Biodegradable silica nanocapsules enable efficient nuclear-targeted delivery of native proteins for cancer therapy.

Cell nucleus is the desired subcellular organelle of many therapeutic drugs. Although numerous nanomaterial-based methods have been developed which could facilitate nuclear-targeted delivery of small-molecule drugs, few are known to be capable of delivering exogenous native proteins. Herein, we report a convenient and highly robust approach for effective nuclear-targeted delivery of native proteins/antibodies by using biodegradable silica nanocapsules (BSNPs) that were surface-modified with different nuclear localization signals (NLS) peptides. We found that, upon gaining entry to mammalian cells via endocytosis, such nanocapsules (protein@BSNP-NLS) could effectively escape from endolysosomal vesicles with the assistance of an endosomolytic peptide (i.e., L17E), accumulate in cell nuclei and release the encapsulated protein cargo with biological activities. Cloaked with HeLa cell membrane, DNase@BSNP-NLS/L17E-M (with L17E encapsulated) homologously delivered functional proteins to cancer cell nuclei in tumor-xenografted mice. In vitro and in vivo anti-tumor properties, such as long blood circulation time and effective tumor growth inhibition, indicate that the nuclear-targeted cell-membrane-cloaked BSNPs (DNase@BSNP-NLS/L17E-M) platform is a promising therapeutic approach to nuclear related diseases.

Blood-brain barrier (BBB)-on-a-chip: a promising breakthrough in brain disease research.

The blood-brain barrier (BBB) represents a key challenge in developing brain-penetrating therapeutic molecules. BBB dysfunction is also associated with the onset and progression of various brain diseases. The BBB-on-a-chip (µBBB), an organ-on-chip technology, has emerged as a powerful in vitro platform that closely mimics the human BBB microenvironments. While the µBBB technology has seen wide application in the study of brain cancer, its utility in other brain disease models ("µBBB+") is less appreciated. Based on the advances of the µBBB technology and the evolution of in vitro models for brain diseases over the last decade, we propose the concept of a "µBBB+" system and summarize its major promising applications in pathological studies, personalized medical research, drug development, and multi-organ-on-chip approaches. We believe that such a sophisticated "µBBB+" system is a highly tunable and promising in vitro platform for further advancement of the understanding of brain diseases.

Porous Silicon Nanocarriers with Stimulus-Cleavable Linkers for Effective Cancer Therapy.

Porous silicon nanoparticles (pSiNPs) are widely utilized as drug carriers due to their excellent biocompatibility, large surface area, and versatile surface chemistry. However, the dispersion in pore size and biodegradability of pSiNPs arguably have hindered the application of pSiNPs for controlled drug release. Here, a step-changing solution to this problem is described involving the design, synthesis, and application of three different linker-drug conjugates comprising anticancer drug doxorubicin (DOX) and different stimulus-cleavable linkers (SCLs) including the photocleavable linker (ortho-nitrobenzyl), pH-cleavable linker (hydrazone), and enzyme-cleavable linker (ß-glucuronide). These SCL-DOX conjugates are covalently attached to the surface of pSiNP via copper (I)-catalyzed alkyne-azide cycloaddition (CuAAC, i.e., click reaction) to afford pSiNP-SCL-DOXs. The mass loading of the covalent conjugation approach for pSiNP-SCL-DOX reaches over 250 µg of DOX per mg of pSiNPs, which is notably twice the mass loading achieved by noncovalent loading. Moreover, the covalent conjugation between SCL-DOX and pSiNPs endows the pSiNPs with excellent stability and highly controlled release behavior. When tested in both in vitro and in vivo tumor models, the pSiNP-SCL-DOXs induces excellent tumor growth inhibition.

An Overview of Organs-on-Chips Based on Deep Learning.

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of "big data" and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.