Apigenin Combined With Gefitinib Blocks Autophagy Flux and Induces Apoptotic Cell Death Through Inhibition of HIF-1α, c-Myc, p-EGFR, and Glucose Metabolism in EGFR L858R+T790M-Mutated H1975 Cells

Cancer cells are characterized by abnormally increased glucose uptake and active bio-energy and biosynthesis to support the proliferation, metastasis, and drug resistant survival. We examined the therapeutic value of the combination of apigenin (a natural small-molecule inhibitor of Glut1 belonging to the flavonoid family) and gefitinib on epidermal growth factor receptor (EGFR)-resistant mutant non-small cell lung cancer, to notably damage glucose utilization and thus suppress cell growth and malignant behavior. Here, we demonstrate that apigenin combined with gefitinib inhibits multiple oncogenic drivers such as c-Myc, HIF-1α, and EGFR, reduces Gluts and MCT1 protein expression, and inactivates the 5′ adenosine monophosphate-activated protein kinase (AMPK) signaling, which regulates glucose uptake and maintains energy metabolism, leading to impaired energy utilization in EGFR L858R-T790M-mutated H1975 lung cancer cells. H1975 cells exhibit dysregulated metabolism and apoptotic cell death following treatment with apigenin + gefitinib. Therefore, the combined apigenin + gefitinib treatment presents an attractive strategy as alternative treatment for the acquired resistance to EGFR-TKIs in NSCLC

Etiologic Diagnosis of Lower Respiratory Tract Bacterial Infections Using Sputum Samples and Quantitative Loop-Mediated Isothermal Amplification

Etiologic diagnoses of lower respiratory tract infections (LRTI) have been relying primarily on bacterial cultures that often fail to return useful results in time. Although DNA-based assays are more sensitive than bacterial cultures in detecting pathogens, the molecular results are often inconsistent and challenged by doubts on false positives, such as those due to system- and environment-derived contaminations. Here we report a nationwide cohort study on 2986 suspected LRTI patients across P. R. China. We compared the performance of a DNA-based assay qLAMP (quantitative Loop-mediated isothermal AMPlification) with that of standard bacterial cultures in detecting a panel of eight common respiratory bacterial pathogens from sputum samples. Our qLAMP assay detects the panel of pathogens in 1047(69.28%) patients from 1533 qualified patients at the end. We found that the bacterial titer quantified based on qLAMP is a predictor of probability that the bacterium in the sample can be detected in culture assay. The relatedness of the two assays fits a logistic regression curve. We used a piecewise linear function to define breakpoints where latent pathogen abruptly change its competitive relationship with others in the panel. These breakpoints, where pathogens start to propagate abnormally, are used as cutoffs to eliminate the influence of contaminations from normal flora. With help of the cutoffs derived from statistical analysis, we are able to identify causative pathogens in 750 (48.92%) patients from qualified patients. In conclusion, qLAMP is a reliable method in quantifying bacterial titer. Despite the fact that there are always latent bacteria contaminated in sputum samples, we can identify causative pathogens based on cutoffs derived from statistical analysis of competitive relationship

An Efficient and Fast Model Reduced Kernel KNN for Human Activity Recognition

With accumulation of data and development of artificial intelligence, human activity recognition attracts lots of attention from researchers. Many classic machine learning algorithms, such as artificial neural network, feed forward neural network, K-nearest neighbors, and support vector machine, achieve good performance for detecting human activity. However, these algorithms have their own limitations and their prediction accuracy still has space to improve. In this study, we focus on K-nearest neighbors (KNN) and solve its limitations. Firstly, kernel method is employed in model KNN, which transforms the input features to be the high-dimensional features. The proposed model KNN with kernel (K-KNN) improves the accuracy of classification. Secondly, a novel reduced kernel method is proposed and used in model K-KNN, which is named as Reduced Kernel KNN (RK-KNN). It reduces the processing time and enhances the classification performance. Moreover, this study proposes an approach of defining number of K neighbors, which reduces the parameter dependency problem. Based on the experimental works, the proposed RK-KNN obtains the best performance in benchmarks and human activity datasets compared with other models. It has super classification ability in human activity recognition. The accuracy of human activity data is 91.60% for HAPT and 92.67% for Smartphone, respectively. Averagely, compared with the conventional KNN, the proposed model RK-KNN increases the accuracy by 1.82% and decreases standard deviation by 0.27. The small gap of processing time between KNN and RK-KNN in all datasets is only 1.26 seconds

