Batemanlockhart7509

QuPath, originally created at the Centre for Cancer Research & Cell Biology at Queen's University Belfast as part of a research programme in digital pathology (DP) funded by Invest Northern Ireland and Cancer Research UK, is arguably the most wildly used image analysis software program in the world. On the back of the explosion of DP and a need to comprehensively visualise and analyse whole slides images (WSI), QuPath was developed to address the many needs associated with tissue based image analysis; these were several fold and, predominantly, translational in nature from the requirement to visualise images containing billions of pixels from files several GBs in size, to the demand for high-throughput reproducible analysis, which the paradigm of routine visual pathological assessment continues to struggle to deliver. Resultantly, large-scale biomarker quantification must increasingly be augmented with DP. Here we highlight the impact of the open source Quantitative Pathology & Bioimage Analysis DP system since its inception, by discussing the scope of scientific research in which QuPath has been cited, as the system of choice for researchers.Accurate cancer type classification based on genetic mutation can significantly facilitate cancer-related diagnosis. However, existing methods usually use feature selection combined with simple classifiers to quantify key mutated genes, resulting in poor classification performance. To circumvent this problem, a novel image-based deep learning strategy is employed to distinguish different types of cancer. Unlike conventional methods, we first convert gene mutation data containing single nucleotide polymorphisms, insertions and deletions into a genetic mutation map, and then apply the deep learning networks to classify different cancer types based on the mutation map. Selleckchem MPTP We outline these methods and present results obtained in training VGG-16, Inception-v3, ResNet-50 and Inception-ResNet-v2 neural networks to classify 36 types of cancer from 9047 patient samples. Our approach achieves overall higher accuracy (over 95%) compared with other widely adopted classification methods. Furthermore, we demonstrate the application of a Guided Grad-CAM visualization to generate heatmaps and identify the top-ranked tumor-type-specific genes and pathways. Experimental results on prostate and breast cancer demonstrate our method can be applied to various types of cancer. Powered by the deep learning, this approach can potentially provide a new solution for pan-cancer classification and cancer driver gene discovery. The source code and datasets supporting the study is available at https//github.com/yetaoyu/Genomic-pan-cancer-classification.Microvascular invasion (MVI) is one of the most important factors leading to poor prognosis for hepatocellular carcinoma (HCC) patients, and detection of MVI prior to surgical operation could great benefit patient's prognosis and survival. Since it is still lacking effective non-invasive strategy for MVI detection before surgery, novel MVI determination approaches were in urgent need. In this study, complete blood count, blood test and AFP test results are utilized to perform preoperative prediction of MVI based on a novel interpretable deep learning method to quantify the risk of MVI. The proposed method termed as "Interpretation based Risk Prediction" can estimate the MVI risk precisely and achieve better performance compared with the state-of-art MVI risk estimation methods with concordance indexes of 0.9341 and 0.9052 on the training cohort and the independent validation cohort, respectively. Moreover, further analyses of the model outputs demonstrate that the quantified risk of MVI from our model could serve as an independent preoperative risk factor for both recurrence-free survival and overall survival of HCC patients. Thus, our model showed great potential in quantification of MVI risk and prediction of prognosis for HCC patients.Water-soluble fullerene derivatives are actively investigated as potential drugs for cancer treatment due to their favorable membranotropic properties. Herein, cytotoxic effects of twenty fullerene derivatives with different solubilizing addends were evaluated in three different types of non-small-cell lung carcinoma (NSCLC). The potential structural descriptors of the solubilizing addends related to the inhibitory activities on each type of lung cancer cell were investigated by the quantitative structure-activity relationship (QSAR) approach. The determination coefficient r2 for the recommended QSAR model were 0.9325, 0.8404, and 0.9011 for A549, H460, and H1299 cell lines, respectively. The results revealed that the chemical features of the fullerene-based compounds including aromatic bonds, sulfur-containing aromatic rings, and oxygen atoms are favored properties and promote the inhibitory effects on H460 and H1299 cells. Particularly, thiophene moiety is the key functional group, which was positively correlated with strong inhibitory effects on the three types of lung cancer cells. The useful information obtained from our regression models may lead to the design of more efficient inhibitors of the three types of NSCLC.Fast and accurate calculations of the electrostatic features of highly charged biomolecules such as DNA, RNA, and highly charged proteins are crucial and challenging tasks. Traditional implicit solvent methods calculate the electrostatic features quickly, but these methods are not able to balance the high net biomolecular charges effectively. Explicit solvent methods add unbalanced ions to neutralize the highly charged biomolecules in molecular dynamic simulations, which require more expensive computing resources. Here we report developing a novel method, Hybridizing Ions Treatment (HIT), which hybridizes the implicit solvent method with an explicit method to realistically calculate the electrostatic potential for highly charged biomolecules. HIT utilizes the ionic distribution from an explicit method to predict the bound ions. The bound ions are then added in the implicit solvent method to perform the electrostatic potential calculations. In this study, two training sets were developed to optimize parameters for HIT.