Photoactivation of sulfonated polyplexes enables localized gene silencing by DsiRNA in breast cancer cells.

Translation potential of RNA interference nanotherapeutics remains challenging due to in vivo off-target effects and poor endosomal escape. Here, we developed novel polyplexes for controlled intracellular delivery of dicer substrate siRNA, using a light activation approach. Sulfonated polyethylenimines covalently linked to pyropheophorbide-α for photoactivation and bearing modified amines (sulfo-pyro-PEI) for regulated endosomal escape were investigated. Gene knock-down by the polymer-complexed DsiRNA duplexes (siRNA-NPs) was monitored in breast cancer cells. Surprisingly, sulfo-pyro-PEI/siRNA-NPs failed to downregulate the PLK1 or eGFP proteins. However, photoactivation of these cell associated-polyplexes with a 661-nm laser clearly restored knock-down of both proteins. In contrast, protein down-regulation by non-sulfonated pyro-PEI/siRNA-NPs occurred without any laser treatments, indicating cytoplasmic disposition of DsiRNA followed a common intracellular release mechanism. Therefore, sulfonated pyro-PEI holds potential as a unique trap and release light-controlled delivery platform for on-demand gene silencing bearing minimal off target effects.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation.

Objective: This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy. Method: The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture. Results: The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture. Conclusion: Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance.