A Unique Approach: Biomimetic Graphdiyne-Based Nanoplatform to Treat Prostate Cancer by Combining Cuproptosis and Enhanced Chemodynamic Therapy.

Purpose: Current treatment approaches for Prostate cancer (PCa) often come with debilitating side effects and limited therapeutic outcomes. There is urgent need for an alternative effective and safe treatment for PCa. Methods: We developed a nanoplatform to target prostate cancer cells based on graphdiyne (GDY) and a copper-based metal-organic framework (GDY-CuMOF), that carries the chemotherapy drug doxorubicin (DOX) for cancer treatment. Moreover, to provide GDY-CuMOF@DOX with homotypic targeting capability, we coated the PCa cell membrane (DU145 cell membrane, DCM) onto the surface of GDY-CuMOF@DOX, thus obtaining a biomimetic nanoplatform (DCM@GDY-CuMOF@DOX). The nanoplatform was characterized by using transmission electron microscope, atomic force microscope, X-ray diffraction, etc. Drug release behavior, antitumor effects in vivo and in vitro, and biosafety of the nanoplatform were evaluated. Results: We found that GDY-CuMOF exhibited a remarkable capability to load DOX mainly through π-conjugation and pore adsorption, and it responsively released DOX and generated Cu+ in the presence of glutathione (GSH). In vivo experiments demonstrated that this nanoplatform exhibits remarkable cell-killing efficiency by generating lethal reactive oxygen species (ROS) and mediating cuproptosis. In addition, DCM@GDY-CuMOF@DOX effectively suppresses tumor growth in vivo without causing any apparent side effects. Conclusion: The constructed DCM@GDY-CuMOF@DOX nanoplatform integrates tumor targeting, drug-responsive release and combination with cuproptosis and chemodynamic therapy, offering insights for further biomedical research on efficient PCa treatment.

Laser Linewidth Analysis and Filtering/Fitting Algorithms for Improved TDLAS-Based Optical Gas Sensor.

Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors.