Risk assessment of imported malaria in China: a machine learning perspective.

BACKGROUND: Following China's official designation as malaria-free country by WHO, the imported malaria has emerged as a significant determinant impacting the malaria reestablishment within China. The objective of this study is to explore the application prospects of machine learning algorithms in imported malaria risk assessment of China. METHODS: The data of imported malaria cases in China from 2011 to 2019 was provided by China CDC; historical epidemic data of malaria endemic country was obtained from World Malaria Report, and the other data used in this study are open access data. All the data processing and model construction based on R, and map visualization used ArcGIS software. RESULTS: A total of 27,088 malaria cases imported into China from 85 countries between 2011 and 2019. After data preprocessing and classification, clean dataset has 765 rows (85 * 9) and 11 cols. Six machine learning models was constructed based on the training set, and Random Forest model demonstrated the best performance in model evaluation. According to RF, the highest feature importance were the number of malaria deaths and Indigenous malaria cases. The RF model demonstrated high accuracy in forecasting risk for the year 2019, achieving commendable accuracy rate of 95.3%. This result aligns well with the observed outcomes, indicating the model's reliability in predicting risk levels. CONCLUSIONS: Machine learning algorithms have reliable application prospects in risk assessment of imported malaria in China. This study provides a new methodological reference for the risk assessment and control strategies adjusting of imported malaria in China.

The potential mechanism of Neu5Gc inducing colorectal cancer based on network pharmacology and experimental validation.

Colorectal cancer has high morbidity and mortality worldwide, especially in western countries; the incidence of colorectal cancer has been high, which is closely related to the high intake of red meat; and the N-glycolylneuraminic acid (Neu5Gc) is responsible for red meat-induced colorectal cancer. A large number of previous studies have suggested that exogenous Neu5Gc-activated inflammation induced the occurrence of colorectal cancer. However, it has not been known whether the Neu5Gc has a direct inducing effect on colorectal cancer. In this study, we found that Neu5Gc promoted the proliferation of colorectal cancer cells and normal intestinal epithelial cells, and further screened out 98 Neu5Gc targets related to the occurrence and development of colorectal cancer by network pharmacology. Subsequently, GO and KEGG enrichment analyses of these targets revealed that mainly enriched in the PI3K-Akt signaling pathway. Then, we selected SRC, HRAS, CDK2, CCNA2, and AKT2 as core targets based on the phenomena of the previous experiments and the available literature reports, and then we used AutoDock for molecular docking with Neu5Gc; the results found that these five genes could bind to Neu5Gc stably. In vitro experiments showed that the mRNA levels of SRC, HRAS, AKT2, CDK2, and CCNA2 were upregulated and the protein levels of HRAS, AKT2, and CCNA2 were enhanced in FHC and SW620 cells after Neu5Gc (100 ng/mL) treatment. In conclusion, this study revealed that Neu5Gc probably acted as a carcinogen that stimulates the expression of proto-oncogene HRAS and the PI3K-Akt pathway and accelerated cell cycle progression. These findings revealed a novel mechanism that Neu5Gc promoted the occurrence and development of colorectal cancer.