Liver fibrosis, caused by chronic hepatic injury, is a major threat to human health worldwide, as there are no specific drugs available for its treatment. Natural compounds, such as berberine (BBR) and quercetin (QR), have shown the ability to regulate energy metabolism and protect the liver without significant adverse effects. Additionally, combination therapy (the cocktail therapy approach), using multiple drugs, has shown promise in treating complicated conditions, including liver injury. In this study, we prepared a salt formulation of BBR and QR (BQS) to enhance their combined effect on liver fibrosis. The formation of BQS was confirmed using various analytical techniques, including nuclear magnetic resonance spectroscopy (NMR), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FTIR), powder X-ray diffractometry (PXRD), and scanning electron microscopy (SEM). The results demonstrated that the dissolution efficiency and bioavailability of QR significantly increased in the BQS form, aligning with that of BBR, compared to the physically mixed (BQP) form. Moreover, BQS exhibited a superior inhibitory effect on fibrosis compared to BQP in the human hepatic stellate cell line LX-2 by modulating lipid accumulation, inflammation, apoptosis, and the cell cycle. Furthermore, in a mouse model of hepatic fibrosis induced by methionine and choline-deficient (MCD) diets, BQS demonstrated enhanced anti-fibrotic activities compared to BQP. These findings suggest that BQS holds promise as a potential alternative treatment for liver fibrosis. Importantly, this study provides novel insights into achieving a cocktail effect through the salt formation of two or more drugs. The results highlight the potential of salt formulations in enhancing the therapeutic efficacy and consistent biological processes of drug combinations.

This paper presents a two-population genetic algorithm (TPGA) for FfJSSPs with the maximum fuzzy completion time. TPGA uses two-string representation to represent a solution and two populations to search the optimal schedule. In each generation, crossover and mutation are only applied to one part of the chromosome and these populations are combined and updated by using half of the individuals with the bigger fitness in the combined population. Some instances of FfJSSP are designed and the performance of TPGA is tested. The computational results demonstrate the promising performance of TPGA on FfJSSP.