Kun Qian

A simple hydrophobic-affinity-derived assembly approach to pack graphene sheets into a nanoporous foam structure has been developed. Nanoporous graphene foams with the highest pore volume and large surface area are obtained. The pore diameter of the graphene foams can be finely adjusted from the mesopore to the macropore range by employing spherical templates with different sizes. Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Janus particles with an anisotropic structure have emerged as a focus of intensive research due to their diverse composition and surface chemistry, which show excellent performance in various fields, especially in biomedical applications. In this review, we briefly introduce the structures, composition, and properties of Janus particles, followed by a summary of their biomedical applications. Then we review several design strategies including morphology, particle size, composition, and surface modification, that will affect the performance of Janus particles. Subsequently, we explore the synthetic methodologies of Janus particles, with an emphasis on the most prevalent synthetic method (surface nucleation and seeded growth). Following this, we highlight Janus particles in biomedical applications, especially in drug delivery, bio-imaging, and bio-sensing. Finally, we will consider the current challenges the materials face with perspectives in the future directions.

Abstract Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls ( p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

@Ag with tunable shell structures by multi-cycled silver mirror reactions. Optimized nanoshells achieved direct laser desorption/ionization mass spectrometry in 0.5 μL of bio-fluids. We applied these nanoshells for disease diagnosis and therapeutic evaluation. We identified patients with postoperative brain infection through daily monitoring and glucose quantitation in cerebrospinal fluid. We measured drug distribution in blood and cerebrospinal fluid systems and validated the function of blood-brain/cerebrospinal fluid-barriers for pharmacokinetics. Our work sheds light on the design of materials for advanced metabolic analysis and precision diagnostics.Preparation of samples for diagnosis can affect the detection of biomarkers and metabolites. Here, the authors use a silver nanoparticle plasmonics approach for the detection of biomarkers in patients as well as investigate the distribution of drugs in serum and cerebral spinal fluid.