Modeling neurodevelopmental disorder-associated human AGO1 mutations in Caenorhabditis elegans Argonaute alg-1.

MicroRNAs (miRNA) associate with Argonaute (AGO) proteins and repress gene expression by base pairing to sequences in the 3' untranslated regions of target genes. De novo coding variants in the human AGO genes AGO1 and AGO2 cause neurodevelopmental disorders (NDD) with intellectual disability, referred to as Argonaute syndromes. Most of the altered amino acids are conserved between the miRNA-associated AGO in Homo sapiens and Caenorhabditis elegans, suggesting that the human mutations could disrupt conserved functions in miRNA biogenesis or activity. We genetically modeled four human AGO1 mutations in C. elegans by introducing identical mutations into the C. elegans AGO1 homologous gene, alg-1. These alg-1 NDD mutations cause phenotypes in C. elegans indicative of disrupted miRNA processing, miRISC (miRNA silencing complex) formation, and/or target repression. We show that the alg-1 NDD mutations are antimorphic, causing developmental and molecular phenotypes stronger than those of alg-1 null mutants, likely by sequestrating functional miRISC components into non-functional complexes. The alg-1 NDD mutations cause allele-specific disruptions in mature miRNA profiles, accompanied by perturbation of downstream gene expression, including altered translational efficiency and/or messenger RNA abundance. The perturbed genes include those with human orthologs whose dysfunction is associated with NDD. These cross-clade genetic studies illuminate fundamental AGO functions and provide insights into the conservation of miRNA-mediated post-transcriptional regulatory mechanisms.

A deep learning approach to remove contrast from contrast-enhanced CT for proton dose calculation.

PURPOSE: Non-Contrast Enhanced CT (NCECT) is normally required for proton dose calculation while Contrast Enhanced CT (CECT) is often scanned for tumor and organ delineation. Possible tissue motion between these two CTs raises dosimetry uncertainties, especially for moving tumors in the thorax and abdomen. Here we report a deep-learning approach to generate NCECT directly from CECT. This method could be useful to avoid the NCECT scan, reduce CT simulation time and imaging dose, and decrease the uncertainties caused by tissue motion between otherwise two different CT scans. METHODS: A deep network was developed to convert CECT to NCECT. The network receives a 3D image from CECT images as input and generates a corresponding contrast-removed NCECT image patch. Abdominal CECT and NCECT image pairs of 20 patients were deformably registered and 8000 image patch pairs extracted from the registered image pairs were utilized to train and test the model. CTs of clinical proton patients and their treatment plans were employed to evaluate the dosimetric impact of using the generated NCECT for proton dose calculation. RESULTS: Our approach achieved a Cosine Similarity score of 0.988 and an MSE value of 0.002. A quantitative comparison of clinical proton dose plans computed on the CECT and the generated NCECT for five proton patients revealed significant dose differences at the distal of beam paths. V100% of PTV and GTV changed by 3.5% and 5.5%, respectively. The mean HU difference for all five patients between the generated and the scanned NCECTs was ∼4.72, whereas the difference between CECT and the scanned NCECT was ∼64.52, indicating a ∼93% reduction in mean HU difference. CONCLUSIONS: A deep learning approach was developed to generate NCECTs from CECTs. This approach could be useful for the proton dose calculation to reduce uncertainties caused by tissue motion between CECT and NCECT.

Modeling neurodevelopmental disorder-associated hAGO1 mutations in C. elegans Argonaute ALG-1.

