Brain Delivery of Protein Therapeutics by Cell Matrix-Inspired Biomimetic Nanocarrier.

Protein therapeutics are anticipated to offer significant treatment options for central nervous system (CNS) diseases. However, the majority of proteins are unable to traverse the blood-brain barrier (BBB) and reach their CNS target sites. Inspired by the natural environment of active proteins, the cell matrix components hyaluronic acid (HA) and protamine (PRTM) are used to self-assemble with proteins to form a protein-loaded biomimetic core and then incorporated into ApoE3-reconstituted high-density lipoprotein (rHDL) to form a protein-loaded biomimetic nanocarrier (Protein-HA-PRTM-rHDL). This cell matrix-inspired biomimetic nanocarrier facilitates the penetration of protein therapeutics across the BBB and enables their access to intracellular target sites. Specifically, CAT-HA-PRTM-rHDL facilitates rapid intracellular delivery and release of catalase (CAT) via macropinocytosis-activated membrane fusion, resulting in improved spatial learning and memory in traumatic brain injury (TBI) model mice (significantly reduces the latency of TBI mice and doubles the number of crossing platforms), and enhances motor function and prolongs survival in amyotrophic lateral sclerosis (ALS) model mice (extended the median survival of ALS mice by more than 10 days). Collectively, this cell matrix-inspired nanoplatform enables the efficient CNS delivery of protein therapeutics and provides a novel approach for the treatment of CNS diseases.

Rapid identification model based on decision tree algorithm coupling with 1H NMR and feature analysis by UHPLC-QTOFMS spectrometry for sandalwood.

Sandalwood is one of the most valuable woods in the world. However, today's counterfeits are widespread, it is difficult to distinguish authenticity. In this paper, similar genus (Dalbergia and Pterocarpus) and confused species (Gluta sp.) of sandalwood were quickly and efficiently identified. Rapid identification model based on 1H NMR and decision tree (DT) algorithm was firstly developed for the identification of sandalwood, and the accuracy was improved by introducing the AdaBoost algorithm. The accuracy of the final model was above 95%. And the feature components between different species of sandalwood were further explored using UHPLC-QTOFMS and NMR spectrometry. The results showed that 183 compounds were identified, among which 99 were known components, 84 were unknown components. The 1H NMR and 13C NMR signals of 505 samples were assigned, among them, 14 compounds were attributed, characteristic chemical shift intervals with great differences in the model were analysed. Furthermore, the fragmentation pattern of different compounds from sandalwood, in both positive and negative ion ESI modes, was summarized. The results showed a potential and rapid tool based on DT, NMR spectroscopy and UHPLC-QTOFMS, which had performed great potential for rapid identification and feature analysis of sandalwood.