[Analysis of adverse reactions risk signal of Xinyuan Capsules based on national ADR monitoring spontaneous reporting system].

In recent years, the safety problems and events of traditional Chinese medicine represented by liver injury have occurred frequently. In particular, Polygonum multiflorum has been widely used and considered as a "non-toxic" tonic traditional Chinese medicine for thousands of years. However, in recent years, frequent reports of liver injury events have attracted widespread attention at home and abroad, which has made unfavorable impacts on traditional Chinese medicine and its international development. Some scho-lars have found that susceptible genes of P. multiflorum on liver injury lay a scientific foundation for formulating rational comprehensive prevention and control measures for liver injury risk of P. multiflorum and its relevant preparations. But what are the risk signals of adverse reactions of P. multiflorum in clinical application? Spontaneous reporting system is an important way to monitor and find adverse drug reaction(ADR) signals after the drug is launched in the market. It can find the ADR signals in time and effectively, and then effectively prevent and avoid the occurrence of adverse drug events. At present, the data mining technique has gradually become the main method of ADR/adverse event(AE) report analysis and evaluation at home and abroad. Specifically, Bayesian confidence propagation neural network in Bayesian method is a commonly used risk signal early warning analysis method. In this paper, BCPNN method was used to excavate the risk signals of adverse reactions of Xinyuan Capsules, a traditional Chinese medicine preparation containing P. multiflorum, such as nausea, diarrhea, rash, dizziness, vomiting, abdominal pain, headache, liver cell damage, so as to provide evidence-based evidence for clinical safe and rational use of drugs.

Exosomes: potential diagnostic markers and drug carriers for adenomyosis.

Adenomyosis is a common benign gynecological disorder and an important factor leading to infertility in fertile women. Adenomyosis can cause deep lesions and is persistent and refractory in nature due to its tumor-like biological characteristics, such as the ability to implant, adhere, and invade. The pathogenesis of adenomyosis is currently unclear. Therefore, new therapeutic approaches are urgently required. Exosomes are nanoscale vesicles secreted by cells that carry proteins, genetic materials and other biologically active components. Exosomes play an important role in maintaining tissue homeostasis and regulating immune responses and metabolism. A growing body of work has shown that exosomes and their contents are key to the development and progression of adenomyosis. This review discusses the current research progress, future prospects and challenges in this emerging therapeutic tool by providing an overview of the changes in the adenomyosis uterine microenvironment and the biogenesis and functions of exosomes, with particular emphasis on the role of exosomes and their contents in the regulation of cell migration, proliferation, fibrosis formation, neovascularization, and inflammatory responses in adenomyosis.

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images.

Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1) Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at "pollen localization problem") and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at "pollen classification problem"). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.