Isobavachalcone exhibits antifungal and antibiofilm effects against C. albicans by disrupting cell wall/membrane integrity and inducing apoptosis and autophagy.

Isobavachalcone (IBC) is a natural flavonoid with multiple pharmacological properties. This study aimed to evaluate the efficacy of IBC against planktonic growth and biofilms of Candida albicans (C. albicans) and the mechanisms underlying its antifungal action. The cell membrane integrity, cell metabolic viability, and cell morphology of C. albicans treated with IBC were evaluated using CLSM and FESEM analyses. Crystal violet staining, CLSM, and FESEM were used to assess the inhibition of biofilm formation, as well as dispersal and killing effects of IBC on mature biofilms. RNA-seq combined with apoptosis and autophagy assays was used to examine the mechanisms underlying the antifungal action of IBC. IBC exhibited excellent antifungal activity with 8 µg/mL of MIC for C. albicans. IBC disrupted the cell membrane integrity, and inhibited biofilm formation. IBC dispersed mature biofilms and damaged biofilm cells of C. albicans at 32 µg/mL. Moreover, IBC induced apoptosis and autophagy-associated cell death of C. albicans. The RNA-seq analysis revealed upregulation or downregulation of key genes involved in cell wall synthesis (Wsc1 and Fks1), ergosterol biosynthesis (Erg3, and Erg11), apoptisis (Hsp90 and Aif1), as well as autophagy pathways (Atg8, Atg13, and Atg17), and so forth, in response to IBC, as evidenced by the experiment-based phenotypic analysis. These results suggest that IBC inhibits C. albicans growth by disrupting the cell wall/membrane, caused by the altered expression of genes associated with ß-1,3-glucan and ergosterol biosynthesis. IBC induces apoptosis and autophagy-associated cell death by upregulating the expression of Hsp90, and altering autophagy-related genes involved in the formation of the Atg1 complex and the pre-autophagosomal structure. Together, our findings provide important insights into the potential multifunctional mechanism of action of IBC.

Goal or Miss? A Bernoulli Distribution for In-Game Outcome Prediction in Soccer.

Due to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting in-game outcomes based on the ongoing game state is already being used as a statistical tool. However, soccer is a sport with low-scoring games and frequent draws, which makes in-game prediction challenging. Most existing studies focus on pre-game prediction instead. This paper, however, proposes a two-stage method for soccer in-game outcome prediction, namely in-game outcome prediction (IGSOP). When the full length of a soccer game is divided into sufficiently small time frames, the goal scored by each team in each time frame can be modeled as a random variable following the Bernoulli distribution. In the first stage, IGSOP adopts state-based machine learning to predict the probability of a scoring goal in each future time frame. In the second stage, IGSOP simulates the remainder of the game to estimate the outcome of a game. This two-stage approach effectively captures the dynamic situation after a goal and the uncertainty in the late phase of a game. Chinese Super League data have been used for algorithm training and evaluation, and the results demonstrate that IGSOP outperforms existing methods, especially in predicting draws and prediction during final moments of games. IGSOP provides a novel perspective to solve the problem of in-game outcome prediction in soccer, which has a potential ripple effect on related research.