Recognition of breast cancer subtypes using FTIR hyperspectral data.

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods.

Breast cancer (BC) molecular subtypes diagnosis involves improving clinical uptake by Fourier transform infrared (FTIR) spectroscopic imaging, which is a non-destructive and powerful technique, enabling label free extraction of biochemical information towards prognostic stratification and evaluation of cell functionality. However, methods of measurements of samples demand a long time to achieve high quality images, making its clinical use impractical because of the data acquisition speed, poor signal to noise ratio, and deficiency of optimized computational framework procedures. To address those challenges, machine learning (ML) tools can facilitate obtaining an accurate classification of BC subtypes with high actionability and accuracy. Here, we propose a ML-algorithm-based method to distinguish computationally BC cell lines. The method is developed by coupling the K-neighbors classifier (KNN) with neighborhood components analysis (NCA), and hence, the NCA-KNN method enables to identify BC subtypes without increasing model size as well as adding additional computational parameters. By incorporating FTIR imaging data, we show that classification accuracy, specificity, and sensitivity improve, respectively, 97.5%, 96.3%, and 98.2%, even at very low co-added scans and short acquisition times. Moreover, a clear distinctive accuracy (up to 9 %) difference of our proposed method (NCA-KNN) was obtained in comparison with the second best supervised support vector machine model. Our results suggest a key diagnostic NCA-KNN method for BC subtypes classification that may translate to advancement of its consolidation in subtype-associated therapeutics.

Selecting High-Performance Gold Nanorods for Photothermal Conversion.

In this work, we establish a new paradigm on identifying optimal arbitrarily shaped metallic nanostructures for photothermal applications. Crucial thermo-optical parameters that rule plasmonic heating are appraised, exploring a nanoparticle size-dependence approach. Our results indicate two distinct figures of merit for the optimization of metallic nanoheaters, under both non-cumulative femtosecond and continuum laser excitation. As a case study, gold nanorods are evaluated for infrared photothermal conversion in water, and the influence of the particle length and diameter are depicted. For non-cumulative femtosecond pulses, efficient photothermal conversion is observed for gold nanorods of small volumes. For continuous wave (CW) excitation at 800 nm and 1064 nm, the optimal gold nanorod dimensions (in water) are, respectively, 90 × 25nm and 150 × 30 nm. Figure of Merit (FoM) variations up to 700% were found considering structures with the same peak wavelength. The effect of collective heating is also appraised. The designing of high-performance plasmonic nanoparticles, based on quantifying FoM, allows a rational use of nanoheaters for localized photothermal applications.

Tailoring the Scattering Response of Optical Nanocircuits Using Modular Assembly.

Owing to the localized plasmon resonance of an ensemble of interacting plasmonic nanoparticles (NPs), there has been a tremendous drive to conceptualize complex optical nanocircuits with versatile functionalities. In comparison to modern research, there is still not a sufficient level of sophistication to treat the nanostructures as lumped circuits that can be adjusted into complex systems on the basis of a metatronic touchstone. Here, we present the design, assembly, and characterization of single relatively complex photonic nanocircuits by accurately positioning several metallic and dielectric nanoparticles acting as modular lumped elements. In this research, Au NPs along with silica NPs were used to compare the proficiency and precision of our lumped circuit model analytically. On increasing the size of an individual Au NP, the spectral peak resonance not only modifies but also causes more scattering efficiency which increases the fringe capacitance linearly and decreases the nanoinductance of lumped circuit element. The NPs-based assembly induced the required spectral resonance ascribed by simple circuit methods and are depicted to be actively reconfigurable by tuning the direction or polarization of input signals. Our work demonstrates a vital step toward developing the modern modular designing tools of complex electronic circuits into nanophotonic-related applications.

Optimizing and Quantifying Gold Nanospheres Based on LSPR Label-Free Biosensor for Dengue Diagnosis.

The localized surface plasmon resonance (LSPR) due to light-particle interaction and its dependence on the surrounding medium have been widely manipulated for sensing applications. The sensing efficiency is governed by the refractive index-based sensitivity (ηRIS) and the full width half maximum (FWHM) of the LSPR spectra. Thereby, a sensor with high precision must possess both requisites: an effective ηRIS and a narrow FWHM of plasmon spectrum. Moreover, complex nanostructures are used for molecular sensing applications due to their good ηRIS values but without considering the wide-band nature of the LSPR spectrum, which decreases the detection limit of the plasmonic sensor. In this article, a novel, facile and label-free solution-based LSPR immunosensor was elaborated based upon LSPR features such as extinction spectrum and localized field enhancement. We used a 3D full-wave field analysis to evaluate the optical properties and to optimize the appropriate size of spherical-shaped gold nanoparticles (Au NPs). We found a change in Au NPs' radius from 5 nm to 50 nm, and an increase in spectral resonance peak depicted as a red-shift from 520 nm to 552 nm. Using this fact, important parameters that can be attributed to the LSPR sensor performance, namely the molecular sensitivity, FWHM, ηRIS, and figure of merit (FoM), were evaluated. Moreover, computational simulations were used to assess the optimized size (radius = 30 nm) of Au NPs with high FoM (2.3) and sharp FWHM (44 nm). On the evaluation of the platform as a label-free molecular sensor, Campbell's model was performed, indicating an effective peak shift in the adsorption of the dielectric layer around the Au NP surface. For practical realization, we present an LSPR sensor platform for the identification of dengue NS1 antigens. The results present the system's ability to identify dengue NS1 antigen concentrations with the limit of quantification measured to be 0.07 µg/mL (1.50 nM), evidence that the optimization approach used for the solution-based LSPR sensor provides a new paradigm for engineering immunosensor platforms.

