RESUMEN

Although transmission electron microscopy (TEM) may be one of the most efficient techniques available for studying the morphological characteristics of nanoparticles, analyzing them quantitatively in a statistical manner is exceedingly difficult. Herein, we report a method for mass-throughput analysis of the morphologies of nanoparticles by applying a genetic algorithm to an image analysis technique. The proposed method enables the analysis of over 150,000 nanoparticles with a high precision of 99.75% and a low false discovery rate of 0.25%. Furthermore, we clustered nanoparticles with similar morphological shapes into several groups for diverse statistical analyses. We determined that at least 1,500 nanoparticles are necessary to represent the total population of nanoparticles at a 95% credible interval. In addition, the number of TEM measurements and the average number of nanoparticles in each TEM image should be considered to ensure a satisfactory representation of nanoparticles using TEM images. Moreover, the statistical distribution of polydisperse nanoparticles plays a key role in accurately estimating their optical properties. We expect this method to become a powerful tool and aid in expanding nanoparticle-related research into the statistical domain for use in big data analysis.

RESUMEN

To create printing substrates for colorimetric sensor arrays, chemically resistant membranes are prepared by coating cellulose filter paper with perfluoroalkoxy (PFA) polymer nanoparticles. A water-based fluorothermoplastic polymer dispersion was diluted with an organic solvent that causes weak aggregation of polymer nanoparticles. The resulting solution improved adhesion between the polymer and the cellulose membrane, providing a more mechanically stable substrate. These PFA polymer-coated substrates demonstrated superior chemical resistance against strong alkalines and had relatively uniform nanoporous structures that substantially improved the printability of a colorimetric sensor array. Finally, colorimetric sensor arrays printed on these substrates were evaluated for the detection of four different toxic industrial chemicals (e.g., ammonia, hydrogen sulfide, nitrogen dioxide, and sulfur dioxide) at or below their permissible exposure limits.

RESUMEN

BACKGROUND: Top priorities for tuberculosis control and elimination include a simple, low-cost screening test using sputum and a non-sputum-based test in patients that do not produce sputum. The aim of this study was to evaluate the performance of a colorimetric sensor array (CSA) test, for analysis of volatile organic compounds in urine, in the diagnosis of pulmonary TB. MATERIAL AND METHODS: Urine samples were collected from individuals suspected of having pulmonary TB in Western Kenya. Reference methods included MGIT culture and/or Xpert MTB/RIF nucleic acid amplification test on sputa. Fresh urine samples were tested with the CSA, with acid and base and without an additive. The CSA were digitally imaged, and the resulting colorimetric response patterns were used for chemometric analysis. Sensitivity, specificity, and negative (NPV) and positive predictive (PPV) values were determined for HIV-positive and HIV-negative patients. RESULTS: In HIV-negative patients, the highest accuracy was obtained in urine samples pre-treated with a base, yielding a sensitivity, specificity, PPV, and NPV of 78.3% (65/83), 69.2% (54/78), 73.0% (n/89) and 75.0% (n/72). The highest sensitivity of 79.5% was achieved using sensor data from all three test conditions at a specificity of 65.4%. In HIV-positive subjects, the sensor performance was substantially lower with sensitivity, specificity, PPV, and NPV ranging from 48.3% to 62.3%, 54.1% to 74.0%, 55.9% to 64.2%, and 60.6% to 64.9%, respectively. CONCLUSION: The CSA fingerprint of urine headspace volatiles showed moderate accuracy in diagnosing TB in HIV-negative patients, but the sensor performance dropped substantially in HIV-coinfected patients. Further development of TB-responsive CSA indicators may improve the accuracy of CSA urine assay.

Asunto(s)

Colorimetría/métodos , Mycobacterium tuberculosis , Tuberculosis/diagnóstico , Tuberculosis/orina , Compuestos Orgánicos Volátiles/orina , Estudios de Casos y Controles , Coinfección , Femenino , Infecciones por VIH , Humanos , Ensayos de Liberación de Interferón gamma , Masculino , Mycobacterium tuberculosis/aislamiento & purificación , Mycobacterium tuberculosis/metabolismo , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Esputo/microbiología , Tuberculosis/microbiología , Tuberculosis Pulmonar/diagnóstico , Tuberculosis Pulmonar/orina

RESUMEN

Here, we systematically investigated the independent, multiple, and synergic effects of three major components, namely, ascorbic acid (AA), seed, and silver ions (Ag+), on the characteristics of gold nanorods (GNRs), i.e., longitudinal localized surface plasmon resonance (LSPR) peak position, shape, size, and monodispersity. To quantitatively assess the shape and dimensions of GNRs, we used an automated transmission electron microscopy image analysis method using a MATLAB-based code developed in-house and the concept of solidity, which is the ratio between the area of a GNR and the area of its convex hull. The solidity of a straight GNR is close to 1, while it decreases for both dumbbell- and dogbone-shaped GNRs. We found that the LSPR peak position, shape, and monodispersity of the GNRs all altered simultaneously with changes in the amounts of individual components. For example, as the amount of AA increased, both the LSPR peak and solidity decreased, while the polydispersity increased. In contrast, as the amount of seeds increased, both the LSPR and solidity increased, while the monodispersity improved. More importantly, we found that the influence of each component can actually change depending on the composition of the GNR growth solution. For instance, the LSPR peak position red-shifted as the amount of AA increased when the seed content was low, whereas it blue-shifted when the seed content was high.