A5074091864- Brain Tumor Detection and Classification
- Dementia and Cognitive Impairment Research
- Advanced Nanomaterials in Catalysis
- Medical Imaging and Analysis
- Renal and Vascular Pathologies
- Alzheimer's disease research and treatments
- Nanoparticle-Based Drug Delivery
- Graphene and Nanomaterials Applications
- Extracellular vesicles in disease
- Carbon and Quantum Dots Applications
University of Shanghai for Science and Technology
2021-2023
Abstract Early diagnosis of Alzheimer's disease (AD) plays a key role in preventing and responding to this neurodegenerative disease. It has shown that, compared with single imaging modality‐based classification AD, synergy exploration among multimodal neuroimages is beneficial for the pathological identification. However, effectively exploiting information still big challenge due lack efficient fusion methods. Herein, network based on attention mechanism proposed, which magnetic resonance...
A graphene oxide (GO)-based nanocarrier that imparts tumor-selective delivery of dual-drug with enhanced therapeutic index, is introduced. GO conjugated Au@Ag and Fe3O4 nanoparticles, which facilitates it SERS tracking magnetic targeting abilities, followed by the covalent binding anti-HER2 antibody, thus allowing to both actively passively target SKBR3 cells, human breast cancer cells expressed HER2. Intracellular drug behaviors are probed using spectroscopy in a spatiotemporal manner,...
Due to the global challenge of donor kidney shortage, expanding pool deceased donors has been proposed include expanded criteria donors. However, lack methods precisely measure injury and predict outcome still leads high discard rates recipient complications. As such, evaluation quality is critical prior transplantation. Biomarkers from urine or serum provide potential advantages for precise quality. Herein, simultaneous detection secretory leukocyte peptidase inhibitor (SLPI) interleukin 18...
It has been a hot topic to detect Alzheimer's disease (AD) from MRI and other neuroimaging data by machine learning in recent years. A large number of deep models have developed, such as alexnet, VGg net, Gan so on. The common limitation these is that they rely on labeled training images need deeper network structure achieve higher accuracy. challenging obtain the field medical image. Training suitable image classification model scratch also requires lot resources. To solve problems, this...