[Policy Analysis in Plastic Pollution Governance and Recommendations in China].

Plastic pollution is a global concern and an issue in environmental governance. Based on the updated "Plastic Prohibition/Restriction Order" issued recently in China, the present study systematically reviewed the implementation effectiveness of the "Plastic Prohibition/Restriction Order" since 2007. Furthermore, we summarized plastics in China and plastic waste management progress policies. Additionally, three deficiencies of the updated "Plastic Prohibition/Restriction Order" were discussed:policy formulation, implementation, and supervision. Some positive recommendations were provided based on the available reports, such as integrating plastic pollution into national basic laws and regulation systems, building a network platform for public attendance, and coordinating the publicity of the "Plastic prohibition/Restriction Order" with the public interest. Besides these measurements, some points about plastic waste management in the future were also highlighted, such as the "blind area" in small retail stores, the rational sharing of environmental protection responsibility, new materials and processes, and recycling and disposal systems for plastic wastes. Most importantly, the present study could provide ideas for policy-makers to address plastic pollution at its sources.

Stems and leaves of Aconitum carmichaelii Debx. as potential herbal resources for treating rheumatoid arthritis: Chemical analysis, toxicity and activity evaluation.

According to folk usage of Aconitum carmichaelii Debx., the present study was designed to determine the feasibility of the stems and leaves of Aconitum carmichaelii Debx. as a new medicinal resource. Fourteen alkaloids in mother roots, fibrous roots, stems, and leaves of Aconitum carmichaelii Debx. were measured by HPLC-MS/MS. And multivariate analysis methods, such as clustering analysis and principal component analysis, were applied to analyze the difference among various parts. In addition, the acute toxicity, analgesia, and anti-inflammatory tests were carried out. The results suggested that the contents of alkaloids in mother roots and fibrous roots were approximate, but those of leaves and stems were different from mother roots and fibrous roots. The results of the acute toxicity testing demonstrated the toxicity of fibrous root was strongest, and mother roots were slightly less toxic than fibrous roots. The stems and leaves were far less toxic than mother and fibrous roots. In addition, the analgesia and inflammatory tests showed the effects of the various tissues had no difference each other. These results provided a basis for developing new complementary and alternative treatments for rheumatoid arthritis patients. Simultaneously, the approach may also turn wastes into treasure and promote the development of circular economy.

Cancer risk assessment in modern radiotherapy workflow with medical big data.

Modern radiotherapy (RT) is being enriched by big digital data and intensive technology. Multimodality image registration, intelligence-guided planning, real-time tracking, image-guided RT (IGRT), and automatic follow-up surveys are the products of the digital era. Enormous digital data are created in the process of treatment, including benefits and risks. Generally, decision making in RT tries to balance these two aspects, which is based on the archival and retrieving of data from various platforms. However, modern risk-based analysis shows that many errors that occur in radiation oncology are due to failures in workflow. These errors can lead to imbalance between benefits and risks. In addition, the exact mechanism and dose-response relationship for radiation-induced malignancy are not well understood. The cancer risk in modern RT workflow continues to be a problem. Therefore, in this review, we develop risk assessments based on our current knowledge of IGRT and provide strategies for cancer risk reduction. Artificial intelligence (AI) such as machine learning is also discussed because big data are transforming RT via AI.