Development of fluorescence-linked immunosorbent assay for rapid detection of Staphylococcus aureus

Staphylococcus aureus is a major pathogen that causes infections and life-threatening diseases. Although antibiotics, such as methicillin, have been used, methicillin-resistant S. aureus (MRSA) causes high morbidity and mortality rates, and conventional detection methods are difficult to be used because of time-consuming process. To control the spread of S. aureus, a development of a rapid and simple detection method is required. In this study, we generated a fluorescent anti-S. aureus antibody, and established a novel fluorescence-linked immunosorbent assay (FLISA)-based S. aureus detection method. The method showed high sensitivity and low limit of detection toward MRSA detection. The assay time for FLISA was 5 h, which was faster than that of conventional enzyme-linked immunosorbent assay (ELISA) or rapid ELISA. Moreover, the FLISA-based detection method was applied to diagnose clinically isolated MRSA samples that required only 5.3 h of preincubation. The FLISA method developed in this study can be widely applied as a useful tool for convenient S. aureus detection. KEY POINTS: • A fluorescence-linked immunosorbent assay-based S. aureus detection method • Simultaneous quantification of a maximum of 96 samples within 5 h • Application of the novel system to diagnosis clinical isolates.

Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis

Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryngeal cancer is challenging, especially when differentiating it from other laryngeal ailments. This study presents an artificial intelligence model designed to distinguish between healthy voices, laryngeal cancer voices, and those of the other laryngeal conditions. We gathered voice samples of individuals with laryngeal cancer, vocal cord paralysis, benign mucosal diseases, and healthy participants. Comprehensive testing was conducted to determine the best mel-frequency cepstral coefficient conversion and machine learning techniques, with results analyzed in-depth. In our tests, laryngeal diseases distinguishing from healthy voices achieved an accuracy of 0.85-0.97. However, when multiclass classification, accuracy ranged from 0.75 to 0.83. These findings highlight the challenges of artificial intelligence-driven voice-based diagnosis due to overlaps with benign conditions but also underscore its potential.

A Waveguide Inline Binary Metasurface for Wavelength-Selective Transmission and Standing Wave Focusing

This study presents an innovative inline metasurface design for selective wavelength transmission and focusing. When integrated into optical fibers, it improves the stability and compatibility with techniques like wavelength division multiplexing and phase modulation. Precise parameters, determined through analytical calculations and simulations, allow for the design of multifunctional lenses within the optical fiber platform. The numerical results demonstrate unmodulated transmission for specific wavelengths, while others exhibit standing wave focusing with a 0.67 μm beam radius and a 0.31 μm depth of focus. This technology holds promise for applications in quantum experiments, sensing, and optical communication.

Simulational investigation of self-aligned bilayer linear grating enabling highly enhanced responsivity of MWIR InAs/GaSb type-II superlattice (T2SL) photodetector

Linear gratings polarizers provide remarkable potential to customize the polarization properties and tailor device functionality via dimensional tuning of configurations. Here, we extensively investigate the polarization properties of single- and double-layer linear grating, mainly focusing on self-aligned bilayer linear grating (SABLG), serving as a wire grid polarizer in the mid-wavelength infrared (MWIR) region. Computational analyses revealed the polarization properties of SABLG, highlighting enhancement in TM transmission and reduction in TE transmission compared to single-layer linear gratings (SLG) due to optical cavity effects. As a result, the extinction ratio is enhanced by approximately 2724-fold in wavelength 3-6 μm. Furthermore, integrating the specially designed SABLG with an MWIR InAs/GaSb Type-II Superlattice (T2SL) photodetector yields a significantly enhanced spectral responsivity. The TM-spectral responsivity of SABLG is enhanced by around twofold than the bare device. The simulation methodology and analytical analysis presented herein provide a versatile route for designing optimized polarimetric structures integrated into infrared imaging devices, offering superior capabilities to resolve linear polarization signatures.

Compassion Catalysts: Unveiling Proactive Pathways to Job Performance

This study aims to provide a better understanding of the mechanisms underlying the relationship between the experiences of compassion and job performance. Specifically, we test if positive emotion and job crafting could help explain the experience of the compassion-job performance link. Using a sample of 312 employees in large-sized domestic companies located in South Korea, we found that the experience of compassion was positively related to job performance. In addition, we adopted the motivational approach to demonstrate that the positive relationship between the experience of compassion and job performance was sequentially mediated by positive emotion and job crafting. Our study advances the literature on workplace compassion by introducing job crafting as a novel driver in explaining the positive effects of compassion and contributes novel insights into the mechanisms underlying the relationship between compassion and job performance. Our findings also suggest that to enhance employees' job performance and facilitate employees' proactive behaviors (i.e., job crafting), organizations must foster a compassionate work environment by placing high importance on compassion.

