Nanosized Prussian blue and its analogs for bioimaging and cancer theranostics.

Prussian blue (PB) nanoparticles (NPs) and Prussian blue analogs (PBAs) can form metal-organic frameworks through the programmable coordination of ferrous ions with cyanide. PB and PBAs represent a burgeoning class of hybrid functional nano-systems with a wide-ranging application spectrum encompassing biomedicine, cancer diagnosis, and therapy. A comprehensive overview of recent advancements is crucial for gaining insights for future research. In this context, we reviewed the synthesis techniques and surface modification strategies employed to tailor the dimensions, morphology, and attributes of PB NPs. Subsequently, we explored advanced biomedical utilities of PB NPs, encompassing photoacoustic imaging, magnetic resonance imaging, ultrasound (US) imaging, and multimodal imaging. In particular, the application of PB NPs-mediated photothermal therapy, photodynamic therapy, and chemodynamic therapy to cancer treatment was reviewed. Based on the literature, we envision an evolving trajectory wherein the future of Prussian blue-driven biological applications converge into an integrated theranostic platform, seamlessly amalgamating bioimaging and cancer therapy. STATEMENT OF SIGNIFICANCE: Prussian blue, an FDA-approved coordinative pigment with a centuries-long legacy, has paved the way for Prussian blue nanoparticles (PB NPs), renowned for their remarkable biocompatibility and biosafety. These PB NPs have found their niche in biomedicine, playing crucial roles in both diagnostics and therapeutic applications. The comprehensive review goes beyond PB NP-based cancer therapy. Alongside in-depth coverage of PB NP synthesis and surface modifications, the review delves into their cutting-edge applications in the realm of biomedical imaging, encompassing techniques such as photoacoustic imaging, magnetic resonance imaging, ultrasound imaging, and multimodal imaging.

Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis.

This paper proposes a selective kernel convolution deep residual network based on the channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis. First, adjacent channel attention modules are connected with the spatial attention mechanism module, then all channel features and spatial features are fused and a channel-spatial attention mechanism is constructed to form the feature enhancement module. Second, the feature enhancement module is embedded in a series model based on selective kernel convolution and deep residual network and combined with multi-layer feature fusion information. The model can more effectively extract fault features from the vibration signal, compared with traditional deep learning methods, and the fault recognition efficiency is improved. Finally, the proposed method was used to experimentally diagnose bearing and gear faults, and identification accuracies of 99.87% and 97.77%, respectively, were achieved. Compared with similar algorithms, the proposed method has higher fault identification ability, thereby demonstrating the advantages of the channel-spatial attention mechanism network. In addition, the accuracy and robustness of the model were verified.