Peptide-Driven Proton Sponge Nano-Assembly for Imaging and Triggering Lysosome-Regulated Immunogenic Cancer Cell Death.

Triggering lysosome-regulated immunogenic cell death (ICD, e.g., pyroptosis and necroptosis) with nanomedicines is an emerging approach for turning an "immune-cold" tumor "hot"-a key challenge faced by cancer immunotherapies. Proton sponge such as high-molecular-weight branched polyethylenimine (PEI) is excellent at rupturing lysosomes, but its therapeutic application is hindered by uncontrollable toxicity due to fixed charge density and poor understanding of resulted cell death mechanism. Here, a series of proton sponge nano-assemblies (PSNAs) with self-assembly controllable surface charge density and cell cytotoxicity are created. Such PSNAs are constructed via low-molecular-weight branched PEI covalently bound to self-assembling peptides carrying tetraphenylethene pyridinium (PyTPE, an aggregation-induced emission-based luminogen). Assembly of PEI assisted by the self-assembling peptide-PyTPE leads to enhanced surface positive charges and cell cytotoxicity of PSNA. The self-assembly tendency of PSNAs is further optimized by tuning hydrophilic and hydrophobic components within the peptide, thus resulting in the PSNA with the highest fluorescence, positive surface charge density, cell uptake, and cancer cell cytotoxicity. Systematic cell death mechanistic studies reveal that the lysosome rupturing-regulated pyroptosis and necroptosis are at least two causes of cell death. Tumor cells undergoing PSNA-triggered ICD activate immune cells, suggesting the great potential of PSNAs to trigger anticancer immunity.

Deep learning assisted sparse array ultrasound imaging.

This study aims to restore grating lobe artifacts and improve the image resolution of sparse array ultrasonography via a deep learning predictive model. A deep learning assisted sparse array was developed using only 64 or 16 channels out of the 128 channels in which the pitch is two or eight times the original array. The deep learning assisted sparse array imaging system was demonstrated on ex vivo porcine teeth. 64- and 16-channel sparse array images were used as the input and corresponding 128-channel dense array images were used as the ground truth. The structural similarity index measure, mean squared error, and peak signal-to-noise ratio of predicted images improved significantly (p < 0.0001). The resolution of predicted images presented close values to ground truth images (0.18 mm and 0.15 mm versus 0.15 mm). The gingival thickness measurement showed a high level of agreement between the predicted sparse array images and the ground truth images, as indicated with a bias of -0.01 mm and 0.02 mm for the 64- and 16-channel predicted images, respectively, and a Pearson's r = 0.99 (p < 0.0001) for both. The gingival thickness bias measured by deep learning assisted sparse array imaging and clinical probing needle was found to be <0.05 mm. Additionally, the deep learning model showed capability of generalization. To conclude, the deep learning assisted sparse array can reconstruct high-resolution ultrasound image using only 16 channels of 128 channels. The deep learning model performed generalization capability for the 64-channel array, while the 16-channel array generalization would require further optimization.