No abstract

Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer detection. However, the complexity and heterogeneity of exosomes require improved methods to achieve the desired sensitivity and accuracy. Herein, we report our study on developing a breast cancer liquid biopsy system, including a fluorescence sensor array and deep learning (DL) tool AggMapNet. In particular, we used a 12-unit sensor array composed of conjugated polyelectrolytes, fluorophore-labeled peptides, and monosaccharides or glycans to collect fluorescence signals from cells and exosomes. Linear discriminant analysis (LDA) processed the fluorescence spectral data of cells and cellderived exosomes, demonstrating successful discrimination between normal and different cancerous cells and 100% accurate classification of different BC cells. For heterogeneous plasma-derived exosome analysis, CNN-based DL tool AggMapNet was applied to transform the unordered fluorescence spectra into feature maps (Fmaps), which gave a straightforward visual demonstration of the difference between healthy donors and BC patients with 100% prediction accuracy. Our work indicates that our fluorescent sensor array and DL model can be used as a promising noninvasive method for BC diagnosis.

No abstract

Structurally well‐defined graphene nanoribbons (GNRs) have attracted great interest because of their unique optical, electronic, and magnetic properties. However, strong π–π interactions within GNRs result in poor liquid‐phase dispersibility, which impedes further investigation of these materials in numerous research areas, including supramolecular self‐assembly. Structurally defined GNRs were synthesized by a bottom‐up strategy, involving grafting of hydrophilic poly(ethylene oxide) (PEO) chains of different lengths (GNR‐PEO). PEO grafting of 42–51 % percent produces GNR‐PEO materials with excellent dispersibility in water with high GNR concentrations of up to 0.5 mg mL−1. The “rod–coil” brush‐like architecture of GNR‐PEO resulted in 1D hierarchical self‐assembly behavior in the aqueous phase, leading to the formation of ultralong nanobelts, or spring‐like helices, with tunable mean diameters and pitches. In aqueous dispersions the superstructures absorbed in the near‐infrared range, which enabled highly efficient conversion of photon energy into thermal energy.

Due to their precise three-dimensional structures and tunable cavities, metallacages and covalent cages have been widely used in various fields, such as catalysis, separation, sensing, and biomedicine. As a result of the unique pattern of directed synthesis and modular assembly, cages can be modified in many different ways to construct stimuli-responsive cages for marker detection and imaging in biological applications. Additionally, imaging agents and drugs can be loaded into the unique cavity of the cages to achieve precise disease diagnosis and treatment. This Review summarizes recent advances in metallacages and covalent cages in biological applications and presents their representative examples in imaging and therapeutics. The problems and future development directions of cages in biological applications are emphasized, which may provide clear guidance for the design and application of precise and controllable biological diagnostic reagents.