[Determination of 18 caine anesthetics in animal meat using solid phase extraction combined with ultra-performance liquid chromatography-tandem mass spectrometry].

Because of the widespread application of anesthetic drugs in the fields of animal breeding and transportation, demand for the rapid, sensitive detection of anesthetic drugs in animal meat is increasing. The complex animal meat matrix contains various interfering substances, such as proteins, fats, and phospholipids, along with anesthetic drug residues at very low concentrations. Therefore, adopting appropriate pretreatment methods is necessary to improve the sensitivity of detection. In this study, a rapid, accurate analytical method based on ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and solid phase extraction (SPE) was established to determine the contents of 18 caines in animal meat. The MS parameters, such as the collision energies of 18 caines, were optimized. Furthermore, the chromatographic separation conditions and response intensities of the caine in different mobile phases were compared. The effects of different pretreatment conditions on the extraction efficiencies of the 18 caines in meat samples and those of different purification conditions, such as extraction solvent, SPE column, and dimethylsulfoxide (DMSO) dosage, on their recoveries were investigated. Combined with the external standard method, the 18 caines in meat were successfully quantified. Sample pretreatment is a three-step process. First, in ultrasound-assisted extraction, 2.0 g samples were added to 2.0 mL water and extracted using 10 mL 0.1% (v/v) formic acid in acetonitrile under ultrasound conditions for 10 min. SPE was then performed using an Oasis PRIME HLB column. Finally, DMSO-assisted concentration was employed: the organic layer was collected and dried at 40 ℃ under a stream of N2 gas with the addition of 100 µL DMSO. Acetonitrile-water (1∶9, v/v) was added to the residue to yield a final volume of 1.0 mL for use in UPLC-MS/MS. The 18 caines were separated using an HSS T3 (100 mm×2.1 mm, 1.8 µm) column with 0.1% (v/v) formic acid in water (containing 0.02 mmol/L ammonium acetate) and methanol as mobile phases. Samples were detected using an electrospray ion source (ESI) in the positive ion and multiple reaction monitoring (MRM) modes during UPLC-MS/MS. Under the optimized conditions, the 18 target caine anesthetics displayed good linearities in the range of 1.00-50.0 µg/L, and the correlation coefficients (R2) were >0.999. The respective limits of detection (LODs) and quantification (LOQs) were 0.2-0.5 µg/kg, and 0.6-1.5 µg/kg. In pork, beef, and mutton samples, the recoveries obtained at three spiked levels were 83.4%-100.4% with relative standard deviations (RSDs) of 3.1%-8.5%. This simple, rapid, sensitive method may be applied in the detection of 18 caine anesthetics in animal meat and may provide technical support to the food safety department in China in monitoring the residues of caine anesthetics in animal meat.

Model design and parameter optimization of CNN for side-channel cryptanalysis.

BACKGROUND: The side-channel cryptanalysis method based on convolutional neural network (CNNSCA) can effectively carry out cryptographic attacks. The CNNSCA network models that achieve cryptanalysis mainly include CNNSCA based on the VGG variant (VGG-CNNSCA) and CNNSCA based on the Alexnet variant (Alex-CNNSCA). The learning ability and cryptanalysis performance of these CNNSCA models are not optimal, and the trained model has low accuracy, too long training time, and takes up more computing resources. In order to improve the overall performance of CNNSCA, the paper will improve CNNSCA model design and hyperparameter optimization. METHODS: The paper first studied the CNN architecture composition in the SCA application scenario, and derives the calculation process of the CNN core algorithm for side-channel leakage of one-dimensional data. Secondly, a new basic model of CNNSCA was designed by comprehensively using the advantages of VGG-CNNSCA model classification and fitting efficiency and Alex-CNNSCA model occupying less computing resources, in order to better reduce the gradient dispersion problem of error back propagation in deep networks, the SE (Squeeze-and-Excitation) module is newly embedded in this basic model, this module is used for the first time in the CNNSCA model, which forms a new idea for the design of the CNNSCA model. Then apply this basic model to a known first-order masked dataset from the side-channel leak public database (ASCAD). In this application scenario, according to the model design rules and actual experimental results, exclude non-essential experimental parameters. Optimize the various hyperparameters of the basic model in the most objective experimental parameter interval to improve its cryptanalysis performance, which results in a hyper-parameter optimization scheme and a final benchmark for the determination of hyper-parameters. RESULTS: Finally, a new CNNSCA model optimized architecture for attacking unprotected encryption devices is obtained-CNNSCAnew. Through comparative experiments, CNNSCAnew's guessing entropy evaluation results converged to 61. From model training to successful recovery of the key, the total time spent was shortened to about 30 min, and we obtained better performance than other CNNSCA models.

ZNRF1 Mediates Epidermal Growth Factor Receptor Ubiquitination to Control Receptor Lysosomal Trafficking and Degradation.

Activation of the epidermal growth factor receptor (EGFR) is crucial for development, tissue homeostasis, and immunity. Dysregulation of EGFR signaling is associated with numerous diseases. EGFR ubiquitination and endosomal trafficking are key events that regulate the termination of EGFR signaling, but their underlying mechanisms remain obscure. Here, we reveal that ZNRF1, an E3 ubiquitin ligase, controls ligand-induced EGFR signaling via mediating receptor ubiquitination. Deletion of ZNRF1 inhibits endosome-to-lysosome sorting of EGFR, resulting in delayed receptor degradation and prolonged downstream signaling. We further demonstrate that ZNRF1 and Casitas B-lineage lymphoma (CBL), another E3 ubiquitin ligase responsible for EGFR ubiquitination, mediate ubiquitination at distinct lysine residues on EGFR. Furthermore, loss of ZNRF1 results in increased susceptibility to herpes simplex virus 1 (HSV-1) infection due to enhanced EGFR-dependent viral entry. Our findings identify ZNRF1 as a novel regulator of EGFR signaling, which together with CBL controls ligand-induced EGFR ubiquitination and lysosomal trafficking.

PyBindingCurve, Simulation, and Curve Fitting to Complex Binding Systems at Equilibrium.

Understanding multicomponent binding interactions in protein-ligand, protein-protein, and competition systems is essential for fundamental biology and drug discovery. Hand-deriving equations quickly become unfeasible when the number of components is increased, and direct analytical solutions only exist to a certain complexity. To address this problem and allow easy access to simulation, plotting, and parameter fitting to complex systems at equilibrium, we present the Python package PyBindingCurve. We apply this software to explore homodimer and heterodimer formations culminating in the discovery that under certain conditions, homodimers are easier to break with an inhibitor than heterodimers and may also be more readily depleted. This is a potentially valuable and overlooked phenomenon of great importance to drug discovery. PyBindingCurve may be expanded to operate on any equilibrium binding system and allows definition of custom systems using a simple syntax. PyBindingCurve is available under the MIT license at https://github.com/stevenshave/pybindingcurve as the Python source code accompanied by examples and as an easily installable package within the Python Package Index.