Identification of two major QTLs for pod shell thickness in peanut (Arachis hypogaea L.) using BSA-seq analysis.

BACKGROUND: Pod shell thickness (PST) is an important agronomic trait of peanut because it affects the ability of shells to resist pest infestations and pathogen attacks, while also influencing the peanut shelling process. However, very few studies have explored the genetic basis of PST. RESULTS: An F2 segregating population derived from a cross between the thick-shelled cultivar Yueyou 18 (YY18) and the thin-shelled cultivar Weihua 8 (WH8) was used to identify the quantitative trait loci (QTLs) for PST. On the basis of a bulked segregant analysis sequencing (BSA-seq), four QTLs were preliminarily mapped to chromosomes 3, 8, 13, and 18. Using the genome resequencing data of YY18 and WH8, 22 kompetitive allele-specific PCR (KASP) markers were designed for the genotyping of the F2 population. Two major QTLs (qPSTA08 and qPSTA18) were identified and finely mapped, with qPSTA08 detected on chromosome 8 (0.69-Mb physical genomic region) and qPSTA18 detected on chromosome 18 (0.15-Mb physical genomic region). Moreover, qPSTA08 and qPSTA18 explained 31.1-32.3% and 16.7-16.8% of the phenotypic variation, respectively. Fifteen genes were detected in the two candidate regions, including three genes with nonsynonymous mutations in the exon region. Two molecular markers (Tif2_A08_31713024 and Tif2_A18_7198124) that were developed for the two major QTL regions effectively distinguished between thick-shelled and thin-shelled materials. Subsequently, the two markers were validated in four F2:3 lines selected. CONCLUSIONS: The QTLs identified and molecular markers developed in this study may lay the foundation for breeding cultivars with a shell thickness suitable for mechanized peanut shelling.

Cytological and transcriptomic analysis to unveil the mechanism of web blotch resistance in Peanut.

BACKGROUND: Peanut is an important oil crop worldwide. Peanut web blotch is a fungal disease that often occurs at the same time as other leaf spot diseases, resulting in substantial leaf drop, which seriously affects the peanut yield and quality. However, the molecular mechanism underlying peanut resistance to web blotch is unknown. RESULTS: The cytological examination revealed no differences in the conidium germination rate between the web blotch-resistant variety ZH and the web blotch-susceptible variety PI at 12-48 hpi. The appressorium formation rate was significantly higher for PI than for ZH at 24 hpi. The papilla formation rate at 36 hpi and the hypersensitive response rate at 60 and 84 hpi were significantly higher for ZH than for PI. We also compared the transcriptional profiles of web blotch-infected ZH and PI plants at 0, 12, 24, 36, 48, 60, and 84 hpi using an RNA-seq technique. There were more differentially expressed genes (DEGs) in ZH and PI at 12, 36, 60, and 84 hpi than at 24 and 48 hpi. Moreover, there were more DEGs in PI than in ZH at each time-point. The analysis of metabolic pathways indicated that pantothenate and CoA biosynthesis; monobactam biosynthesis; cutin, suberine and wax biosynthesis; and ether lipid metabolism are specific to the active defense of ZH against YY187, whereas porphyrin metabolism as well as taurine and hypotaurine metabolism are pathways specifically involved in the passive defense of ZH against YY187. In the protein-protein interaction (PPI) network, most of the interacting proteins were serine acetyltransferases and cysteine synthases, which are involved in the cysteine synthesis pathway. The qRT-PCR data confirmed the reliability of the transcriptome analysis. CONCLUSION: On the basis of the PPI network for the significantly enriched genes in the pathways which were specifically enriched at different time points in ZH, we hypothesize that serine acetyltransferases and cysteine synthases are crucial for the cysteine-related resistance of peanut to web blotch. The study results provide reference material for future research on the mechanism mediating peanut web blotch resistance.

Macroscale Superlubricity of Hydrated Anions in the Boundary Lubrication Regime.

Cations can achieve excellent hydration lubrication at smooth interfaces under both microscale and macroscale conditions due to the boundary layer composed of hydration shells surrounding charges, but what about anions? Commonly used friction pairs are negatively charged at the solid/solution interface. Achieving anionic adsorption through constructing positively charged surfaces is a prerequisite for studying the hydration lubrication of anions. Here we report the hydration layer composed of anions adsorbed on the positively charged polymer/sapphire interface at acidic electrolyte solutions with pH below the isoelectric point, which contributes to the hydration lubrication of anions. Strongly hydrated anions (for the case of SO42-) exhibit stable superlubricity comparable to cations, with strikingly low boundary friction coefficient of 0.003-0.007 under contact pressures above 15 MPa without a running-in period. The hydration lubrication performance of anions is determined by both the ionic hydration strength and ion adsorption density based on the surface potential and tribological experiments. The results shed light on the role of anions in superlubricity and hydration lubrication, which may be relevant for understanding the lubrication mechanism and improving lubrication performance in acidic environments, for example, in acid pumps, sealing rings of compressors for handling acidic media, and processing devices of nuclear waste.