MicroRNAs (miRNA) are endogenous non-coding RNAs important for post-transcriptional regulation of gene expression. miRNAs associate with Argonaute proteins to bind to the 3' UTR of target genes and confer target repression. Recently, multiple de novo coding variants in the human Argonaute gene AGO1 ( hAGO1 ) have been reported to cause a neurodevelopmental disorder (NDD) with intellectual disability (ID). Most of the altered amino acids are conserved between the miRNA-associated Argonautes in H. sapiens and C. elegans , suggesting the hAGO1 mutations could disrupt evolutionarily conserved functions in the miRNA pathway. To investigate how the hAGO1 mutations may affect miRNA biogenesis and/or functions, we genetically modeled four of the hAGO1 de novo variants (referred to as NDD mutations) by introducing the identical mutations to the C. elegans hAGO1 homolog, alg-1 . This array of mutations caused distinct effects on C. elegans miRNA functions, miRNA populations, and downstream gene expression, indicative of profound alterations in aspects of miRNA processing and miRISC formation and/or activity. Specifically, we found that the alg-1 NDD mutations cause allele-specific disruptions in mature miRNA profiles both in terms of overall abundances and association with mutant ALG-1. We also observed allele-specific profiles of gene expression with altered translational efficiency and/or mRNA abundance. The sets of perturbed genes include human homologs whose dysfunction is known to cause NDD. We anticipate that these cross-clade genetic studies may advance the understanding of fundamental Argonaute functions and provide insights into the conservation of miRNA-mediated post-transcriptional regulatory mechanisms.

Contour interpolation by deep learning approach.

Purpose: Contour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in the gaps, therefore saving time and efforts. The most used conventional shape-based interpolation (SBI) algorithm, which operates on shape information, often performs suboptimally near the superior and inferior borders of organs and for the gastrointestinal structures. In this study, we present a generic deep learning solution to improve the robustness and accuracy for contour interpolation, especially for these historically difficult cases. Approach: A generic deep contour interpolation model was developed and trained using 16,796 publicly available cases from 5 different data libraries, covering 15 organs. The network inputs were a 128 × 128 × 5 image patch and the two-dimensional contour masks for the top and bottom slices of the patch. The outputs were the organ masks for the three middle slices. The performance was evaluated on both dice scores and distance-to-agreement (DTA) values. Results: The deep contour interpolation model achieved a dice score of 0.95 ± 0.05 and a mean DTA value of 1.09 ± 2.30 mm , averaged on 3167 testing cases of all 15 organs. In a comparison, the results by the conventional SBI method were 0.94 ± 0.08 and 1.50 ± 3.63 mm , respectively. For the difficult cases, the dice score and DTA value were 0.91 ± 0.09 and 1.68 ± 2.28 mm by the deep interpolator, compared with 0.86 ± 0.13 and 3.43 ± 5.89 mm by SBI. The t-test results confirmed that the performance improvements were statistically significant ( p < 0.05 ) for all cases in dice scores and for small organs and difficult cases in DTA values. Ablation studies were also performed. Conclusions: A deep learning method was developed to enhance the process of contour interpolation. It could be useful for expediting the tasks of manual segmentation of organs and structures in the medical images.

Critical contribution of 3' non-seed base pairing to the in vivo function of the evolutionarily conserved let-7a microRNA.

Base pairing of the seed region (g2-g8) is essential for microRNA targeting; however, the in vivo function of the 3' non-seed region (g9-g22) is less well understood. Here, we report a systematic investigation of the in vivo roles of 3' non-seed nucleotides in microRNA let-7a, whose entire g9-g22 region is conserved among bilaterians. We find that the 3' non-seed sequence functionally distinguishes let-7a from its family paralogs. The complete pairing of g11-g16 is essential for let-7a to fully repress multiple key targets, including evolutionarily conserved lin-41, daf-12, and hbl-1. Nucleotides at g17-g22 are less critical but may compensate for mismatches in the g11-g16 region. Interestingly, a certain minimal complementarity to let-7a 3' non-seed sequence can be required even for sites with perfect seed pairing. These results provide evidence that the specific configurations of both seed and 3' non-seed base pairing can critically influence microRNA-mediated gene regulation in vivo.

Analgesic Effects of Epidural Labor Analgesia at Different Periods and Its Effects on Maternal and Infant Outcomes and MiRNA-146b Level.

This paper aimed to analyze the analgesic effects of continuous epidural labor analgesia (ELA) at different periods and its effects on postpartum depression, maternal and infant outcomes, and maternal blood pressure. Giving birth in our hospital from September 2017 to August 2019, 119 primiparas with spontaneous delivery were enrolled and divided into an observation group (65 cases) and a control group (54 cases). Patients in the observation group received epidural block analgesia in advance, whereas those in the control group received epidural block analgesia routinely. At 25 days after delivery, breast milk samples were collected, in which miRNA-146b level was detected by PCR. The patients were compared between the two groups with respect to progress of labor, analgesic effects during 3 stages of labor, labor outcomes, adverse reactions, and levels of NO, ANP, and ET-1 in the parturients' umbilical artery blood. Compared with those in the control group, patients in the observation group had a remarkably higher miRNA-146b level in the breast milk (P < 0.05), remarkably lower average Visual Analogue Scale (VAS) scores during the active phase and the second stage of labor (P < 0.05), and remarkably higher levels of NO, ANP, and ET-1 (P < 0.05). There were no statistically significant differences in adverse reactions and modes of delivery between the two groups (P < 0.05). ELA starting from the latent phase can improve the miRNA-146b level in maternal breast milk, alleviate labor pain of parturients, and shorten stages of labor. Therefore, our study is worthy of clinical promotion. We still need to do more experiments and use more data to conclude more scientific results in future research work.

[Effects of dezocine on cardiac myocytes injury induced by hypoxia and reoxygenation in rats and its mechanism].

Objective: To investigate the mechanisms of dezocine on regulating H9C2 oxidative stress and apoptosis of rat cardiac myocytes induced by hypoxia-reoxygenation(H/R) by regulating the expressions of microRNA-7a- 5p(miR-7a-5p)/ubiquitin E3 ligase tripartite motif 10(TRIM10). Methods: H9C2 cells were divided into control group (cultured normally), H/R group (treated with hypoxia for 3 h and then reoxygenation for 4 h), different doses of dezocine intervention group (H9c2 cells were pretreated with dezocine at the concentrations of 10-7, 10-6 and 10-5 mmol/L for 24 h, and then treated with H/R), H/R+miR-7a-5p group (H9C2 cells were transfected with miR-7a-5p mimics and then treated with H/R), H/R+miR-NC group (H9C2 cells were transfected with miR-NC and then treated with H/R), H/R+Dezocine+anti-miR-7a-5p group (H9c2 cells transfected with anti-miR-7a-5p were pretreated with 10-5 mmol/L dezocine for 24 h, and then treated with H/R), H/R+dezocine+ anti-miR-NC Group (H9c2 cells transfected with anti-miR-NC were pretreated with 10-5 mmol/L dezocine for 24 h, and then treated with H/R). Each group of cells was set with 3 replicate wells, and the experiment was repeated 3 times. The content of malondialdehyde(MDA) and activity of superoxide dismutase(SOD) and glutathione peroxidas(GSH-Px) were detected by the enzyme-linked immunosorbent assay. The cells apoptosis was detected by flow cytometry. The protein expressions of B-cell lymphoma-2(Bcl-2), Bcl-2-associated X protein(Bax) and TRIM10 were detected by Western blot, and the expressions of miR-7a-5p and TRIM10 mRNA were detected by real-time quantitative PCR(RT-qPCR). The double luciferase reporter gene experiment was used to verify the regulatory relationship between miR-7a-5p and TRIM10. Results: Compared with the control group, the MDA content, apoptosis rate, the expression of Bax protein, and the expression of TRIM10 mRNA and protein in the H/R group were all increased (P＜0.05), while the activities of SOD and GSH-Px, and the expressions of Bcl-2 protein and miR-7a-5p were all decreased (P＜0.05). Compared with the H/R group, the MDA content, apoptosis rate, the expression of Bax protein, and the expression of TRIM10 mRNA and protein in the different doses of dezocine intervention group were decreased (P＜0.05), while the activities of SOD and GSH-Px, and the expressions of Bcl-2 protein and miR-7a-5p were all increased (P＜0.05), and there were significant differences in each index between the different doses of dezocine intervention groups (P＜ 0.05). Compared with the H/R+miR-NC group, the MDA content, apoptosis rate, the protein expressions of Bax and TRIM10 in the H/R+miR-7a-5p group were decreased (P＜0.05), while the activities of SOD and GSH-Px, and the expression of Bcl-2 protein were all increased (P＜0.05). Compared with the H/R+dezocine+anti- miR-NC group, the MDA content, apoptosis rate, the protein expressions of Bax and TRIM10 in the H/R+dezocine+anti-miR-7a-5p group were all increased (P＜0.05), while the activities of SOD and GSH-Px, and the expression of Bcl-2 protein were all decreased (P＜0.05). Conclusion: Dezocine can reduce oxidative stress and apoptosis of rat cardiomyocytes H9C2 induced by H/R, which may play a role in regulating the miR-7a-5p / TRIM10 axis.

Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study.

Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data.

Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes-either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

Digital tongue image analyses for health assessment.

Traditional Chinese Medicine (TCM), as an effective alternative medicine, utilizes tongue diagnosis as a major method to assess the patient's health status by examining the tongue's color, shape, and texture. Tongue images can also give the pre-disease indications without any significant disease symptoms, which provides a basis for preventive medicine and lifestyle adjustment. However, traditional tongue diagnosis has limitations, as the process may be subjective and inconsistent. Hence, computer-aided tongue diagnoses have a great potential to provide more consistent and objective health assessments. This paper reviewed the current trends in TCM tongue diagnosis, including tongue image acquisition hardware, tongue segmentation, feature extraction, color correction, tongue classification, and tongue diagnosis system. We also present a case of TCM constitution classification based on tongue images.

Auto3DCryoMap: an automated particle alignment approach for 3D cryo-EM density map reconstruction.

BACKGROUND: Cryo-EM data generated by electron tomography (ET) contains images for individual protein particles in different orientations and tilted angles. Individual cryo-EM particles can be aligned to reconstruct a 3D density map of a protein structure. However, low contrast and high noise in particle images make it challenging to build 3D density maps at intermediate to high resolution (1-3 Å). To overcome this problem, we propose a fully automated cryo-EM 3D density map reconstruction approach based on deep learning particle picking. RESULTS: A perfect 2D particle mask is fully automatically generated for every single particle. Then, it uses a computer vision image alignment algorithm (image registration) to fully automatically align the particle masks. It calculates the difference of the particle image orientation angles to align the original particle image. Finally, it reconstructs a localized 3D density map between every two single-particle images that have the largest number of corresponding features. The localized 3D density maps are then averaged to reconstruct a final 3D density map. The constructed 3D density map results illustrate the potential to determine the structures of the molecules using a few samples of good particles. Also, using the localized particle samples (with no background) to generate the localized 3D density maps can improve the process of the resolution evaluation in experimental maps of cryo-EM. Tested on two widely used datasets, Auto3DCryoMap is able to reconstruct good 3D density maps using only a few thousand protein particle images, which is much smaller than hundreds of thousands of particles required by the existing methods. CONCLUSIONS: We design a fully automated approach for cryo-EM 3D density maps reconstruction (Auto3DCryoMap). Instead of increasing the signal-to-noise ratio by using 2D class averaging, our approach uses 2D particle masks to produce locally aligned particle images. Auto3DCryoMap is able to accurately align structural particle shapes. Also, it is able to construct a decent 3D density map from only a few thousand aligned particle images while the existing tools require hundreds of thousands of particle images. Finally, by using the pre-processed particle images, Auto3DCryoMap reconstructs a better 3D density map than using the original particle images.

DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM.

BACKGROUND: Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. RESULTS: Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. CONCLUSIONS: Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately.

Effect of Trimetazidine on Diabetic Patients with Coronary Heart Diseases: A Meta-Analysis of Randomized, Controlled Trials.

