Diffusion-mediated carving of interior topologies of all-natural protein nanoparticles to tailor sustained drug release for effective breast cancer therapy.

Proteins are promising base materials for developing drug carriers with efficient blood circulation due to low possibilities of clearance by macrophages. However, such natural biopolymers have highly sophisticated molecular structures, preventing them from being assembled into nano-platforms with manipulable payload release profiles. Here, we report the self-assembly of two natural proteins (milk casein and rice protein) into protein nanoparticles (NPs, ∼150 nm) with tailorable release profiles. Diffusion of plant-derived paclitaxel (PTX)-containing eugenol into the hydrophobic cores of the NPs and subsequent dialysis to remove eugenol from the cores lead to the carving of the NP interiors. With the increase in the mass ratios of casein and rice protein, this process generates all-natural NPs with PTX loaded in their full cavities, semi-full cavities, or solid cores. These NPs can be efficiently uptaken by breast cancer cells and could kill the cancer cells efficiently. PTX in these NPs demonstrates increasingly sustained in vivo release profiles from full cavities, semi-full cavities, to solid cores, gradually extending its pharmacokinetic profiles in blood plasma to favor drug accumulation in breast tumor models. Consequently, the NPs with solid cores completely inhibit tumor growth in vivo, more effectively than those with full and semi-full cavities. Our work opens up a new avenue to the use of diffusion-mediated nanoscale carving in producing biomaterials with controllable interior topologies relevant to drug release profiles.

Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM.

Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.

Self-Cleaning Piezoelectric Membrane for Oil-in-Water Separation.

Ultrasound (US) treatment coupled with membrane filtration has been utilized for membrane fouling control in water treatment; however, large-scale implementation of ultrasonic cleaning equipment appeared to be cost-prohibitive. In this study, a porous lead zirconate titanate (PZT) membrane is presented that enables in situ ultrasound generation by the application of an alternating voltage (AV) to mitigate fouling during oil-in-water (O/W) emulsion separation. We expect that this method is much more cost-effective because it is more direct, avoiding buildup of fouling and the need to take the membrane offline. Because the PZT membrane is hydrophilic, its underwater surface is oleophobic so that the accumulated oil droplets will have little affinity and hence can be removed easily by in situ-generated US. The effect of the in situ US generation on membrane fouling was investigated through variation in the excitation AV and its frequency, O/W emulsion pH, emulsified oil concentration, crossflow velocity, and transmembrane pressure. The results indicated that the in situ US generation resulted in a substantial decrease of fouling during the filtration process of O/W emulsions, whereas the membrane flux was maintained closely at its initial value.