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Efforts on bladder cancer treatment have been shifting from extensive surgery to organ preservation in the past decade. To this end, we herein develop a multifunctional nanoagent for bladder cancer downstaging and bladder-preserving therapy by integrating mucosa penetration, reduced off-target effects, and internal irradiation therapy into a nanodrug. Specifically, an iron oxide nanoparticle was used as a carrier that was coated with hyaluronic acid (HA) for facilitating mucosa penetration. Dibenzocyclooctyne (DBCO) was introduced into the HA coating layer to react through bioorthogonal reaction with azide as an artificial receptor of bladder cancer cells, to improve the cellular internalization of the nanoprobe labeled with 177Lu. Through magnetic resonance imaging, the targeted imaging of both nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) was realized after intravesical instillation of the multifunctional probe, both NMIBC and MIBC were found downstaged, and the metastasis was inhibited, which demonstrates the potential of the multifunctional nanoprobe for bladder preservation in bladder cancer treatment.

Cardiac‐cerebral vascular diseases (CCVDs) are acknowledged as a major threat to public health, leading to more than one‐third of all deaths worldwide. The complex anatomical structure and immune features of blood vessels significantly affect the development of CCVDs, and magnetic resonance angiography (MRA) is one of the main diagnostic approaches for the accurate diagnosis and prognosis of CCVDs. However, MRA suffers from intrinsic problems derived from its blood flow‐dependency, and the clinical Gd‐chelating contrast agents are limited by their rapid vascular extravasation. Over the past decade, spurred on by nanoscience and nanotechnology, numerous contrast agents based on magnetic nanomaterials have been developed to enhance the contrast of MRA, with these including iron oxide nanoparticles, rare earth‐doped nanoparticles, and metal‐organic coordination polymers. The molecular MR imaging of vasculopathy using specific nanoprobes has been explored to obtain a better understanding of the molecular aspects of CCVDs. In this review, the state of the art in MRA nanoprobes is introduced, and recent achievements in the diagnosis of CCVDs using MR imaging are summarized. Additionally, the future prospects and limitations of MRA based on nanoprobes are discussed. The current review provides methodological designs and ideas for subsequent MRA nanoprobes. Based on numerous novel nanoprobes, the molecular MR imaging of vasculopathy has been explored for the accurate diagnosis and prognosis of cardiac‐cerebral vascular diseases (CCVDs), including acute ischemic stroke, coronary heart disease, thrombus, etc.

Leveraging deep learning-based techniques to classify diseases has attracted extensive research interest in recent years. Nevertheless, most of the current studies only consider single-modal medical images, and the number of ophthalmic diseases that can be classified is relatively small. Moreover, imbalanced data distribution of different ophthalmic diseases is not taken into consideration, which limits the application of deep learning techniques in realistic clinical scenes. In this paper, we propose a Multimodal Multi-disease Long-tailed Classification Network (M2LC-Net) in response to the challenges mentioned above. M2LC-Net leverages ResNet18-CBAM to extract features from fundus images and Optical Coherence Tomography (OCT) images, respectively, and conduct feature fusion to classify 11 common ophthalmic diseases. Moreover, Class Activation Mapping (CAM) is employed to visualize each mode to improve interpretability of M2LC-Net. We conduct comprehensive experiments on realistic dataset collected from a Grade III Level A ophthalmology hospital in China, including 34,396 images of 11 disease labels. Experimental results demonstrate effectiveness of our proposed model M2LC-Net. Compared with the state-of-the-art, various performance metrics have been improved significantly. Specifically, Cohen's kappa coefficient κ has been improved by 3.21%, which is a remarkable improvement.