Paeoniflorin mitigates MMP-12 inflammation in silicosis via Yang-Yin-Qing-Fei Decoction in murine models.

BACKGROUND: Silicosis presents a significant clinical challenges and economic burdens, with Traditional Chinese Medicine (TCM) emerging as a potential therapeutic avenue. However, the precise effects and mechanisms of TCM in treating silicosis remain uncertain and subject to debate. OBJECTIVE: The study aims to elucidate the therapeutic role and mechanisms of the Yang-Yin-Qing-Fei Decoction (YYQFD) and its key component, paeoniflorin, in silicosis using a murine model. METHODS: Silicotic mice were treated with YYQFD, pirfenidone (PFD), or paeoniflorin. RAW264.7 cells and mouse lung fibroblasts (MLF) were stimulated with silica, matrix metalloproteinase-12 (MMP-12), or TGF-ß1, followed by treatment with paeoniflorin, PFD, or relevant inhibitors. YYQFD constituents were characterized using High-Performance Liquid Chromatography (HPLC). Lung fibrosis severity was assessed via histopathological examination, micro-CT imaging, lung functions, and Western blot analysis. Transcriptome sequencing and bioinformatics analysis were employed to delineate the gene expression profile and target genes modulated by YYQFD in silicosis. RESULTS: Treatment with YYQFD ameliorated silica-induced lung fibrosis. Transcriptome sequencing identified MMP-12 as a potential common target of YYQFD and PFD. Additionally, a potential pro-inflammatory role of MMP-12, regulated by silica-induced TLR4 signaling pathways, was revealed. Paeoniflorin, one of the most distinctive compounds in YYQFD, attenuated silica-induced MMP-12 increase and its derived inflammatory factors in macrophages through a direct binding effect. Notably, paeoniflorin treatment exerted anti-fibrotic effects by inhibiting MMP-12-derived inflammatory factors and TGF-ß1-induced myofibroblast differentiation in silica-exposed mice. CONCLUSIONS: This study underscores paeoniflorin as one of the most principal bioactive compounds in YYQFD, highlighting its capacity to attenuate lung inflammation driven by macrophage-derived MMP-12 and reduce lung fibrosis both in vivo and in vitro.

Modal-Transition-Induced Valleys of K2 in Piezoelectric Bilayer Laterally Vibrating Resonators.

Piezoelectric Laterally Vibrating Resonators (LVRs) have attracted significant attention as a potential technology for next-generation wafer-level multi-band filters. Piezoelectric bilayer structures such as Thin-film Piezoelectric-on-Silicon (TPoS) LVRs which aim to increase the quality factor (Q) or aluminum nitride and silicon dioxide (AlN/SiO2) composite membrane for thermal compensation have been proposed. However, limited studies have investigated the detailed behaviors of the electromechanical coupling factor (K2) of these piezoelectric bilayer LVRs. Herein, AlN/Si bilayer LVRs are selected as an example, we observed notable degenerative valleys in K2 at specific normalized thicknesses using two-dimensional finite element analysis (FEA), which has not been reported in the previous studies of bilayer LVRs. Moreover, the bilayer LVRs should be designed away from the valleys to minimize the reduction in K2. Modal-transition-induced mismatch between electric and strain fields of AlN/Si bilayer LVRs are investigated to interpret the valleys from energy considerations. Furthermore, the impact of various factors, including electrode configurations, AlN/Si thickness ratios, the Number of Interdigitated Electrode (IDT) Fingers (NFs), and IDT Duty Factors (DFs), on the observed valleys and K2 are analyzed. These results can provide guidance for the designs of piezoelectric LVRs with bilayer structure, especially for LVRs with a moderate K2 and low thickness ratio.

Surface Plasmon Resonance Sensor Based on Core-Shell Fe3O4@SiO2@Au Nanoparticles Amplification Effect for Detection of T-2 Toxin.

