Cytotoxic Effect of the Essential oils from Erigeron Canadensis L. on Human Cervical Cancer HeLa Cells in Vitro.

Erigeron Canadensis L. (E. canadensis) is a widely distributed invasive weed species in China. Potentially anti-cancer qualities may exist in its essential oils (EOs). The purpose of this study was to analyze the components of the EOs of E. canadensis and their effects on the normal liver cell lines L02 and the human cervical cancer cell lines HeLa. The EOs from the upper region of E. canadensis were prepared, its components were identified by GC/MS. Cell viability, cell morphology observation, AO/EB dual fluorescence staining assay, flow cytometry, mitochondrial membrane potential, western blot, caspase inhibitor test, and oxidative stress tests were used to investigate the impact of the EOs on HeLa cells. Network pharmacological analysis was employed to study the potential mechanism of the EOs in the treatment of cervical cancer. According to the findings, the EOs had 21 chemical components, of which limonene made up 65.68 %. After being exposed to the EOs, the cell viability of HeLa and L02 dramatically declined. The inhibition of EOs was more effective than that of limonene when used in an amount equivalent to that in the EOs. L02 cells were less susceptible to the cytotoxicity of EOs than HeLa cells were. Furthermore, EOs altered the cell cycle in HeLa cells and caused oxidative stress and apoptosis. Compared with the control group, the reactive oxygen species (ROS) levels increased in HeLa cells at first and then decreased, total superoxide dismutase (SOD) and catalase (CAT) activities in HeLa cells significantly decreased. G1 phase cells decreased whereas G2/M phase cells increased. The rate of apoptosis rose. Reduced mitochondrial membrane potential and Caspase-3, -9, and -12 protein expression were both observed. Nerolidol, dextroparaffinone, and α-pinene were shown to be the primary components for the suppression of HeLa cells, according to the results of the prediction of pharmacologic targets. In conclusion, findings of this study indicated the EOs may have the potential to curb the growth of cervical cancer cells. Further research is needed to explore the in vivo effect of EOs.

Neuralized E3 Ubiquitin Protein Ligase 3 Is an Inducible Antiviral Effector That Inhibits Hepatitis C Virus Assembly by Targeting Viral E1 Glycoprotein.

Hepatitis C virus (HCV) infection is a major cause of chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma. HCV can be sensed by host innate immunity to induce expression of interferons (IFNs) and a number of antiviral effectors. In this study, we found HCV infection induced the expression of neuralized E3 ubiquitin protein ligase 3 (NEURL3), a putative E3 ligase, in a manner that requires the involvement of innate immune sensing but is independent of the IFN action. Furthermore, we showed that NEURL3 inhibited HCV infection while it had little effect on other RNA viruses, including Zika virus (ZIKV), dengue virus (DENV), and vesicular stomatitis virus (VSV). Mechanistic studies demonstrated that NEURL3 inhibited HCV assembly by directly binding HCV envelope glycoprotein E1 to interfere with the E1/E2 heterodimerization, an important prerequisite for virion morphogenesis. Finally, we showed that knockout of NEURL3 significantly enhanced HCV infection. In summary, we identified NEURL3 as a novel inducible antiviral host factor that suppresses HCV assembly. Our results not only shed new insight into how host innate immunity acts against HCV but also revealed a new important biological function for NEURL3.IMPORTANCE The exact biological function of NEURL3, a putative E3 ligase, remains largely unknown. In this study, we found that NEURL3 could be upregulated upon HCV infection in a manner dependent on pattern recognition receptor-mediated innate immune response. NEURL3 inhibits HCV assembly by directly binding viral E1 envelope glycoprotein to disrupt its interaction with E2, an action that requires its Neuralized homology repeat (NHR) domain but not the RING domain. Furthermore, we found that NEURL3 has a pangenotypic anti-HCV activity and interacts with E1 of genotypes 2a, 1b, 3a, and 6a but does not inhibit other closely related RNA viruses, such as ZIKV, DENV, and VSV. To our knowledge, our study is the first report to demonstrate that NEURL3 functions as an antiviral host factor. Our results not only shed new insight into how host innate immunity acts against HCV, but also revealed a new important biological function for NEURL3.

Laboratory of genetics and physiology 2 (LGP2) plays an essential role in hepatitis C virus infection-induced interferon responses.