A new classification method for diagnosing COVID-19 pneumonia based on joint CNN features of chest X-ray images and parallel pyramid MLP-mixer module.

During the past three years, the coronavirus disease 2019 (COVID-19) has swept the world. The rapid and accurate recognition of covid-19 pneumonia are ,therefore, of great importance. To handle this problem, we propose a new pipeline of deep learning framework for diagnosing COVID-19 pneumonia via chest X-ray images from normal, COVID-19, and other pneumonia patients. In detail, the self-trained YOLO-v4 network was first used to locate and segment the thoracic region, and the output images were scaled to the same size. Subsequently, the pre-trained convolutional neural network was adopted to extract the features of X-ray images from 13 convolutional layers, which were fused with the original image to form a 14-dimensional image matrix. It was then put into three parallel pyramid multi-layer perceptron (MLP)-Mixer modules for comprehensive feature extraction through spatial fusion and channel fusion based on different scales so as to grasp more extensive feature correlation. Finally, by combining all image features from the 14-channel output, the classification task was achieved using two fully connected layers as well as Softmax classifier for classification. Extensive simulations based on a total of 4099 chest X-ray images were conducted to verify the effectiveness of the proposed method. Experimental results indicated that our proposed method can achieve the best performance in almost all cases, which is good for auxiliary diagnosis of COVID-19 and has great clinical application potential.

ExpGCN: Review-aware Graph Convolution Network for explainable recommendation.

Existing works in recommender system have widely explored extracting reviews as explanations beyond user-item interactions, and formulated the explanation generation as a ranking task to enhance item recommendation performance. To associate explanations with users and items, graph neural networks (GNN) are usually employed to learn node representations on the heterogeneous user-item-explanation interaction graph. However, modeling heterogeneous graph convolution poses limitations in both message passing styles and computational efficiency, resulting in sub-optimal recommendation performance. To address the limitations, we propose an Explanation-aware Graph Convolution Network (ExpGCN). In particular, the heterogeneous interaction graph is divided to subgraphs regard to the edge types in ExpGCN. By aggregating information from distinct subgraphs, ExpGCN is capable of generating node representations for explanation ranking task and item recommendation task respectively. Task-oriented graph convolution can not only reduce the complexity of heterogeneous node aggregation, but also alleviate the performance degeneration caused by the conflicts between task learning objectives, which has been neglected in current studies. Extensive experiments on four public datasets show that ExpGCN significantly outperforms state-of-the-art baselines with high efficiency, demonstrating the effectiveness of ExpGCN in explainable recommendations.

Person re-identification via semi-supervised adaptive graph embedding.

Video surveillance is an indispensable part of the smart city for public safety and security. Person Re-Identification (Re-ID), as one of elementary learning tasks for video surveillance, is to track and identify a given pedestrian in a multi-camera scene. In general, most existing methods has firstly adopted a CNN based detector to obtain the cropped pedestrian image, it then aims to learn a specific distance metric for retrieval. However, unlabeled gallery images are generally overlooked and not utilized in the training. On the other hands, Manifold Embedding (ME) has well been applied to Person Re-ID as it is good to characterize the geometry of database associated with the query data. However, ME has its limitation to be scalable to large-scale data due to the huge computational complexity for graph construction and ranking. To handle this problem, we in this paper propose a novel scalable manifold embedding approach for Person Re-ID task. The new method is to incorporate both graph weight construction and manifold regularized term in the same framework. The graph we developed is discriminative and doubly-stochastic so that the side information has been considered so that it can enhance the clustering performances. The doubly-stochastic property can also guarantee the graph is highly robust and less sensitive to the parameters. Meriting from such a graph, we then incorporate the graph construction, the subspace learning method in the unified loss term. Therefore, the subspace results can be utilized into the graph construction, and the updated graph can in turn incorporate discriminative information for graph embedding. Extensive simulations is conducted based on three benchmark Person Re-ID datasets and the results verify that the proposed method can achieve better ranking performance compared with other state-of-the-art graph-based methods.

