the current ANN model

Aerosol Microdroplets Exhibit a Stable pH Gradient

Suspended aqueous aerosol droplets (\u3c50 μm) are microreactors for many important atmospheric reactions. In droplets and other aquatic environments, pH is arguably the key parameter dictating chemical and biological processes. The nature of the droplet air/ water interface has the potential to significantly alter droplet pH relative to bulk water. Historically, it has been challenging to measure the pH of individual droplets because of their inaccessibility to conventional pH probes. In this study, we scanned droplets containing 4-mercaptobenzoic acid–functionalized gold nanoparticle pH nanoprobes by 2D and 3D laser confocal Raman microscopy. Using surface-enhanced Raman scattering, we acquired the pH distribution inside approximately 20-μm-diameter phosphate-buffered aerosol droplets and found that the pH in the core of a droplet is higher than that of bulk solution by up to 3.6 pH units. This finding suggests the accumulation of protons at the air/water interface and is consistent with recent thermodynamic model results. The existence of this pH shift was corroborated by the observation that a catalytic reaction that occurs only under basic conditions (i.e., dimerization of 4-aminothiophenol to produce dimercaptoazobenzene) occurs within the high pH core of a droplet, but not in bulk solution. Our nanoparticle probe enables pH quantification through the cross-section of an aerosol droplet, revealing a spatial gradient that has implications for acid-base–catalyzed atmospheric chemistry

MGITC Facilitated Formation of AuNP Multimers

Malachite green isothiocyanate (MGITC) is frequently used as a surface bound Raman reporter for metal nanoparticle-enabled surface enhanced Raman scattering (SERS). To date, however, no study has focused on the application of MGITC for the formation of stable “hot-spot” aggregates for Raman imaging applications. Herein we report a method to produce a series of suspensions of MGITC functionalized gold nanoparticles (MGITC-AuNPs) that at one extreme consist primarily of monomers and at the other extreme as mixtures of multimers and monomers. Monomer and multimer morphologies were characterized by scanning electron microscopy and atomic force microscopy using a reliable spin-coating deposition sampling method. The multimers generally include 2, 3, or 4 individual AuNPs with an average number of 3 ± 1. The number of multimers produced in a given suspension was found to be dependent on the volume and concentration of MGITC initially applied. The surface binding of MGITC to both monomeric and multimeric MGITC-AuNPs was investigated by Raman and SERS, and the degree of aggregation in the multimer suspension was evaluated based upon the measured variation of the MGITC SERS intensity of the AuNPs. Using an estimated extinction coefficient of 1.22 ± 0.41 × 10¹¹ M^–1 cm^–1 at ≈850 nm for the localized surface plasmon resonance (LSPR) band of the MGITC-AuNP multimers, the multimer concentrations were calculated by Beer’s Law

Nanoclustered Gold Honeycombs for Surface-Enhanced Raman Scattering

A honeycomb-shaped gold substrate was developed for surface-enhanced Raman imaging (SERI). The honeycombs are composed of clusters of 50–70 nm gold nanoparticles and exhibit high Raman enhancement efficiency. An average surface enhancement factor (ASEF) of 1.7 × 10⁶ was estimated for a monolayer of l-cysteine molecules adsorbed to gold via a thiol linkage. The presence of a linear relationship in the low concentration region was observed in SERI detection of malachite green isothiocyanate (MGITC). These results together with the high reproducibility and simple and cost-effective fabrication of this substrate suggest that it has utility for applications of surface-enhanced Raman scattering (SERS) in quantitative diagnoses and analyte detection

Controlled Evaluation of the Impacts of Surface Coatings on Silver Nanoparticle Dissolution Rates

Silver nanoparticles (AgNPs) are increasingly being incorporated into a range of consumer products and as such there is significant potential for the environmental release of either the AgNPs themselves or Ag⁺ ions. When AgNPs are exposed to environmental systems, the engineered surface coating can potentially be displaced or covered by naturally abundant macromolecules. These capping agents, either engineered or incidental, potentially block reactants from surface sites and can alter nanoparticle transformation rates. We studied how surface functionalization affects the dissolution of uniform arrays of AgNPs fabricated by nanosphere lithography (NSL). Bovine serum albumin (BSA) and two molecular weights of thiolated polyethylene glycol (PEG; 1000 and 5000 Da) were tested as model capping agents. Dissolution experiments were conducted in air-saturated phosphate buffer containing 550 mM NaCl. Tapping-mode atomic force microscopy (AFM) was used to measure changes in AgNP height over time. The measured dissolution rate for unfunctionalized AgNPs was 1.69 ± 0.23 nm/d, while the dissolution rates for BSA, PEG1000, and PEG5000 functionalized samples were 0.39 ± 0.05, 0.20 ± 0.10, and 0.14 ± 0.07 nm/d, respectively. PEG provides a steric barrier restricting mass transfer of reactants to sites on the AgNP surface and thus diminishes the dissolution rate. The effects of BSA, however, are more complicated with BSA initially enhancing dissolution, but providing protection against dissolution over extended time

Differentiation of Microcystin, Nodularin, and Their Component Amino Acids by Drop-Coating Deposition Raman Spectroscopy

Raman spectra of microcystin-LR (MC-LR), MC-RR, MC-LA, MC-LF, MC-LY, MC-LW, MC-YR, and nodularin collected by drop-coating deposition Raman (DCDR) spectroscopy are sufficiently unique for variant identification. Amino acid spectra of l-phenylalanine, l-leucine, l-alanine, d-alanine, l-glutamic acid, l-arginine, l-tryptophan, l-tyrosine, and <i>N</i>-methyl-d-aspartic acid were collected in crystalline, DCDR, and aqueous forms to aid in cyanotoxin Raman peak assignments. Both peak ratio analysis and principal component analysis (PCA) properly classified 72 DCDR spectra belonging to the eight toxins. Loading plots for the first three principal components (PCs) most heavily weighted the peaks highlighted in the peak ratio analysis, specifically the 760 cm^–1 tryptophan peak, 853 cm^–1 tyrosine peak, and 1006 cm^–1 phenylalanine peak. Peak ratio analyses may be preferred under some circumstances because of the ease and speed with which the ratios can be computed, even by untrained lab technicians. A set of rules was created to mathematically classify toxins using the peak ratios. DCDR methods hold great potential for future application in routine monitoring because portable and hand-held Raman spectrometers are commercially available, DCDR spectra can be collected in seconds for biomolecule mixtures as well as samples containing impurities, and the method requires far fewer consumables than conventional cyanotoxin detection methods