The small-molecule alkaloid halofuginone (HF) is obtained from febrifugine. Recent studies on HF have aroused widespread attention owing to its universal range of noteworthy biological activities and therapeutic functions, which range from parasite infections and fibrosis to autoimmune diseases. In particular, HF is believed to play an excellent anticancer role by suppressing the proliferation, adhesion, metastasis, and invasion of cancers. This review supports the goal of demonstrating various anticancer effects and molecular mechanisms of HF. In the studies covered in this review, the anticancer molecular mechanisms of HF mainly included transforming growth factor-β (TGF-β)/Smad-3/nuclear factor erythroid 2-related factor 2 (Nrf2), serine/threonine kinase proteins (Akt)/mechanistic target of rapamycin complex 1(mTORC1)/wingless/integrated (Wnt)/β-catenin, the exosomal microRNA-31 (miR-31)/histone deacetylase 2 (HDAC2) signaling pathway, and the interaction of the extracellular matrix (ECM) and immune cells. Notably, HF, as a novel type of adenosine triphosphate (ATP)-dependent inhibitor that is often combined with prolyl transfer RNA synthetase (ProRS) and amino acid starvation therapy (AAS) to suppress the formation of ribosome, further exerts a significant effect on the tumor microenvironment (TME). Additionally, the combination of HF with other drugs or therapies obtained universal attention. Our results showed that HF has significant potential for clinical cancer treatment.
The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.