Analysis on the evolution and governance of the biotechnology industry of China

The past twenty years have witnessed the high-speed growth of China’s biotechnology industry, and this presents an excellent opportunity to examine the changes that have taken place, especially, to carry out overall evaluation and governance analysis from the perspective of technology policies. Although China’s biotechnology industry has achieved tremendous extension both in scale and structure, the strengths it gained from basic research have been significantly weakened by commercialization. This has resulted in the comparatively limited scale of the whole industry, innovation-lacking products, poor output from research and development and scarcity of industrial resources. A large range of literature regarding China’s biotechnology industry attributes these outcomes to vague and even inappropriate governance, findings supported mainly by analyses based on the linear model of impact of government policies on industrial development. In these analyses, government, enterprises and companies as well as R&D organizations are either put on the opposite poles or in a straight line. After examining the nature of China’s biotechnology industry, and in particular the dynamic procedures in research and development, the authors of this paper argue that besides government, enterprises and R&D organizations, a diverse array of factors should be taken into account as we tackle issues emerging in understanding the development of China’s biotechnology industry. Furthermore, these factors, human or nonhuman, should not be arranged as opposing poles or linearly connected points on a straight line. They are in fact all knitted in networks and act as both knitters and knots. China’s biotechnology industry gains its strength to develop and evolve from these networks, thus its governance must be aimed at improving their stability and quality. Although the main disciplinary perspectives of this research are historical and sociological (including identification of the three development stages of biotechnology in China since 1978 to present days), a large number of concepts and ideas from management studies as well as an interdisciplinary approach are also incorporated into the analysis. The main model used in this research is Actor Network Theory, which is employed as a basic theoretical frame. From this starting point the authors attempt to make a closer examination of China’s biotechnology industry both at the level of technology research and development and at the level of commercialization. The modeling process in this research can be regarded as an attempt to explore the social construction of China’s biotechnology industry. The paper reveals how China’s biotechnology industry develops in the form of networks within the country’s social context and what kinds of relationships exist among the relevant factors; therefore, providing guiding insights for improving the governance of China’s biotechnology industry both in policy and management

Tumor-associated EGFR over-expression specifically activates Stat3 and Smad7 resulting in desensitization of TGF-β signaling

Transforming Growth Factor-[beta] (TGF-[beta]) and Epidermal Growth Factor (EGF) signaling pathways are both independently implicated as key regulators in tumor formation and progression. Here, we demonstrate that activation of the tumor-associated and over-expressed EGFR desensitizes TGF-[beta] signaling and its cytostatic regulation through specific Stat3 activation and Smad7 induction. In normal and tumor human cell lines, reduction of TGF-[beta]-mediated Smad2 phosphorylation, nuclear translocation and Smad3 target gene activation were observed where EGFR is over-expressed, but not in cells which expressed EGFR at normal levels. The EGFR downstream signaling molecules phosphatidyinositol-3 Kinase (PI3K) or mitogen-activated protein kinase/ERK kinase (MEK) are not responsible for the down-regulation of TGF-[beta] signaling since blockade of them by specific pharmacological inhibitors LY294002 and U0126 had little effects on the sensitivity of TGF-[beta] signaling. We identified Stat3 as a signaling molecule activated specifically and persistently by over-expressed EGFR, but not by normal levels. Importantly, Stat3 is responsible for the reduced TGF-[beta] sensitivity, since its knockdown by siRNA restored TGF-[beta] signaling sensitivity. Furthermore, over-expressed EGFR, through Stat3 activates Smad7 promoter activity, increasing its protein levels, which is a negative regulator of TGF-[beta] signaling. Consequently, cells were re-sensitized to TGF-[beta] when Smad7 expression was reduced using siRNA. Therefore we establish a novel EGFR-Stat3-Smad7-TGF-[beta] signaling molecular axis where tumor-associated over-expression of EGFR in epithelial cells results in hyperactivation of Stat3, which activates Smad7 expression, compromising the TGF-[beta]'s cytostatic regulation of epithelium and consequent tumor formation

Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works