Analysis of clinical characteristics of scrub typhus associated with hemophagocytic syndrome in 15 children / 中国热带医学

@#Abstract: Objective　To analyze the clinical features of hemophagocytic syndrome (HPS) associated with Orientia tsutsugamushi disease in children. Methods　The case data of patients with scrub typhus in Kunming Children's Hospital from January 1st 2019 to December 31st 2021 was retrospectively analyzed. The patients were divided into the HPS group and the non-HPS group according to whether associated with HPS. The clinical data of the two groups were analyzed using SPSS 25.0. Results　Eighty-five cases of scrub typhus in children were collected, 15 cases (17.6%) had HPS. The mean age of patients with HPS was (5.10±3.82) years, included 9 males and 6 female, there was no significant difference in gender and age between the HPS and the non-HPS group (P>0.05). Comparison of the two groups indicted that the incidence of cough, lung rales, edema, and hepatomegaly were significantly increased in the HPS group (P<0.05). The data showed that compared to the non-HPS group, the HPS group showed significant decreases in the levels of hemoglobin (HGB), platelet (PLT), albumin (ALB), fibrinogen (Fib) (P<0.05), and significant decreases in the levels of C-reactive protein (CRP), lactic dehydrogenase (LDH), triglyceride (TG), serum ferritin (SF) (P<0.05). The proportion of CD4+ T lymphocytes, CD4+/CD8+ were significantly decreased (P<0.05); the proportion of CD3+, CD8+ T lymphocytes were significantly increased (P<0.05). The proportion of pulmonary exudation or consolidation in the HPS group was higher than the non-HPS group, which was statistically significant (P<0.05). All the patients with scrub typhus associated with HPS were treated with oral doxycycline, and intravenous immunoglobulin was given in 13 cases (86.7%). There was one case of death and 14 cases discharged from hospital after treatment in HPS group. Conclusion　HPS in scrub typhus infected children is a nonnegligible complication. Prolonged fever, lung rales, hepatomegaly，HGB decreased, thrombocytopenia, hyperferritinemia, and abnormal lymphocyte subsets may associate with HPS. It should be alerted to scrub typhus when presenting with HPS in endemic areas. The scrub typhus associated with HPS can be successfully treated with appropriate antibiotic and immunomodulator treatment.

Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning.

Electrocardiogram classification is crucial for various applications such as the medical diagnosis of cardiovascular diseases, the level of heart damage, and stress. One of the typical challenges of electrocardiogram classification problems is the small size of the datasets, which may lead to limitation in the performance of the classification models, particularly for models based on deep-learning algorithms. Transfer learning has demonstrated effectiveness in transferring knowledge from a source model with a similar domain and can enhance the performance of the target model. Nevertheless, the consideration of datasets with similar domains restricts the selection of source domains. In this paper, electrocardiogram classification was enhanced by distant transfer learning where a generative-adversarial-network-based auxiliary domain with a domain-feature-classifier negative-transfer-avoidance (GANAD-DFCNTA) algorithm was proposed to bridge the knowledge transfer from distant sources to target domains. To evaluate the performance of the proposed algorithm, eight benchmark datasets were chosen, with four from electrocardiogram datasets and four from the following distant domains: ImageNet, COCO, WordNet, and Sentiment140. The results showed an average accuracy improvement of 3.67 to 4.89%. The proposed algorithm was also compared with existing works using traditional transfer learning, revealing an average accuracy improvement of 0.303-5.19%. Ablation studies confirmed the effectiveness of the components of GANAD-DFCNTA.

Bioactive abietane diterpenes and benzofuran neolignans from the resins of Toxicodendron vernicifluum.

Six new compounds (1-6), including two abietane diterpenes (1,2) and four benzofuran neolignans (3-6), along with five known compounds (7-11) were isolated and identified through phytochemical investigation on the resins of Toxicodendron vernicifluum (Toxicodendri Resina). The structures of the new compounds were fully elucidated by their 1D and 2D NMR, HRESIMS, UV, and IR spectroscopic data analyses. The absolute configurations of 1-4 were deduced by comparison of the experimental and calculated electronic circular dichroism (ECD) data. The inhibitory effects of the isolates on myocardial fibrosis induced by TGF-ß were examined, and compounds 1, 5, and 7-10 showed the anti-proliferation of myocardial fibroblasts at the concentrations of 10-40 µM in a dose-dependent manner.

Joint clothes image detection and search via anchor free framework.

