Analgesic effect of flurbiprofen ester and its effect on serum inflammatory factors and Β-endorphin expression in rats with incision pain

Purpose: To study the analgesic effect of flurbiprofen ester in rats with incision pain, and its effect on serum inflammatory factors and β-endorphin expression. Methods: Seventy-five (75) healthy rats with foot contraction threshold induced by basic mechanical stimulation were randomly assigned to control, model control and treatment groups. Flurbiprofen was administered in 3 doses: 5, 10 and 15 mg/kg. Then, 3 mL of ventricular blood was taken from anesthetized rats and the serum levels of tumor necrosis factor-α (TNF- α), interleukin-1 β, interleukin-6 and β-endorphin were measured. The expression of β-endorphin in the spinal cord of rats with lumbar enlargement and ARC was determined. Results: The TNF- α, interleukin-1 β and interleukin-6 concentrations were significantly lower in treatment group than in model rats, and decreased with time and dose (p < 0.05). In the treatment group, the level of serum β-endorphin decreased with increase in dose at 1 h, but increased with increase in dose at 5 h and 10 h (p < 0.05). The levels of β-endorphin in the spinal cord, was significantly lower in model rats than in control rats (p < 0.05). Conclusion: Pre-administration of flurbiprofen ester reduces serum inflammatory factors and upregulates β-endorphin expression in rats with incision pain. Thus, it flurbiprofen exerts analgesic effect. Keywords: Flurbiprofen ester, Incision pain, Rat, Analgesia, Inflammatory factor, β-endorphi

HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods

Research on the Standardization of Drug Test Data

In order to solve the problems that test data of the drug control institution is not standardized and low quality, the data exchange and information sharing are realized, the data value is mined and the information level is improved. Method: combined with the business of control institution and information practice, refer to the practice of relevant standard development; carry out the research on standard development work from the content, principle and process. Result and conclusion: this research completes development of the local standard of Guangdong drug test data, which can provide reference for the development of similar standards in future

Similarity-Aware Multimodal Prompt Learning for Fake News Detection

The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and it requires expert knowledge to use textual information to debunk fake news. Recently, studies focusing on multimodal fake news detection have outperformed text-only methods. Recent approaches utilizing the pre-trained model to extract unimodal features, or fine-tuning the pre-trained model directly, have become a new paradigm for detecting fake news. Again, this paradigm either requires a large number of training instances, or updates the entire set of pre-trained model parameters, making real-world fake news detection impractical. Furthermore, traditional multimodal methods fuse the cross-modal features directly without considering that the uncorrelated semantic representation might inject noise into the multimodal features. This paper proposes a Similarity-Aware Multimodal Prompt Learning (SAMPLE) framework. First, we incorporate prompt learning into multimodal fake news detection. Prompt learning, which only tunes prompts with a frozen language model, can reduce memory usage significantly and achieve comparable performances, compared with fine-tuning. We analyse three prompt templates with a soft verbalizer to detect fake news. In addition, we introduce the similarity-aware fusing method to adaptively fuse the intensity of multimodal representation and mitigate the noise injection via uncorrelated cross-modal features. For evaluation, SAMPLE surpasses the F1 and the accuracies of previous works on two benchmark multimodal datasets, demonstrating the effectiveness of the proposed method in detecting fake news. In addition, SAMPLE also is superior to other approaches regardless of few-shot and data-rich settings

Map-based Channel Modeling and Generation for U2V mmWave Communication

Unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) technologies have a promising prospect in the future communication networks. By considering the factors of three-dimensional (3D) scattering space, 3D trajectory, and 3D antenna array, a non-stationary channel model for UAV-to-vehicle (U2V) mmWave communications is proposed. The computation and generation methods of channel parameters including interpath and intra-path are analyzed in detail. The inter-path parameters are calculated in a deterministic way, while the parameters of intra-path rays are generated in a stochastic way. The statistical properties are obtained by using a Gaussian mixture model (GMM) on the massive ray tracing (RT) data. Then, a modified method of equal areas (MMEA) is developed to generate the random intra-path variables. Meanwhile, to reduce the complexity of RT method, the 3D propagation space is reconstructed based on the user-defined digital map. The simulated and analyzed results show that the proposed model and generation method can reproduce non-stationary U2V channels in accord with U2V scenarios. The generated statistical properties are consistent with the theoretical and measured ones as well

An All-Solid-State Phosphate Electrode with H3PO4 Doped Polyaniline as the Sensitive Layer