Identifying high performance gold nanoshells for singlet oxygen generation enhancement.

Metallic nanostructures can improve the production of singlet oxygen (1O2) of a photosensitizer during photodynamic therapy (PDT) . Engineering a high performance nanoparticle is mandatory for an appropriate use of plasmonic nanostructures in PDT. Metal enhanced singlet oxygen generation requires the use of nanoparticles with high scattering efficiency, capable of inducing a significant electric field enhancement and with plasmon peak overlapping the photosensitizer absorption spectrum. Herein, we report the optimization of nanoshells structure (silica core radius and gold shell thickness) to increase the singlet oxygen production by Methylene Blue photosensitizer. A 3D Full-wave field analysis was used to evaluate the plasmonic spectrum, scattering efficiency and localized field intensity of Au nanoshells as a function of their dimensions. The 40/20 core radius/shell thickness optimized gold nanoshell showed 75% scattering efficiency and field enhancement up to 35 times. Metal-enhanced singlet oxygen generation was observed and quantified for Methylene Blue water solution with gold nanoshell particles. Moreover, the influence of the irradiation time and the metallic nanostructures concentration on metal enhanced singlet oxygen generation were also appraised. The experimental results showed that the use of gold nanoshell improved 320% the 1O2 production in a MB solution. The approach used to select a high performance metallic nanoparticle provides insights on engineering plasmonic structures for metal enhanced singlet oxygen generation for PDT.

Photodynamic inactivation assisted by localized surface plasmon resonance of silver nanoparticles: In vitro evaluation on Escherichia coli and Streptococcus mutans.

Localized surface plasmon resonance (LSPR) of gold nanoparticles has been reported to increase the antimicrobial effect of the photodynamic therapy. Although silver nanoparticles (AgNPs) are an efficient growth inhibitor of microorganisms, no studies exploring LSPR of AgNPs to enhance the photodynamic inactivation (PDI) have been related. In this work, we described the LSPR phenomenon of AgNP sand investigated its interaction with riboflavin, a natural photosensitizer. We evaluated the use of AgNPs coated with pectin (p-AgNP) in riboflavin (Rb)-mediated PDI of Escherichia coli (Gram- bacteria) and Streptococcus mutans (Gram + bacteria) using a blue light-emitting diode (λ = 455 ±â€¯20 nm) of optical power 200 mW. Irradiance was 90 mW/cm2 and radiant exposure varied according to the time exposure. Uptake of Rb and p-AgNP by the cells was evaluated by measuring the supernatant absorption spectra of the samples. We observed that LSPR of p-AgNPs was able to enhance the riboflavin photodynamic action on S. mutans but not on E. coli, probably due to the lower uptake of Rb by E. coli. Taken together, our results provide insights to explore the use of the LPRS promoted by silver nanostructures to optimize antimicrobial PDI protocols.

Acute chest syndrome in sickle cell disease.

Acute chest syndrome (ACS) is a leading complication of sickle cell disease (SCD) with significant morbidity and mortality. ACS is the most common cause of death and the second most common cause of hospitalization in patients with SCD. Delineating the specific cause of ACS is often difficult, and multiple risk factors that precipitate ACS frequently coexist. The prominent risk factors include infection, hypoxia, bronchial hyperresponsiveness, the SCD genotype, and opioid use. The key to the successful treatment of ACS is early recognition and initiation of treatment without delay. The main goal is to prevent and treat acute respiratory failure and, thus, minimize irreversible lung damage. This review focuses on the risk factors, pathogenesis, clinical presentation, and management of ACS.

Correction: Optimizing SIEM Throughput on the Cloud Using Parallelization.

[This corrects the article DOI: 10.1371/journal.pone.0162746.].

Salvage Plasmapheresis for Post-hepatectomy Liver Failure.

In the latest decades, an important change has been registered in liver surgery related to the progress of surgical techniques, critical care, and postoperative treatment, allowing a sharp decrease in mortality and morbidity. However, management of post-hepatectomy liver failure (PHLF) still remains a challenge and no supportive treatment has been found to be generally effective. The present study is a reappraisal of plasmapheresis as a potential supportive measure in patients with PHLF following major liver resection.

Optimizing SIEM Throughput on the Cloud Using Parallelization.

Processing large amounts of data in real time for identifying security issues pose several performance challenges, especially when hardware infrastructure is limited. Managed Security Service Providers (MSSP), mostly hosting their applications on the Cloud, receive events at a very high rate that varies from a few hundred to a couple of thousand events per second (EPS). It is critical to process this data efficiently, so that attacks could be identified quickly and necessary response could be initiated. This paper evaluates the performance of a security framework OSTROM built on the Esper complex event processing (CEP) engine under a parallel and non-parallel computational framework. We explain three architectures under which Esper can be used to process events. We investigated the effect on throughput, memory and CPU usage in each configuration setting. The results indicate that the performance of the engine is limited by the number of events coming in rather than the queries being processed. The architecture where 1/4th of the total events are submitted to each instance and all the queries are processed by all the units shows best results in terms of throughput, memory and CPU usage.