In this paper, a core-shell based on the Fe3O4@SiO2@Au nanoparticle amplification technique for a surface plasmon resonance (SPR) sensor is proposed. Fe3O4@SiO2@AuNPs were used not only to amplify SPR signals, but also to rapidly separate and enrich T-2 toxin via an external magnetic field. We detected T-2 toxin using the direct competition method in order to evaluate the amplification effect of Fe3O4@SiO2@AuNPs. A T-2 toxin-protein conjugate (T2-OVA) immobilized on the surface of 3-mercaptopropionic acid-modified sensing film competed with T-2 toxin to combine with the T-2 toxin antibody-Fe3O4@SiO2@AuNPs conjugates (mAb-Fe3O4@SiO2@AuNPs) as signal amplification elements. With the decrease in T-2 toxin concentration, the SPR signal gradually increased. In other words, the SPR response was inversely proportional to T-2 toxin. The results showed that there was a good linear relationship in the range of 1 ng/mL~100 ng/mL, and the limit of detection was 0.57 ng/mL. This work also provides a new possibility to improve the sensitivity of SPR biosensors in the detection of small molecules and in disease diagnosis.

Trimetallic Au@Pd@Pt nanozyme-enhanced lateral flow immunoassay for the detection of SARS-CoV-2 nucleocapsid protein.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seriously threatened global public health. Establishing a rapid and sensitive diagnostic test for early detection of the SARS-CoV-2 nucleocapsid protein is urgently required to defend against the pandemic. Herein, an enhanced lateral flow immunoassay (LFIA) was fabricated by trimetallic Au@Pd@Pt core-shell nanozymes for detection of the SARS-CoV-2 nucleocapsid protein. The Au@Pd@Pt nanozymes (Au@Pd@Pt NZs) synthesized via a one-pot method, with a dendrite morphology and uniform particle size, showed excellent peroxidase-like activity. Due to the perfect enzyme-like catalytic activity toward 3,3',5,5'-tetramethylbenzidine (TMB) in the presence of hydrogen peroxide (H2O2), the catalytic signal could be generated even by a tiny amount of Au@Pd@Pt NZs accumulated on the test strip. Therefore, rapid detection with higher sensitivity was achieved. The Au@Pd@Pt NZs-based LFIA provided a quantitative range of 0.05-100 ng mL-1 with a limit of detection of 0.037 ng mL-1, which was 17-fold lower than the LFIA without enhancement. The average recoveries from spiked samples were in the range of 92.5-107.9% with relative standard deviations all less than 4%, indicating the reliability and repeatability of the proposed LFIA. Additionally, the proposed LFIA could report results within 30 min using a microplate reader. In conclusion, the Au@Pd@Pt NZs-LFIA is a rapid, simple, and sensitive method for detecting the SARS-CoV-2 nucleocapsid protein.

Magnetic/fluorescent dual-modal lateral flow immunoassay based on multifunctional nanobeads for rapid and accurate SARS-CoV-2 nucleocapsid protein detection.

The SARS-CoV-2 pandemic has posed a huge challenge to rapid and accurate diagnosis of SARS-CoV-2 in the early stage of infection. In this work, we developed a novel magnetic/fluorescent dual-modal lateral flow immunoassay (LFIA) based on multifunctional nanobeads for rapid and accurate determination of SARS-CoV-2 nucleocapsid protein (NP). The multifunctional nanobeads were fabricated by using polyethyleneimine (PEI) as a mediate shell to combine superparamagnetic Fe3O4 core with dual quantum dot shells (MagDQD). The core-shell structure of MagDQD label with high loading density of quantum dots (QDs) and superior magnetic content realized LFIA with dual quantitative analysis modal from the assemblies of individual single nanoparticles. The LFIA integrated the advantages of magnetic signal and fluorescent signal, resulting excellent accuracy for quantitative analysis and high elasticity of the overall detection. In addition, magnetic signal and fluorescent signal both had high sensitivity with the limit of detection (LOD) as 0.235 ng mL-1 and 0.012 ng mL-1, respectively. The recovery rates of the methods in simulated saliva samples were 91.36%-103.60% (magnetic signal) and 94.39%-104.38% (fluorescent signal). The results indicate the method has a considerable potential to be an effective tool for diagnose SARS-CoV-2 in the early stage of infection.