We here describe the construction of a highly sensitive and selective all-solid-state phosphate electrode based on polyaniline and H3PO4 doped polyaniline. The polyaniline layer was electroplated on the gold substrate with Chronoamperometry method and was in-situ doped by H3PO4. The Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy (SEM, EDS) and contact angle measurement was taken to explain the difference of the two layers. This electrode can be used in both freshwater and seawater systems. In both of the two systems, the electrode exhibits linear response in the concentration range 10-1 to 10-6 M with detection limit of 10-6 M. and response time of <1 seconds. The selectivity of the electrodes was also studied in 10-1-10-5 M KH2PO4 solutions containing either 0.01 M sulfate, nitrate, chloride as the interference ions. During 12 hours continuous monitoring in 10-3 M KH2PO4 with 3.5% NaCl the potential drift was 0.05 mV/h and the lifetime of the electrode was over 40 days when preserved in this solutionpublishersversionPeer reviewe

RNA polymerase II-mediated transcription at active loci does not require histone H3S10 phosphorylation in Drosophila

JIL-1 is the major kinase controlling the phosphorylation state of histone H3S10 at interphase in Drosophila. In this study, we used three different commercially available histone H3S10 phosphorylation antibodies, as well as an acid-free polytene chromosome squash protocol that preserves the antigenicity of the histone H3S10 phospho-epitope, to examine the role of histone H3S10 phosphorylation in transcription under both heat shock and non-heat shock conditions. We show that there is no redistribution or upregulation of JIL-1 or histone H3S10 phosphorylation at transcriptionally active puffs in such polytene squash preparations after heat shock treatment. Furthermore, we provide evidence that heat shock-induced puffs in JIL-1 null mutant backgrounds are strongly labeled by antibody to the elongating form of RNA polymerase II (Pol IIoser2), indicating that Pol IIoser2 is actively involved in heat shock-induced transcription in the absence of histone H3S10 phosphorylation. This is supported by the finding that there is no change in the levels of Pol IIoser2 in JIL-1 null mutant backgrounds compared with wild type. mRNA from the six genes that encode the major heat shock protein in Drosophila, Hsp70, is transcribed at robust levels in JIL-1 null mutants, as directly demonstrated by qRT-PCR. Taken together, these data are inconsistent with the model that Pol II-dependent transcription at active loci requires JIL-1-mediated histone H3S10 phosphorylation, and instead support a model in which transcriptional defects in the absence of histone H3S10 phosphorylation are a result of structural alterations of chromatin

An Energy-Aware Routing Protocol in Wireless Sensor Networks

The most important issue that must be solved in designing a data gathering algorithm for wireless sensor networks (WSNS) is how to save sensor node energy while meeting the needs of applications/users. In this paper, we propose a novel energy-aware routing protocol (EAP) for a long-lived sensor network. EAP achieves a good performance in terms of lifetime by minimizing energy consumption for in-network communications and balancing the energy load among all the nodes. EAP introduces a new clustering parameter for cluster head election, which can better handle the heterogeneous energy capacities. Furthermore, it also introduces a simple but efficient approach, namely, intra-cluster coverage to cope with the area coverage problem. We use a simple temperature sensing application to evaluate the performance of EAP and results show that our protocol significantly outperforms LEACH and HEED in terms of network lifetime and the amount of data gathered

Pre-Training on Large-Scale Generated Docking Conformations with HelixDock to Unlock the Potential of Protein-ligand Structure Prediction Models

Protein-ligand structure prediction is an essential task in drug discovery, predicting the binding interactions between small molecules (ligands) and target proteins (receptors). Although conventional physics-based docking tools are widely utilized, their accuracy is compromised by limited conformational sampling and imprecise scoring functions. Recent advances have incorporated deep learning techniques to improve the accuracy of structure prediction. Nevertheless, the experimental validation of docking conformations remains costly, it raises concerns regarding the generalizability of these deep learning-based methods due to the limited training data. In this work, we show that by pre-training a geometry-aware SE(3)-Equivariant neural network on a large-scale docking conformation generated by traditional physics-based docking tools and then fine-tuning with a limited set of experimentally validated receptor-ligand complexes, we can achieve outstanding performance. This process involved the generation of 100 million docking conformations, consuming roughly 1 million CPU core days. The proposed model, HelixDock, aims to acquire the physical knowledge encapsulated by the physics-based docking tools during the pre-training phase. HelixDock has been benchmarked against both physics-based and deep learning-based baselines, showing that it outperforms its closest competitor by over 40% for RMSD. HelixDock also exhibits enhanced performance on a dataset that poses a greater challenge, thereby highlighting its robustness. Moreover, our investigation reveals the scaling laws governing pre-trained structure prediction models, indicating a consistent enhancement in performance with increases in model parameters and pre-training data. This study illuminates the strategic advantage of leveraging a vast and varied repository of generated data to advance the frontiers of AI-driven drug discovery