The alkynyl-containing compounds from mushrooms and their biological activities.

Mushrooms have been utilized by humans for thousands of years due to their medicinal and nutritional properties. They are a crucial natural source of bioactive secondary metabolites, and recent advancements have led to the isolation of several alkynyl-containing compounds with potential medicinal uses. Despite their relatively low abundance, naturally occurring alkynyl compounds have attracted considerable attention due to their high reactivity. Bioactivity studies have shown that alkynyl compounds exhibit significant biological and pharmacological activities, including antitumor, antibacterial, antifungal, insecticidal, phototoxic, HIV-inhibitory, and immunosuppressive properties. This review systematically compiles 213 alkynyl-containing bioactive compounds isolated from mushrooms since 1947 and summarizes their diverse biological activities, focusing mainly on cytotoxicity and anticancer effects. This review serves as a detailed and comprehensive reference for the chemical structures and bioactivity of alkynyl-containing secondary metabolites from mushrooms. Moreover, it provides theoretical support for the development of chemical constituents containing alkynyl compounds in mushrooms based on academic research and theory.

Diagnostic classification of cancers using DNA methylation of paracancerous tissues.

The potential role of DNA methylation from paracancerous tissues in cancer diagnosis has not been explored until now. In this study, we built classification models using well-known machine learning models based on DNA methylation profiles of paracancerous tissues. We evaluated our methods on nine cancer datasets collected from The Cancer Genome Atlas (TCGA) and utilized fivefold cross-validation to assess the performance of models. Additionally, we performed gene ontology (GO) enrichment analysis on the basis of the significant CpG sites selected by feature importance scores of XGBoost model, aiming to identify biological pathways involved in cancer progression. We also exploited the XGBoost algorithm to classify cancer types using DNA methylation profiles of paracancerous tissues in external validation datasets. Comparative experiments suggested that XGBoost achieved better predictive performance than the other four machine learning methods in predicting cancer stage. GO enrichment analysis revealed key pathways involved, highlighting the importance of paracancerous tissues in cancer progression. Furthermore, XGBoost model can accurately classify nine different cancers from TCGA, and the feature sets selected by XGBoost can also effectively predict seven cancer types on independent GEO datasets. This study provided new insights into cancer diagnosis from an epigenetic perspective and may facilitate the development of personalized diagnosis and treatment strategies.

Parameter estimation of the COVID-19 transmission model using an improved quantum-behaved particle swarm optimization algorithm.

The outbreak of coronavirus disease (COVID-19) and its accompanying pandemic have created an unprecedented challenge worldwide. Parametric modeling and analyses of the COVID-19 play a critical role in providing vital information about the character and relevant guidance for controlling the pandemic. However, the epidemiological utility of the results obtained from the COVID-19 transmission model largely depends on accurately identifying parameters. This paper extends the susceptible-exposed-infectious-recovered (SEIR) model and proposes an improved quantum-behaved particle swarm optimization (QPSO) algorithm to estimate its parameters. A new strategy is developed to update the weighting factor of the mean best position by the reciprocal of multiplying the fitness of each best particle with the average fitness of all best particles, which can enhance the global search capacity. To increase the particle diversity, a probability function is designed to generate new particles in the updating iteration. When compared to the state-of-the-art estimation algorithms on the epidemic datasets of China, Italy and the US, the proposed method achieves good accuracy and convergence at a comparable computational complexity. The developed framework would be beneficial for experts to understand the characteristics of epidemic development and formulate epidemic prevention and control measures.