Clothes image search is an important learning task in fashion analysis to find the most relevant clothes in a database given a user-provided query. To address this problem, most existing methods employ a two-step approach, i.e., first detect the target clothes, and then crop it to feed the model for similarity learning. But the two-step approach is time-consuming and resource-intensive. On the other hand, one-step methods provide efficient solutions to integrate clothes detection and search in a unified framework. However, since one-step methods usually explore anchor-based detectors, they inevitably inherit limitations, such as high computational complexity caused by dense anchors, and high sensitivity to hyperparameters. To address the aforementioned issues, we propose an anchor-free framework for joint clothes detection and search. Specifically, we first choose an anchor-free detector as backbone. We then add a mask prediction branch and a Re-ID embedding branch to the framework. The mask prediction branch aims to predict the masks of clothes, while Re-ID embedding branch aims to extract the rich embedding features of clothes, in which we aggregate the feature of clothes via a mask pooling module by referencing the estimated target clothes masks. In this way, the extracted target clothes features can grasp more information in the area of the clothes mask; finally, we further introduce a match loss to fine-tune the embedding feature in Re-ID branch for improving the retrieval performance. Simulation results based on real datasets demonstrate the effectiveness of the proposed work.

Cascaded Triplanar Autoencoder M-Net for Fully Automatic Segmentation of Left Ventricle Myocardial Scar From Three-Dimensional Late Gadolinium-Enhanced MR Images.

While three-dimensional (3D) late gadolinium-enhanced (LGE) magnetic resonance (MR) imaging provides good conspicuity of small myocardial lesions with short acquisition time, it poses a challenge for image analysis as a large number of axial images are required to be segmented. We developed a fully automatic convolutional neural network (CNN) called cascaded triplanar autoencoder M-Net (CTAEM-Net) to segment myocardial scar from 3D LGE MRI. Two sub-networks were cascaded to segment the left ventricle (LV) myocardium and then the scar within the pre-segmented LV myocardium. Each sub-network contains three autoencoder M-Nets (AEM-Nets) segmenting the axial, sagittal and coronal slices of the 3D LGE MR image, with the final segmentation determined by voting. The AEM-Net integrates three features: (1) multi-scale inputs, (2) deep supervision and (3) multi-tasking. The multi-scale inputs allow consideration of the global and local features in segmentation. Deep supervision provides direct supervision to deeper layers and facilitates CNN convergence. Multi-task learning reduces segmentation overfitting by acquiring additional information from autoencoder reconstruction, a task closely related to segmentation. The framework provides an accuracy of 86.43% and 90.18% for LV myocardium and scar segmentation, respectively, which are the highest among existing methods to our knowledge. The time required for CTAEM-Net to segment LV myocardium and the scar was 49.72 ± 9.69s and 120.25 ± 23.18s per MR volume, respectively. The accuracy and efficiency afforded by CTAEM-Net will make possible future large population studies. The generalizability of the framework was also demonstrated by its competitive performance in two publicly available datasets of different imaging modalities.

Three new coumarins and a new coumarin glycoside from Micromelum integerrimum.

Three new coumarins, integmarins A-C (1-3), and a new coumarin glycoside, integmaside A (4) were isolated from the leaves and stems of Micromelum integerrimum. Their structures were elucidated on the basis of 1D and 2D NMR and MS data, and their absolute configurations were assigned according to the ECD data of the in situ formed transition metal complexes and comparison of experimental and calculated ECD data. Compounds 1 and 2 are two rare coumarins with butyl and propyl moieties at the C-6 position; compound 3 is a novel coumarin with a highly oxidized prenyl group, and compound 4 is a rare bisdihydrofuranocoumarin glycoside.

Evaluation of the effects of notoginseng total saponins (NS), safflower total flavonoids (SF), and the combination of NS and SF (CNS) on the activities of cytochrome P450 enzymes using a cocktail method in rats.