A photoelectrochemical immunosensor based on Z-scheme CdS composite heterojunction for aflatoxin B1.

Aflatoxin B1 (AFB1) is a highly toxic fungal contaminant widely found in agricultural products. It causes serious harm to human health and the environment. Thus, a fast and sensitive detection approach is urgently needed to prevent AFB1-contaminated products from entering the market effectively. A photoelectrochemical (PEC) immunosensor was developed based on tungsten trioxide/cadmium sulfide core/shell coated with a composite layer consisting of polydopamine and loaded gold nanoparticles (WO3/CdS@PDA/Au) for AFB1 detection. CdS formed a heterojunction with WO3, which improved the photoelectric performance. The coated PDA reducing CdS toxicity was demonstrated by biological experiment of Bacillus subtilis. PDA and Au NPs promoted electron transfer between the semiconductors, being beneficial promoting the photoelectron transfer. Additionally, the antibodies were immobilized on WO3/CdS@PDA/Au via the reactive quinones on the surface of the PDA and electrostatic adsorption from Au NPs. The WO3/CdS@PDA/Au composite as a Z-scheme heterojunction possessed high performance of photocurrent response, and the photoproduced electron/hole transfer path was speculated by electrons spin-resonance spectroscopy technique. Under the optimum experimental conditions, the PEC immunosensor showed a wide linear detection range from 0.05 to 100 ng mL-1 for AFB1, indicating that the immunosensor has a bright application prospect.

Virtual Sensor Array Based on Butterworth-Van Dyke Equivalent Model of QCM for Selective Detection of Volatile Organic Compounds.

Recently virtual sensor arrays (VSAs) have been developed to improve the selectivity of volatile organic compound (VOC) sensors. However, most reported VSAs rely on detecting single property change of the sensing material after their exposure to VOCs, thus resulting in a loss of much valuable information. In this work, we propose a VSA with the high dimensionality of outputs based on a quartz crystal microbalance (QCM) and a sensing layer of MXene. Changes in both mechanical and electrical properties of the MXene film are utilized in the detection of the VOCs. We take the changes of parameters of the Butterworth-van Dyke model for the QCM-based sensor operated at multiple harmonics as the responses of the VSA to various VOCs. The dimensionality of the VSA's responses has been expanded to four independent outputs, and the responses to the VOCs have shown good linearity in multidimensional space. The response and recovery times are 16 and 54 s, respectively. Based on machine learning algorithms, the proposed VSA accurately identifies different VOCs and mixtures, as well as quantifies the targeted VOC in complex backgrounds (with an accuracy of 90.6%). Moreover, we demonstrate the capacity of the VSA to identify "patients with diabetic ketosis" from volunteers with an accuracy of 95%, based on the detection of their exhaled breath. The QCM-based VSA shows great potential for detecting VOC biomarkers in human breath for disease diagnosis.

Improving the catalytic thermostability of Bacillus altitudinis W3 ω-transaminase by proline substitutions.

As a green biocatalyst, transaminase with high thermostability can be better employed to synthesize many pharmaceutical intermediates in industry. To improve the thermostability of (R)-selective amine transaminase from Bacillus altitudinis W3, related mutation sites were determined by multiple amino acid sequence alignment between wild-type ω-transaminase and four potential thermophilic ω-transaminases, followed by replacement of the related amino acid residues with proline by site-directed mutagenesis. Three stabilized mutants (D192P, T237P, and D192P/T237P) showing the highest stability were obtained and used for further analysis. Comparison with the wild-type enzyme revealed that the double mutant D192P/T237P exhibited the largest shift in thermostability, with a 2.5-fold improvement of t 1/2 at 40 °C, and a 6.3 °C increase in T 50 15, and a 5 °C higher optimal catalytic temperature. Additionally, this mutant exhibited an increase in catalytic efficiency (k cat/K m) relative to the wild-type enzyme. Modeling analysis indicated that the improved thermostability of the mutants could be associated with newly formed hydrophobic interactions and hydrogen bonds. This study shown that proline substitutions guided by sequence alignment to improve the thermostability of (R)-selective amine transaminase was effective and this method can also be used to engineering other enzymes.