Enhancing the Immunogenicity of RBD Protein Variants through Amino Acid E484 Mutation in SARS-CoV-2

In the context of the COVID-19 pandemic, conducting antibody testing and vaccination is critical. In particular, the continued evolution of SARS-CoV-2 raises concerns about the effectiveness of vaccines currently in use and the activity of neutralizing antibodies. Here, we used the Escherichia coli expression system to obtain nine different SARS-CoV-2 RBD protein variants, including six single-point mutants, one double-point mutant, and two three-point mutants. Western blotting results show that nine mutants of the RBD protein had strong antigenic activity in vitro. The immunogenicity of all RBD proteins was detected in mice to screen for protein mutants with high immunogenicity. The results show that the mutants E484K, E484Q, K417T-E484K-N501Y, and K417N-E484K-N501Y, especially the former two, had better immunogenicity than the wild type. This suggests that site E484 has a significant impact on the function of the RBD protein. Our results demonstrate that recombinant RBD protein expressed in E. coli can be an effective tool for the development of antibody detection methods and vaccines

Enhancement of Binding Kinetics on Affinity Substrates Using Asymmetric Electroosmotic Flow on a Sinusoidal Bipolar Electrode

In the context of the COVID-19 epidemic, enhancing the transport of analyte to a sensor surface is crucial for rapid detection of biomolecules since common conditions, including low diffusion coefficients, cause inordinately long detection times. Integrated microfluidic immunoassay chips are receiving increasing attention for their low sample volume and fast response time. We herein take advantage of asymmetric ICEO flow at a bipolar sinusoidal electrode to improve the rate of antibody binding to the reaction surface based on finite element modeling. Three different microfluidic cavities are proposed by changing the positions of the surface reaction area. We further investigate the relationship between binding enhancement and reaction surface positions, Damkohler number, and the voltage and frequency of the AC signal applied to the driving electrodes. Furthermore, the influence of the AC signal applied to the sinusoidal bipolar electrode on antigen–antibody-binding performance is studied in detail. Above all, the simulation results demonstrate that the microfluidic immune-sensor with a sinusoidal bipolar electrode could not only significantly improve the heterogeneous immunoassays but also enable efficient enhancement of assays in a selected reaction region within the micro-cavity, providing a promising approach to a variety of immunoassay applications, such as medical diagnostics and environmental and food monitoring

Intelligent Recognition System Based on Contour Accentuation for Navigation Marks

Sensing navigational environment represented by navigation marks is an important task for unmanned ships and intelligent navigation systems, and the sensing can be performed by recognizing the images from a camera. In order to improve the image recognition accuracy, this paper combined a contour accentuation algorithm into a multiple scale attention mechanism-based classification model for navigation marks. Experimental results show that the method increases the accuracy of navigation mark classification from 95.98% to 96.53%. Based on the classification model, an intelligent navigation mark recognition system was developed for the Changjiang Nanjing Waterway Bureau, in which the model is deployed and updated by the TensorFlow Serving

The VHSE-based prediction of proteasomal cleavage sites.

Prediction of proteasomal cleavage sites has been a focus of computational biology. Up to date, the predictive methods are mostly based on nonlinear classifiers and variables with little physicochemical meanings. In this paper, the physicochemical properties of 14 residues both upstream and downstream of a cleavage site are characterized by VHSE (principal component score vector of hydrophobic, steric, and electronic properties) descriptors. Then, the resulting VHSE descriptors are employed to construct prediction models by support vector machine (SVM). For both in vivo and in vitro datasets, the performance of VHSE-based method is comparatively better than that of the well-known PAProC, MAPPP, and NetChop methods. The results reveal that the hydrophobic property of 10 residues both upstream and downstream of the cleavage site is a dominant factor affecting in vivo and in vitro cleavage specificities, followed by residue's electronic and steric properties. Furthermore, the difference in hydrophobic potential between residues flanking the cleavage site is proposed to favor substrate cleavages. Overall, the interpretable VHSE-based method provides a preferable way to predict proteasomal cleavage sites