Objective To explore the therapeutic effects of trimetazidine (TMZ) on diabetic patients with coronary heart diseases.Methods We conducted a comprehensive electronic search of PubMed, EMBASE, and Cochrane databases between the inception dates of databases and May 2019 (last search conducted on 30 May 2019) to identify randomized controlled trials. The evaluation method recommended by Cochrane Collaboration for bias risk assessment was employed for quality assessment. Random or fixed models were used to investigate pooled mean differences in left ventricular function, serum glucose metabolism, serum lipid profile, myocardial ischemia episodes and exercise tolerance with effect size indicated by the 95% confidence interval (CI).Results Additional TMZ treatment contributed to considerable improvement of left ventricular ejection fraction (WMD=4.39, 95%CI: 3.83, 4.95, P<0.00001), left ventricular end diastolic diameter (WMD=-3.17, 95%CI: -4.90, -1.44, P=0.0003) and left ventricular end systolic diameter (WMD=-4.69, 95%CI: -8.66, -0.72, P=0.02). TMZ administration also significantly decreased fasting blood glucose (SMD=-0.43, 95%CI: -0.70, -0.17, P=0.001), glycosylated hemoglobin level (WMD=-0.59, 95%CI: -0.95, -0.24, P=0.001), serum level of total cholesterol (WMD=-20.36, 95%CI: -39.80, -0.92, P=0.04), low-density lipoprotein cholesterol (WMD=-20.12, 95%CI: -32.95, -7.30, P=0.002) and incidence of myocardial ischemia episodes (SMD=-0.84, 95%CI: -1.50, -0.18, P=0.01). However, there were no significant differences in serum triglyceride level, high-density lipoprotein cholesterol, exercise tolerance between the TMZ group and the control group. Conclusion TMZ treatment in diabetic patients with coronary heart disease is effective to improve cardiac function, serum glucose and lipid metabolism and clinical symptoms.

The effects of dietary reduced mineral elements and coated cysteamine supplementation on bacterial diversity in the ileum of finishing pigs.

This study was conducted to investigate the effects of different levels of dietary partial MEs and coated cysteamine (CC) supplementation on gut microbiota in finishing pigs. Results showed that whittling down dietary partial MEs (Cu, Fe, Zn, Mn) by 20% and 40% had little effect on the microbial diversity, community structure, and bacterial relative abundance in the ileum of finishing pigs. Supplementation with 1,600 mg/kg CC also had no obvious effect on the microbial diversity, community structure, and bacterial relative abundance in the finishing pig ileum when fed diets with a normal MEs level. However, the abundance of Peptostreptococcaceae, Pasteurella, and Pasteurella_aerogenes was higher, and the abundance of Actinobacillus_minor was lower in the 20% ME reduction diet treatment than that in the 20% ME reduction with 1,600 mg/kg CC diet group (p < 0.05). In conclusion, our results suggested that there is no obvious effect on gut microbiota when dietary partial MEs are reduced by 20% or 40%, which indicates the feasibility of reducing dietary partial MEs by 20% or 40% in finishing pigs. Supplementation with CC changed the relative abundance of some bacteria related to opportunistic pathogenicity in the finishing pig ileum when were fed a 20% ME reduction diet.

Discovery of Dioxino[2,3-f]quinazoline derivative VEGFR-2 inhibitors exerting significant antipro-liferative activity in HUVECs and mice.

Twelve 2,3-dihydro-[1,4]-dioxino[2,3-f]quinazoline derivatives were designed and evaluated as vascular endothelial growth factor receptor 2 (VEGFR-2) inhibitors. The most half-maximal inhibitory concentration (IC50) values of them were less than 10 nM. Among these compounds, 13d displayed highly effective inhibitory activity against VEGFR-2 (IC50 = 2.4 nM) and excellent antiproliferative activities against human umbilical vein endothelial cells (HUVECs) (IC50 = 1.2 nM). When anti-tumor animal experiments were carried out in mice, the tumor almost disappeared (TGI = 133.0%) after six days of administration of 13d. Therefore, 13d was a potential and effective anticancer agent. The binding conformations were respectively compared between VEGFR-2 with 13d and leading compound lenvatinib, and shows that they have similar binding modes.