Notoginseng total saponins (NS), safflower total flavonoids (SF), and the combination of NS and SF, namely CNS, are used for the treatment of cardiovascular diseases in clinic. This study developed a cocktail assay involving seven cytochrome P450 (CYP) enzymes to elucidate the effect of NS, SF, and CNS on CYP enzymes and to explore the synergistic effect of CNS in terms of CYP enzymes. Ultra-performance liquid chromatography-MS and reverse-transcription polymerase chain reaction were applied to detect the activities and mRNA expression levels of CYP enzymes. SF exhibited inhibitory effects on CYP1A2, 2B1, 2E1, and 2C11 and induction effects on CYP2C19 and 2D4. NS exhibited induction effects on CYP1A2, 2B1, 2E1, 2C11, 2C19, and 2D4. CNS exhibited induction effects on CYP1A2, 2B1, 2E1, 2C19, and 2D4 and inhibitory effects on CYP3A1 in vivo. Moreover, mRNA expression results were consistent with pharmacokinetic results. Potential herb-drug interactions should be studied closely when SF, NS, or CNS with clinical drugs are metabolized by CYP1A2, 2B1, 2E1, 2C11, 2C19, 2D4, and 3A1. CNS could change the inhibition or induction effects of CYP compared to the NS group, which might be one of the causes for the synergistic effects of the combination of NS and SF.

Rapid identification, isolation, and evaluation on anti-neuroinflammatory activity of limonoids derivatives from the root bark of Dictamnus dasycarpus.

A total of 49 limonoids derivatives were rapidly identified by UNIFI software and three new limonoids derivatives, named dasycarinone (1, DAS), isodictamdiol C (2) and dasycarinone A (3), along with nineteen known compounds, were isolated from the root bark of Dictamnus dasycarpus, named as "Baixianpi" in Chinese. Their structures were elucidated on the basis of spectroscopic data (UV, IR, HR-ESI-MS, NMR, CD spectra and OR). All the compounds were tested for anti-inflammatory activities by suppressing the nitric oxide (NO) production in lipopolysaccharide (LPS) induced BV-2 cells. DAS exhibited a strong anti-inflammatory activity with IC50 value of 1.8 µM. Nuclear Factor kappa B (NF-κB) luciferase assay and enzyme-linked immune sorbent assay indicated that DAS can suppress the release of inflammatory cytokines such as Tumor Necrosis Factor α (TNF-α), interleukin 6 (IL-6) via inactivating NF-κB signaling pathways. Moreover, we found that anti-inflammatory activities of obacunone-class are better than those of limonin-class by analyzing structure-activity relationship. Our results suggested that obacunone derivatives play an important role on anti-inflammation of Baixianpi. As a representative among them, DAS showed a strong anti-inflammatory activity via suppressing NF-κB signaling pathways.

A new prenylated coumarin diglycoside with insulin-release promoting activity from Clausena dunniana.

A new prenylated coumarin diglycoside, 6-prenylcoumarin-7-O-ß-D-apiofuranosyl-(1→6)-ß-D-glucopyranoside (1) and five known flavonoid glycosides (2-6) were isolated from the leaves and stems of Clausena dunniana. The structures of these isolates were elucidated based on comprehensive MS, UV, IR, and NMR spectroscopic data analysis and comparison with the data reported in literature. Compounds 2-6 are obtained from the title plant for the first time. All these isolates were evaluated for their insulin-release promoting effects, and compounds 1, 2, and 4 exhibited significant activities (2.0 to 3.3-fold higher in comparison with the control, p < 0.01) at 40 µM.[Formula: see text].

Isolation and structure characterization of cytotoxic alkaloids from Micromelum integerrimum.

Ten undescribed alkaloids, named integerrines A-J, including one racemic heterodimer of carbazole and indole, two racemic, two scalemic, and one enantiomerically enriched biscarbazoles, two aldoximes, and one racemic pyrrolone, were isolated from the dried leaves and stems of Micromelum integerrimum. The racemic or scalemic compounds were resolved using chiral-phase HPLC and their configurations were determined by comparison of experimental and calculated ECD data. Four compounds exhibited moderate to weak cytotoxicities against HepG2, HTC-116, HeLa, and PANC-1 cell lines, with IC50 values of 14.1-67.5 µM.

Spatiotemporal Tree Filtering for Enhancing Image Change Detection.

Change detection has received extensive attention because of its realistic significance and broad application fields. However, none of the existing change detection algorithms can handle all scenarios and tasks so far. Different from the most of contributions from the research community in recent years, this paper does not work on designing new change detection algorithms. We, instead, solve the problem from another perspective by enhancing the raw detection results after change detection. As a result, the proposed method is applicable to various kinds of change detection methods, and regardless of how the results are detected. In this paper, we propose Fast Spatiotemporal Tree Filter (FSTF), a purely unsupervised detection method, to enhance coarse binary detection masks obtained by different kinds of change detection methods. In detail, the proposed FSTF has adopted a volumetric structure to effectively synthesize spatiotemporal information of the same target from the current time and history frames to enhance detection. The computational complexity analyzed in the view of graph theory also show that the fast realization of FSTF is a linear time algorithm, which is capable of handling efficient on-line detection tasks. Finally, comprehensive experiments based on qualitative and quantitative analysis verify that FSTF-based change detection enhancement is superior to several other state-of-the-art methods including fully connected Conditional Random Field (CRF), joint bilateral filter, and guided filter. It is illustrated that FSTF is versatile enough to also improve saliency detection as well as semantic image segmentation.

