An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
In this paper, to study rum or spreading model for the web forum of which the topological structure has a strong heterogeneity, we propose a novel susceptible-exposed-infected-removed (SEIR) model by introducing the importance and fuzziness of the content of the rum or. We derive mean-field equations characterizing the dynamics of the SEIR model on heterogeneous network. Then a steady-state analysis is conducted to investigate the critical threshold and the final size of the rum or spreading. The simulation results demonstrate that small fuzziness can effectively reduce the maximum rum or influence. In addition, the critical threshold is independent of the stifling rate. We also get a conclusion that whether a rum or can spread is related to the fuzziness of rum or itself.
Studies have described vasculogenic mimicry (VM) as an alternative circulatory system to blood vessels in multiple malignant tumor types, including hepatocellular carcinoma (HCC). In the current study, we aimed to seek novel and more efficient treatment strategies by targeting VM and explore the underlying mechanisms in HCC cells. Cell counting kit-8 (CCK-8) assay and colony survival assay were performed to explore the inhibitory effect of incarvine C (IVC) on human cancer cell proliferation. Flow cytometry was performed to analyze the cell cycle distribution after DNA staining and cell apoptosis by the Annexin V-PE and 7-AAD assay. The effect of IVC on Rho-associated, coiled-coil-containing protein kinase (ROCK) was determined by western blotting and stress fiber formation assay. The inhibitory role of IVC on MHCC97H cell VM formation was determined by formation of tubular network structures on Matrigel in vitro, real time-qPCR, confocal microscopy and western blotting techniques. We explored an anti-metastatic HCC agent, IVC, derived from traditional Chinese medicinal herbs, and found that IVC dose-dependently inhibited the growth of MHCC97H cells. IVC induced MHCC97H cell cycle arrest at G1 transition, which was associated with cyclin-dependent kinase 2 (CDK-2)/cyclin-E1 degradation and p21/p53 up-regulation. In addition, IVC induced apoptotic death of MHCC97H cells. Furthermore, IVC strongly suppressed the phosphorylation of the ROCK substrate myosin phosphatase target subunit-1 (MYPT-1) and ROCK-mediated actin fiber formation. Finally, IVC inhibited cell-dominant tube formation in vitro, which was accompanied with the down-regulation of VM-key factors as detected by real time-qPCR and immunofluorescence. Taken together, the effective inhibitory effect of IVC on MHCC97H cell proliferation and neovascularization was associated with ROCK inhibition, suggesting that IVC may be a new potential drug candidate for the treatment of HCC.
An ensemble-modeling scheme incorporating coarse-grained simulations with experimental small-angle X-ray scattering (SAXS) data is applied to dengue virus 2 (DENV2) nonstructural protein 5 (NS5). NS5 serves a key role in viral replication through its two domains that are connected by a 10-residue polypeptide segment. A set of representative structures is generated from a simulated structure pool using SAXS data fitting by the non-negativity least squares (NNLS) or standard ensemble optimization method (EOM) based on a genetic algorithm (GA). It is found that a proper low-energy threshold of the structure pool is necessary to produce a conformational ensemble of two representative structures by both NNLS and GA that agrees well with the experimental SAXS profile. The stability of the constructed ensemble is validated also by molecular dynamics simulations with an all-atom force field. The constructed ensemble successfully revealed the domain–domain orientation and domain-contacting interface of DENV2 NS5. Using experimental data fitting and additional investigations with synthesized data, it is found that energy restraint on the conformational pool is necessary to avoid overinterpretation of experimental data by spurious conformational representations.
<b><i>Introduction:</i></b> Bladder cancer (BC) is a major health concern that poses a significant threat to the population, with an increasing incidence rate and a high risk of recurrence and progression. The primary clinical method for diagnosing BC is cystoscopy, but due to the limitations of traditional white light cystoscopy and inadequate clinical experience among junior physicians, its detection rate for bladder tumor, especially small and flat lesions, is relatively low. However, recent years have seen remarkable advancements in the application of artificial intelligence (AI) technology in the field of medicine. This has led to the development of numerous AI algorithms that have been successfully integrated into medical practices, providing valuable assistance to clinicians. The purpose of this study is to develop a cystoscopy algorithm that is real time, cost effective, high performing, and accurate, with the aim of enhancing the detection rate of bladder tumors during cystoscopy. <b><i>Materials and Methods:</i></b> For this study, a dataset of 3,500 cystoscopic images obtained from 100 patients diagnosed with BC was collected, and a deep learning model was developed utilizing the U-Net algorithm within a convolutional neural network for training purposes. <b><i>Results:</i></b> This study randomly divided 3,500 images from 100 BC patients into training and validation groups, and each patient’s pathology result was confirmed. In the validation group, the accuracy of tumor recognition by the U-Net algorithm reached 98% compared to primary urologists, with greater accuracy and faster detection speed. <b><i>Conclusion:</i></b> This study highlights the potential of U-Net-based deep learning techniques in the detection of bladder tumors. The establishment and optimization of the U-Net model is a significant breakthrough and it provides a valuable reference for future research in the field of medical image processing.
Abstract Quantification of circulating tumor DNA (ctDNA) levels in blood enables non-invasive surveillance of cancer progression. Fragle is an ultra-fast deep learning-based method for ctDNA quantification directly from cell-free DNA fragment length profiles. We developed Fragle using low-pass whole genome sequence (lpWGS) data from multiple cancer types and healthy control cohorts, demonstrating high accuracy, and improved lower limit of detection in independent cohorts as compared to existing tumor-naïve methods. Uniquely, Fragle is also compatible with targeted sequencing data, exhibiting high accuracy across both research and commercial targeted gene panels. We used this method to study longitudinal plasma samples from colorectal cancer patients, identifying strong concordance of ctDNA dynamics and treatment response. Furthermore, prediction of minimal residual disease in resected lung cancer patients demonstrated significant risk stratification beyond a tumor-naïve gene panel. Overall, Fragle is a versatile, fast, and accurate method for ctDNA quantification with potential for broad clinical utility.
Abstract Our understanding of mutations in noncoding DNA is still nascent across most cancer types. Similarly, the potential clinical utility of noncoding DNA in cancer liquid biopsy assays remains mostly unexplored. Here, we identify high-frequency noncoding mutation hotspots in whole genomes from hundreds of gastrointestinal tumors and show that these mutations can be detected in liquid biopsy samples from cancer patients. We demonstrate how profiling of noncoding mutation hotspots can significantly increase the accuracy of ctDNA burden estimation, with minimal impact on sequencing cost, when paired with existing targeted cfDNA assays. Our results suggest that targeted NGS liquid biopsy assays should target both protein-coding and noncoding mutations when estimation and monitoring of ctDNA burden is a key clinical endpoint. Citation Format: Guo Yu, Zhong Wee Poh, Guanhua Zhu, Iain Tan, Sarah Ng, Patrick Tan, Anders Skanderup. Monitoring of ctDNA burden from noncoding DNA [abstract]. In: Proceedings of the AACR Special Conference on Advancing Precision Medicine Drug Development: Incorporation of Real-World Data and Other Novel Strategies; Jan 9-12, 2020; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_1):Abstract nr 44.
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