Min Tang

Jialiang Yang

Li Xiao

Thanks to the development and deployment of whole-slide imaging technology in pathology, glass slides previously observed under a traditional microscope are now scanned and converted to digital images, which are more beneficial for remote access, portability, and ease of sharing to facilitate telepathology. More importantly, digitization of glass slides paves the way towards the wide use of artificial intelligence (AI) tools including machine/deep learning algorithms, resulting in improved diagnostic accuracy. In the past decade, a large number of studies have demonstrated the remarkable success of AI, particularly deep learning, in digital pathology, such as tumor region identification, metastasis detection, and patient prognosis. Differing from handcrafted feature-based approaches that take advantage of domain knowledge to delineate specific morphological measurements (e.g., nuclei shape and size and tissue texture) in the images as features for training, deep learning is a paradigm of feature learning entirely driven by the image data and/or labels. Herein, the use of deep learning in pathological diagnosis can not only handle increased workloads and expertise shortages but also obviate subjective diagnosis from pathologists. Yet there remain many scientific and technological challenges associated with the efficiency of deep learning algorithms for use in clinical practice. For example, deep learning requires a sufficient amount of training data for generalization and suffers from a lack of feature interpretability. The overarching goal of this special issue is to highlight novel research accomplishments and directions, related to advanced AI methodology development and applications in digital pathology.

Artificial Intelligence in Digital Pathology Image Analysis

Artificial intelligence in digital pathology image analysis

Glioblastoma (GBM) is the most common and fatal adult primary brain cancer. Maximal safe surgical resection followed by concurrent chemotherapy and radiotherapy results in a five-year survival rate less than 7% for patients afflicted with GBM, and therapeutic advances to treat glioblastoma remain stagnant. In vivo, complex cell-matrix and cellular interactions among tumor cells, stromal cells, and the extracellular matrix (ECM) lead to a dynamic and heterogeneous GBM tumor ecosystem, characterized by significant immune infiltration and immunosuppression. Traditional model systems such as 2D cell culture or animal models either lack sufficient complexity to mimic the pathophysiological GBM microenvironment or face challenges with engraftment, lack of normal immune interactions, and low throughput nature of animal experiments. The discrepancies between drug evaluation results from pre-clinical models and actual clinical outcomes have also led to failure of many compounds in clinical trials. In this dissertation, ECM and cellular aspects of GBM are independently investigated using 3D models generated with a light-based 3D bioprinting technique and brain tumor-relevant biomaterials, revealing important criteria for fabricating biomimetic GBM models. The models serve as scalable and physiologic platforms to interrogate the role of different factors, including matrix properties and cellular crosstalk, in various tumor progression events, such as tumor migration, functional dependencies, drug responses, and immunologic interactions within a species-matched condition. In addition, a computational method based on machine learning algorithms is developed to predict drug sensitivity of monocyte or microglia infiltrated GBM tissues to small molecule probes and reveal features that contribute to the outcome of the prediction. The integration of computational tool with more clinically relevant tumor models could be a promising solution to investigate the disease mechanisms and accelerate the drug development for GBM.

Investigating Glioblastoma Microenvironment and Cellular Interactions with 3D Bioprinted Tumor Models

Roni Reiter-Palmon

Zorana Ivcevic

Creativity and Innovation in Times of Crisis (COVID-19)

Cheng Guo

Ling Kui

Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume III

Shuai Cheng Li

Computational Methods in Inferring Cancer Tissue-of-Origin and Cancer Molecular Classification, Volume I

Computational tools in inferring cancer tissue-of-origin and molecular classification towards personalized cancer therapy, Volume II

In this book, author Min Tang examines the political economy of the China-based leading global Internet giant, Tencent. Tracing the historical context and shaping forces, the book illuminates Tencent’s emergence as a joint creation of the Chinese state and transnational financial capital. Tencent reveals interweaving axes of power on different levels, particularly interactions between the global digital industry and contemporary China. The expansion strategies Tencent has employed—horizontal and vertical integration, diversification and transnationalization—speak to the intrinsic trends of capitalist reproduction and the consistent features of the political economy of communications. The book also pinpoints two emerging and entangling trends— transnationalization and financialization—as unfolding trajectories of the global political economy. Understanding Tencent’s dynamics of growth helps to clarify the complex nature of China’s contemporary transformation and the multifaceted characteristics of its increasingly globalized Internet industry. This short and highly topical research volume is perfect for students and scholars of of global media, political economy, and Chinese business, media and communication, and society.

The Political Economy of China’s Surging Internet Giant

Tencent

Min Tang

Books

Your next favorite book is out there