Coumarin derivatives from the leaves and twigs of Murraya exotica L. and their anti-inflammatory activities.

Fifteen previously undescribed coumarin derivatives, murratins A-M, including two pairs of coumarin enantiomers with a cyclopropane unit, two benzocoumarins, a dimeric coumarin, and eight C-8-substituted coumarins, together with six known analogues were isolated from the extract of the leaves and twigs of Murraya exotica L., a medicinal plant named "Jiulixiang" in Chinese. Their structures were elucidated on the basis of comprehensive analysis of 1D and 2D NMR and HRMS spectroscopic data, and the absolute configurations were assigned via comparison of the specific rotations, the ECD exciton coupling method, comparison of experimental and calculated ECD data, and the ECD data of in situ formed transition metal complexes. All the isolates were evaluated for their inhibitory effects on lipopolysaccharide-induced nitric oxide production in RAW 264.7 cells, and five coumarin derivatives showed moderate inhibitory activities. The possible mechanism for NO inhibition of undescribed bioactive compounds was deduced to interact with iNOS protein via molecular docking. The above results shed some light on a better understanding of the traditional anti-inflammation effect of M. exotica and coumarins are disclosed to be its potential anti-inflammatory constituents.

Large Volume Direct Injection Ultra-High Performance Liquid Chromatography-Tandem Mass Spectrometry-Based Comparative Pharmacokinetic Study between Single and Combinatory Uses of Carthamus tinctorius Extract and Notoginseng Total Saponins.

The combination of Carthamus tinctorius extract (CTE) and notoginseng total saponins (NGTS), namely, CNP, presents a synergistic effect on myocardial ischemia protection. Herein, comparative pharmacokinetic studies between CNP and CTE/NGTS were conducted to clarify their synergistic mechanisms. A large volume direct injection ultra-high performance liquid chromatography-tandem mass spectrometry (LVDI-UHPLC-MS/MS) platform was developed for sensitively assaying the multi-component pharmacokinetic and in vitro cocktail assay of cytochrome p450 (CYP450) before and after compatibility of CTE and NGTS. The pharmacokinetic profiles of six predominantly efficacious components of CNP, including hydroxysafflor yellow A (HSYA); ginsenosides Rg1 (GRg1), Re (GRe), Rb1 (GRb1), and Rd (GRd); and notoginsenoside R1 (NGR1), were obtained, and the results disclosed that CNP could increase the exposure levels of HSYA, GRg1, GRe, GRb1, and NGR1 at varying degrees. The in vitro cocktail assay demonstrated that CNP exhibited more potent inhibition on CYP1A2 than CTE and NGTS, and GRg1, GRb1, GRd, quercetin, kaempferol, and 6-hydroxykaempferol were found to be the major inhibitory compounds. The developed pharmacokinetic interaction-based strategy provides a viable orientation for the compatibility investigation of herb medicines.

Cucurbitacin E Inhibits Huh7 Hepatoma Carcinoma Cell Proliferation and Metastasis via Suppressing MAPKs and JAK/STAT3 Pathways.

Cucurbitacin E (CuE), a highly oxygenated tetracyclic triterpene from Cucurbitaceae, has shown to exhibit potent cytotoxic and anti-proliferative properties against several human cancer cells. However, the underlying effects and mechanisms of CuE regarding hepatocellular carcinoma (HCC) have not been well understood. In the current study, unbiased RNA-sequencing (RNA-seq) and bioinformatics analysis was applied to elucidate the underlying molecular mechanism. CuE could significantly inhibit cell proliferation and migration of Huh7 cells, meanwhile CuE exhibited potent anti-angiogenic activity. RNA-seq analysis revealed that CuE negatively regulated 241 differentially expressed genes (DEGs) involved in multiple processes including cytoskeleton formation, angiogenesis and focal adhesion. Further analysis revealed that CuE effectually regulated diversified pharmacological signaling pathways such as MAPKs and JAK-STAT3. Our findings demonstrated the role of CuE in inhibiting proliferation and migration, providing an insight into the regulation of multiple signaling pathways as a new paradigm for anti-cancer treatment strategy.