Due to the abuse of antibiotics, antimicrobial resistance is rapidly emerging and becoming a major global risk for public health. Thus, there is an urgent need for reducing the use of antibiotics, finding novel treatment approaches, and developing controllable release systems. In this work, a dual synergistic antibacterial platform with on-demand release ability based on silver nanoparticles (AgNPs) and antimicrobial peptide (AMP) coloaded porous silicon (PSi) was developed. The combination of AgNPs and AMPs (Tet-213, KRWWKWWRRC) exhibited an excellent synergistic antibacterial effect. As a carrier, porous silicon can efficiently load AgNPs and AMP under mild conditions and give the platform an on-demand release ability and a synergistic release effect. The AgNPs and AMP coloaded porous silicon microparticles (AgNPs-AMP@PSiMPs) exhibited an acid pH and reactive oxygen species (ROS)-stimulated release of silver ions (Ag+) and AMPs under bacterial infection conditions because of oxidation and desorption effects. Moreover, the release of the bactericide could be promoted by each other due to the interplay between AgNPs and Tet-213. In vitro antibacterial tests demonstrated that AgNPs-AMP@PSiMPs inherited the intrinsic properties and synergistic antibacterial efficiency of both bactericides. In addition, wound dressing loaded with AgNPs-AMP@PSiMPs showed outstanding in vivo bacteria-killing activity, accelerating wound-healing, and low biotoxicity in aStaphylococcus aureus-infected rat wound model. The present work demonstrated that PSiMPS might be an efficient platform for loading the antibiotic-free bactericide, which could synergistically and on-demand release to fight wound infection and promote wound healing.

Spatially resolved tissue lipidomics is essential for accurate intraoperative and postoperative cancer diagnosis by revealing molecular information in the tumor microenvironment. Matrix-free laser desorption ionization mass spectrometry imaging (LDI-MSI) is an emerging attractive technology for label-free visualization of metabolites distributions in biological specimens. However, the development of LDI-MSI technology that could conveniently and authentically reveal molecular distribution on tissue samples is still a challenge. Herein, we present a tissue imprinting technology by retaining tissue lipids on 2D nanoflakes-capped silicon nanowires (SiNWs) for further mass spectrometry imaging and cancer diagnosis. The 2D nanoflakes were prepared by liquid exfoliation of molybdenum disulfide (MoS2) with nitrogen-doped graphene quantum dots (NGQDs), which serve as both intercalation agent and dispersant. The obtained NGQD@MoS2 nanoflakes were then decorated on the tip of vertical SiNWs, forming a hybrid NGQD@MoS2/SiNWs nanostructure, which display excellent lipid extraction ability, enhanced LDI efficiency and molecule imaging capability. The peak number and total ion intensity of different lipids species on animal lung tissues obtained by tissue imprinting LDI-MSI on NGQD@MoS2/SiNWs were ∼4–5 times greater than those on SiNWs substrate. As a proof-of-concept demonstration, the NGQD@MoS2/SiNWs nanostructure was further applied to visualize phospholipids on sliced non small cell lung cancer (NSCLC) tissue along with the adjacent normal tissue. On the basis of selected feature lipids and machine learning algorithm, a prediction model was constructed to discriminate NSCLC tissues from the adjacent normal tissues with an accuracy of 100% for the discovery cohort and 91.7% for the independent validation cohort.

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Serum lipid metabolites have been emerging as ideal biomarkers for disease diagnosis and prediction. In the current stage, nontargeted or targeted lipidomic research mainly relies on a liquid chromatography–mass spectrometry (LC–MS) platform, but future clinical applications need more robust and high-speed platforms. Surface-assisted laser desorption ionization mass spectrometry (SALDI-MS) has shown excellent advantages in the high-speed analysis of lipid metabolites. However, the platform in the positive ion mode is more inclined to target a certain class of lipids, leading to the low coverage of lipid detection and limiting its practical translation to clinical applications. Herein, we proposed a dual-mechanism-driven strategy for high-coverage detection of serum lipids on a novel SALDI-MS target, which is a composite nanostructure comprising vertical silicon nanowires (VSiNWs) decorated with AuNPs and polydopamine (VSiNW-Au-PDA). The performance of laser desorption and ionization on the target can be enhanced by charge-driven desorption coupled with thermal-driven desorption. Simultaneous detection of 236 serum lipids (S/N ≥ 5) including neutral and polar lipids can be achieved in the positive ion mode. Among these, 107 lipid peaks were successfully identified. When combined with VSiNW-Au-PDA and VSiNW chips, 479 lipid peaks can be detected in serum samples in positive and negative ion modes, respectively. Based on the platform, serum samples from 57 hepatocellular carcinoma (HCC) patients and 76 healthy controls were analyzed. After data mining, 14 lipids containing different lipid types (TAG, CE, PC) were selected as potential lipidomic biomarkers. With the assistance of an artificial neural network, a diagnostic model with a sensitivity of 92.7% and a specificity of 96% was constructed for HCC diagnosis.