Retinoic acid-inducible gene I (RIG-I)-like receptors are cytosolic pattern recognition receptors (PRRs) that detect non-self-RNA and activate downstream interferon (IFN) signaling. One of the RIG-I-like receptors, laboratory of genetics and physiology 2 (LGP2), was originally thought to be a negative feedback regulator in the RIG-I signaling pathway, but growing evidence indicates that LGP2 is one cofactor of melanoma differentiation-associated protein 5 (MDA5) in MDA5-mediated IFN signaling activation. Our previous work showed that MDA5 was the major PRR to sense hepatitis C virus (HCV) infection in hepatocytes, but the role of LGP2 in HCV infection-induced IFN signaling has not been elucidated. In this study, we reported that LGP2 was a positive regulator of HCV infection-induced IFN signaling. Knockout of LGP2 in hepatocytes significantly diminished IFN production in response to HCV infection, but not to HCV 3'untranslated region RNA transfection. Mechanistic studies showed that LGP2 exerted its function at a step upstream of MDA5 in the IFN signaling. HCV infection promoted the molecular interaction between LGP2 and MDA5, which, in turn, enhanced MDA5/HCV RNA association. Finally, we demonstrated that the ATPase activity of LGP2 was critical for assisting MDA5/HCV RNA interaction and activating IFN signaling during HCV infection. CONCLUSION: Our work demonstrated that LGP2 plays an essential role in activating IFN signaling against HCV infection by promoting MDA5 recognition of HCV pathogen-associated molecular patterns. (Hepatology 2017;65:1478-1491).

Contribution of kv7.4/kv7.5 heteromers to intrinsic and calcitonin gene-related peptide-induced cerebral reactivity.

OBJECTIVE: Middle cerebral artery (MCA) diameter is regulated by inherent myogenic activity and the effect of potent vasodilators such as calcitonin gene-related peptide (CGRP). Previous studies showed that MCAs express KCNQ1, 4, and 5 potassium channel genes, and the expression products (Kv7 channels) participate in the myogenic control of MCA diameter. The present study investigated the contribution of Kv7.4 and Kv7.5 isoforms to myogenic and CGRP regulation of MCA diameter and determined whether they were affected in hypertensive animals. APPROACH AND RESULTS: Isometric tension recordings performed on MCA from normotensive rats produced CGRP vasodilations that were inhibited by the pan-Kv7 channel blocker linopirdine (P<0.01) and after transfection of arteries with siRNA against KCNQ4 (P<0.01) but not KCNQ5. However, isobaric myography revealed that myogenic constriction in response to increases in intravascular pressure (20-80 mm Hg) was affected by both KCNQ4 and KCNQ5 siRNA. Proximity ligation assay signals were equally abundant for Kv7.4/Kv7.4 or Kv7.4/Kv7.5 antibody combinations but minimal for Kv7.5/Kv7.5 antibodies or Kv7.4/7.1 combinations. In contrast to systemic arteries, Kv7 function and Kv7.4 abundance in MCA were not altered in hypertensive rats. CONCLUSIONS: This study reveals, for the first time to our knowledge, that in cerebral arteries, Kv7.4 and Kv7.5 proteins exist predominantly as a functional heterotetramer, which regulates intrinsic myogenicity and vasodilation attributed to CGRP. Surprisingly, unlike systemic arteries, Kv7 activity in MCAs is not affected by the development of hypertension, and CGRP-mediated vasodilation is well maintained. As such, cerebrovascular Kv7 channels could be amenable for therapeutic targeting in conditions such as cerebral vasospasm.

Object detection in optical imaging of the Internet of Things based on deep learning.

This article endeavors to enhance image recognition technology within the context of the Internet of Things (IoT). A dynamic image target detection training model is established through the convolutional neural network (CNN) algorithm within the framework of deep learning (DL). Three distinct model configurations are proposed: a nine-layer convolution model, a seven-layer convolution model, and a residual module convolution model. Subsequently, the simulation model of CNN image target detection based on optical imaging is constructed, and the simulation experiments are conducted in scenarios of simple and salient environments, complex and salient environments, and intricate micro-environment. By determining the optimal training iterations, comparisons are drawn in terms of precision, accuracy, Intersection Over Union (IoU), and frames per second (FPS) among different model configurations. Finally, an attention mechanism is incorporated within the DL framework, leading to the construction of an attention mechanism CNN target detection model that operates at three difficulty levels: simple, intermediate, and challenging. Through comparative analysis against prevalent target detection algorithms, this article delves into the accuracy and detection efficiency of various models for IoT target detection. Key findings include: (1) The seven-layer CNN model exhibits commendable accuracy and confidence in simple and salient environments, although it encounters certain instances of undetected images, indicating scope for improvement. (2) The residual network model, when employing a loss function comprising both mean square error (MSE) and cross entropy, demonstrates superior performance in complex and salient environments, manifesting high precision, IoU, and accuracy metrics, thereby establishing itself as a robust detection model. (3) Within intricate micro-environments, the residual CNN model, utilizing loss functions of MSE and cross entropy, yields substantial results, with precision, IoU, and FPS values amounting to 0.99, 0.83, and 29.9, respectively. (4) The CNN model enriched with an attention mechanism outperforms other models in IoT target image detection, achieving the highest accuracy rates of 24.86%, 17.8%, and 14.77% in the simple, intermediate, and challenging levels, respectively. Although this model entails slightly longer detection times, its overall detection performance is excellent, augmenting the effectiveness of object detection within IoT. This article strives to enhance image target detection accuracy and speed, bolster the recognition capability of IoT systems, and refine dynamic image target detection within IoT settings. The implications encompass reduced manual recognition costs and the provision of a theoretical foundation for optimizing imaging and image target detection technologies in the IoT context.