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Cancer, accounts for around 10 million deaths annually, is the second leading cause of death globally. The continuous emergency of drug-resistant cancers and the low specificity of anticancer agents are the main challenges in the control and eradication of cancers, so it is imperative to develop novel anticancer agents. Immense efforts have been made in developing new lead compounds and novel chemotherapeutic strategies for the treatment of various forms of cancers in recent years. β-Lactam derivatives constitute versatile and attractive scaffolds for the drug discovery since these kinds of compounds possess a variety of pharmacological properties, and some of them exhibited promising potency against both drug-sensitive and drug-resistant cancer cell lines. Thus, β-lactam moiety is a useful template for the development of novel anticancer agents. This review will provide an overview of β-lactam derivatives with the potential therapeutic application for the treatment of cancers covering articles published between 2000 and 2020. The mechanisms of action, the critical aspects of design and structureactivity relationships are also discussed.

To improve the diagnostic efficiency and accuracy of corona virus disease 2019 (COVID-19), and to study the application of artificial intelligence (AI) in COVID-19 diagnosis and public health management, the computer tomography (CT) image data of 200 COVID-19 patients are collected, and the image is input into the AI auxiliary diagnosis software based on the deep learning model, "uAI the COVID-19 intelligent auxiliary analysis system", for focus detection. The software automatically carries on the pneumonia focus identification and the mark in batches, and automatically calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes, and in density, the most common shadow is the ground glass opacity. The detection rate of manual detection method is 95.30%, misdiagnosis rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of AI software focus detection method based on deep learning model is 99.76%, the misdiagnosis rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, it can effectively identify COVID-19 focus and provide relevant data information of focus to provide objective data support for COVID-19 diagnosis and public health management.

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