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Cancer is a leading public health problem worldwide. Its treatment remains a daunting challenge, although significant progress has been made in existing treatments in recent years. A large concern is the poor therapeutic effect due to lack of specificity and low bioavailability. Gene therapy has recently emerged as a powerful tool for cancer therapy. However, delivery methods limit its therapeutic effects. Exosomes, a subset of extracellular vesicles secreted by most cells, have the characteristics of good biocompatibility, low toxicity and immunogenicity, and great designability. In the past decades, as therapeutic carriers and diagnostic markers, they have caught extensive attention. This review introduced the characteristics of exosomes, and focused on their applications as delivery carriers in DNA, messenger RNA (mRNA), microRNA (miRNA), small interfering RNA (siRNA), circular RNA (circRNA) and other nucleic acids. Meanwhile, their application in cancer therapy and exosome-based clinical trials were presented and discussed. Through systematic summarization and analysis, the recent advances and current challenges of exosome-mediated nucleic acid delivery for cancer therapy are introduced, which will provide a theoretical basis for the development of nucleic acid drugs. Graphical Abstract

Monodisperse, uniform, and hierarchically mesostructured silica particles with a thin shell have been fabricated via one-step synthesis using dodecanethiol (C(12)-SH) and CTAB as dual templates. A series of hierarchically mesostructured silica particles with a morphology similar to that of pomegranate can be obtained by simply adjusting the mass ratio of C(12)-SH to CTAB. When the mass ratio is increased, a mesophase transformation occurs from an ordered 2D hexagonal structure to a mesostructured cellular foam in the core of the hierarchically mesoporous silica particles. These unique silica particles are characterized by small-angle X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and energy dispersive spectroscopy (EDS), Fourier transform infrared spectroscopy (FT-IR), and nitrogen adsorption-desorption measurements. The formation mechanism of the hierarchically mesostructured silica particles with a thin shell is proposed according to the experimental results. Synergistic self-assembly of C(12)-SH and CTAB in the solution is believed to play a key role in mediating the formation of these hierarchical silica mesostructures, and the hydrophobic dodecanethiol can act as both the swelling agent for CTAB micelles and coagent for the formation of a microemulsion with CTAB micelles. This synthesis method is simple, straightforward, and suitable for the preparation of the other biomineral nanostructures that are unique scaffolds in biological, medical, and catalytic applications.

The data in a patient's laboratory test result is a notable resource to support clinical investigation and enhance medical research. However, for a variety of reasons, this type of data often contains a non-trivial number of missing values. For example, physicians may neglect to order tests or document the results. Such a phenomenon reduces the degree to which this data can be utilized to learn efficient and effective predictive models. To address this problem, various approaches have been developed to impute missing laboratory values; however, their performance has been limited. This is due, in part, to the fact no approaches effectively leverage the contextual information (1) in individual or (2) between laboratory test variables. We introduce an approach to combine an unsupervised prefilling strategy with a supervised machine learning approach, in the form of extreme gradient boosting (XGBoost), to leverage both types of context for imputation purposes. We evaluated the methodology through a series of experiments on approximately 8200 patients' records in the MIMIC-III dataset. The results demonstrate that the new model outperforms baseline and state-of-the-art models on 13 commonly collected laboratory test variables. In terms of the normalized root mean square derivation (nRMSD), our model exhibits an imputation improvement by over 20%, on average. Missing data imputation on the temporal variables can be largely improved via prefilling strategy and the supervised training technique, which leverages both the longitudinal and cross-sectional context simultaneously.

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