Silver-functionalized bismuth oxide (AgBi2O3) nanoparticles for the superior electrochemical detection of glucose, NO2- and H2O2

In this study, silver-functionalized bismuth oxide (AgBi₂O₃) nanoparticles (SBO NPs) were successfully synthesized by a highly efficient hydrothermal method. The as-synthesized SBO nanoparticles were characterized using FT-IR, P-XRD, XPS, HR-SEM, and HR-TEM analytical methods. It was found that the NPs were in spherical shape and hexagonal crystal phase. The newly prepared SBO electrode was further utilized for the detection of glucose, NO₂^- and H₂O₂ by cyclic voltammetry (CV) and amperometric methods. The electrodes exhibited high sensitivity (2.153 μA mM^-1 cm^-2 for glucose, 22 μA mM^-1 cm^-2 for NO₂^- and 1.72 μA mM^-1 cm^-2 for H₂O₂), low LOD (0.87 μM for glucose, 2.8 μM for NO₂^- and 1.15 μM for H₂O₂) and quick response time (3 s for glucose, 2 s for both NO₂^- and H₂O₂ respectively). The sensor exhibited outstanding selectivity despite the presence of various interferences. The developed sensor exhibited good repeatability, reproducibility, and stability. In addition, the sensor was used to measure glucose, H₂O₂ in human serum, and NO₂^- in milk and river water samples, demonstrating its potential for use in the real sample.

Generation of a novel monoclonal antibody against inflammatory biomarker S100A8 using hybridoma technology

OBJECTIVES

S100A8 is highly expressed in several inflammatory and oncological conditions. To address the current lack of a reliable and sensitive detection method for S100A8, we generated a monoclonal antibody with a high binding affinity to human S100A8 to enable early disease diagnosis.

RESULTS

A soluble recombinant S100A8 protein with a high yield and purity was produced using Escherichia coli. Next, mice were immunized with recombinant S100A8 to obtain anti-human S100A8 monoclonal antibodies using hybridoma technology. Lastly, the high binding activity of the antibody was confirmed and its sequence was identified.

CONCLUSIONS

This method, including the production of antigens and antibodies, will be useful for the generation of hybridoma cell lines that produce anti-S100A8 monoclonal antibodies. Moreover, the sequence information of the antibody can be used to develop a recombinant antibody for use in various research and clinical applications.

A review of the Bayesian approach with the MCMC and the HMC as a competitor of classical likelihood statistics for pharmacometricians

This article reviews the Bayesian inference with the Monte Carlo Markov Chain (MCMC) and the Hamiltonian Monte Carlo (HMC) samplers as a competitor of the classical likelihood statistical inference for pharmacometricians. The MCMC and the HMC samplers have greatly contributed to realization of the Bayesian methods with minimal requirement of mathematical theory. They do not require any closed form of the posterior density nor linear approximation of complex nonlinear models in high dimension even with non-conjugate priors. The HMC even weakens the dependency of the chain and improves computational efficiency. Pharmacometrics is one of great beneficiaries since they use complex multivariate multilevel nonlinear mixed effects models based on the restricted maximum likelihood estimation. Comprehension of the Bayesian approach will help pharmacometricians to access the data analysis more conveniently.

Free Vibration Responses of Functionally Graded CNT-Reinforced Composite Conical Shell Panels

Functionally graded CNT (carbon nanotube)-reinforced composites (FG-CNTRCs) are intensively studied because the mechanical behaviors of conventional composites can be dramatically improved. Only a small amount of CNTs are appropriately distributed through the thickness. However, the studies on conical shell panels have been poorly reported when compared with beams, plates and cylindrical shells, even though more parameters are associated with the mechanical behaviors of conical shell panels. In this context, this study intends to profoundly investigate the free vibration of FG-CNTRC conical shell panels by developing an effective and reliable 2-D (two-dimensional) numerical method. The displacement field is expressed using the first-order shear deformation shell theory, and it is approximated by the 2-D planar natural element method (NEM). The conical shell surface is transformed into the 2-D planar NEM grid, and the approach for MITC3+shell element is employed to suppress the shear locking. The developed numerical method is validated through the benchmark experiments, and the free vibration responses of FG-CNTRC conical shell panels are investigated with respect to all the associated parameters. It is found from the numerical results that the free vibration of FG-CNTRC conical shell panels is significantly influenced by the volume fraction and distribution pattern of CNTs, the geometry parameters of the conical shell, and the boundary condition.

Abnormal network flow detection based on application execution patterns from Web of Things (WoT) platforms

In this paper, we present a research work on a novel methodology of identifying abnormal behaviors at the underlying network monitor layer during runtime based on the execution patterns of Web of Things (WoT) applications. An execution pattern of a WoT application is a sequence of profiled time delays between the invocations of involved Web services, and it can be obtained from WoT platforms. We convert the execution pattern to a time sequence of network flows that are generated when the WoT applications are executed. We consider such time sequences as a whitelist. This whitelist reflects the valid application execution patterns. At the network monitor layer, our applied RETE algorithm examines whether any given runtime sequence of network flow instances does not conform to the whitelist. Through this approach, it is possible to interpret a sequence of network flows with regard to application logic. Given such contextual information, we believe that the administrators can detect and reason about any abnormal behaviors more effectively. Our empirical evaluation shows that our RETE-based algorithm outperforms the baseline algorithm in terms of memory usage.