RESUMEN
Objective: The objective of this investigation was to elucidate the key active compounds and molecular mechanisms underlying the therapeutic potential of airpotato yam rhizome (AYR) in colorectal cancer (CRC) treatment. Methods: By utilizing network pharmacology and molecular docking, key targets and signaling pathways of AYR against CRC were predicted and subsequently validated in cellular and mouse xenograft models. Results: This study initially predicted that quercetin was the primary compound in AYR that might have potential efficacy against CRC and that EGFR and AKT1 could be the main targets of AYR, with the EGF/EGFR-induced PI3K/AKT signaling pathway potentially playing a crucial role in the anti-CRC effects of AYR. Molecular docking analysis further indicated a strong binding affinity between quercetin and EGFR, primarily through hydrogen bonds. Additionally, the AYR-derived drug-containing serum was found to inhibit the PI3K/AKT signaling pathway, as demonstrated by decreased levels of p-PI3K, p-AKT, and BCL2, which ultimately led to enhanced apoptosis of HCT116 and HT29 cells. The potential antitumor effects of AYR were investigated in nude mouse xenograft models of human HCT116 and HT29 cells, in which AYR was found to induce tumor cell apoptosis and inhibit tumor formation. Conclusion: AYR may promote CRC cell apoptosis by suppressing the PI3K/AKT signaling pathway, which provides a basis for further research on the safe and effective use of AYR for the treatment of CRC.
RESUMEN
A wearable Braille-to-speech translation system is of great importance for providing auditory feedback in assisting blind people and people with speech impairment. However, previous reported Braille-to-speech translation systems still need to be improved in terms of comfortability or integration. Here, a Braille-to-speech translation system that uses dual-functional electrostatic transducers which are made of fabric-based materials and can be integrated into textiles is reported. Based on electrostatic induction, the electrostatic transducer can either serve as a tactile sensor or a loudspeaker with the same design. The proposed electrostatic transducers have excellent output performances, mechanical robustness, and working stability. By combining the devices with machine learning algorithms, it is possible to translate the Braille alphabet and 40 commonly used words (extensible) into speech with an accuracy of 99.09% and 97.08%, respectively. This work demonstrates a new approach for further developments of advanced assistive technology toward improving the lives of disabled people.