Bio-Inspired PHM Model for Diagnostics of Faults in Power Transformers Using Dissolved Gas-in-Oil Data.

Prognostics and Health Management (PHM) is an emerging technique which can improve the availability and efficiency of equipment. A series of related optimization of the PHM system has been achieved due to the growing need for lowering the cost of maintenance. The PHM system highly relies on data collected from its components. Based on the theory of machine learning, this paper proposes a bio-inspired PHM model based on a dissolved gas-in-oil dataset (DGA) to diagnose faults of transformes in power grids. Specifically, this model applies Bat algorithm (BA), a metaheuristic population-based algorithm, to optimize the structure of the Back-propagation neural network (BPNN). Furthermore, this paper proposes a modified Bat algorithm (MBA); here the chaos strategy is utilized to improve the random initialization process of BA in order to avoid falling into local optima. To prove that the proposed PHM model has better fault diagnostic performance than others, fitness and mean squared error (MSE) of Bat-BPNN are set as reference amounts to compare with other power grid PHM approaches including BPNN, Particle swarm optimization (PSO)-BPNN, as well as Genetic algorithm (GA)-BPNN. The experimental results show that the BA-BPNN model has increased the fault diagnosis accuracy from 77.14% to 97.14%, which is higher than other power transformer PHM models.

Machine Learning-Based Sensor Data Modeling Methods for Power Transformer PHM.

An emerging prognostic and health management (PHM) technology has recently attracted a great deal of attention from academies, industries, and governments. The need for higher equipment availability and lower maintenance cost is driving the development and integration of prognostic and health management systems. PHM models depend on the smart sensors and data generated from sensors. This paper proposed a machine learning-based methods for developing PHM models from sensor data to perform fault diagnostic for transformer systems in a smart grid. In particular, we apply the Cuckoo Search (CS) algorithm to optimize the Back-propagation (BP) neural network in order to build high performance fault diagnostics models. The models were developed using sensor data called dissolved gas data in oil of the power transformer. We validated the models using real sensor data collected from power transformers in China. The results demonstrate that the developed meta heuristic algorithm for optimizing the parameters of the neural network is effective and useful; and machine learning-based models significantly improved the performance and accuracy of fault diagnosis/detection for power transformer PHM.

Anticancer effect of Polyphyllin Ι in colorectal cancer cells through ROS-dependent autophagy and G2/M arrest mechanisms.

Polyphyllin Ι is a steroidal saponin isolated from the rhizoma of Paris polyphylla. In the present study, we aimed to investigate the anticancer effects of polyphyllin Ι in colorectal cancer and to elucidate the potential underlying molecular mechanisms. Using, CCK8 assay, flow cytometry, laser confocal microscope analysis and western blot, the anticancer effects of the polyphyllin Ι were analysed in colorectal cells. Our results indicate that polyphyllin Ι significantly decreased cell viability of HCT 116 cells and induced autophagy. Furthermore, we found that polyphyllin Ι induced autophagy in an ROS-dependent cell death and not related with PI3 K/AKT/mTOR pathway. We also provide evidence that excessive ROS triggered by polyphyllin Ι could induce G2/M phase arrest via regulating cycle proteins expression of cell cycle regulators, such as p21 and cyclinB1. In conclusion, polyphyllin Ι exhibit anticancer effect through ROS-dependent autophagy and induces G2/M arrest in colorectal cancer.