Microorganisms are closely related to skin diseases, and microbiological imbalances or invasions of exogenous pathogens can be a source of various skin diseases. The development and prognosis of such skin diseases are also closely related to the type and composition ratio of microorganisms present. Therefore, through detection of the characteristics and changes in microorganisms, the possibility for diagnosis and prediction of skin diseases can be markedly improved. The abundance of microorganisms and an understanding of the vast amount of biological information associated with these microorganisms has been a formidable task. However, with advances in large-scale sequencing, artificial intelligence (AI)-related machine learning can serve as a means to analyze large-scales of data related to microorganisms along with determinations regarding the type and status of diseases. In this review, we describe some uses of this exciting, new emerging field. In specific, we described the recognition of fungi with convolutional neural networks (CNN), the combined application of microbial genome sequencing and machine learning and applications of AI in the diagnosis of skin diseases as related to the gut-skin axis.

Antibody-drug conjugates (ADCs) are anticancer drugs that combine cytotoxic small-molecule drugs (payloads) with monoclonal antibodies through a chemical linker and that transfer toxic payloads to tumor cells expressing target antigens. All ADCs are based on human IgG. In 2009, the Food and Drug Administration (FDA) approved gemtuzumab ozogamicin as the initial first-generation ADC. Since then, at least 100 ADC-related projects have been initiated, and 14 ADCs are currently being tested in clinical trials. The limited success of gemtuzumab ozogamicin has led to the development of optimization strategies for the next generation of drugs. Subsequently, experts have improved the first-generation ADCs and have developed second-generation ADCs such as ado-trastuzumab emtansine. Second-generation ADCs have higher specific antigen levels, more stable linkers and longer half-lives and show great potential to transform cancer treatment models. Since the first two generations of ADCs have served as a good foundation, the development of ADCs is accelerating, and third-generation ADCs, represented by trastuzumab deruxtecan, are ready for wide application. Third-generation ADCs are characterized by strong pharmacokinetics and high pharmaceutical activity, and their drug-to-antibody ratio mainly ranges from 2 to 4. In the past decade, the research prospects of ADCs have broadened, and an increasing number of specific antigen targets and mechanisms of cytotoxic drug release have been discovered and studied. To date, seven ADCs have been approved by the FDA for lymphoma, and three have been approved to treat breast cancer. The present review explores the function and development of ADCs and their clinical use in cancer treatment.