In the past decade, mesoporous silica nanoparticles (MSNs) have attracted more and more attention for their potential biomedical applications. With their tailored mesoporous structure and high surface area, MSNs as drug delivery systems (DDSs) show significant advantages over traditional drug nanocarriers. In this review, we overview the recent progress in the synthesis of MSNs for drug delivery applications. First, we provide an overview of synthesis strategies for fabricating ordered MSNs and hollow/rattle-type MSNs. Then, the in vitro and in vivo biocompatibility and biotranslocation of MSNs are discussed in relation to their chemophysical properties including particle size, surface properties, shape, and structure. The review also highlights the significant achievements in drug delivery using mesoporous silica nanoparticles and their multifunctional counterparts as drug carriers. In particular, the biological barriers for nano-based targeted cancer therapy and MSN-based targeting strategies are discussed. We conclude with our personal perspectives on the directions in which future work in this field might be focused.

Language model pre-training, such as BERT, has significantly improved the performances of many natural language processing tasks. However, pre-trained language models are usually computationally expensive, so it is difficult to efficiently execute them on resourcerestricted devices. To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models. By leveraging this new KD method, the plenty of knowledge encoded in a large "teacher" BERT can be effectively transferred to a small "student" Tiny-BERT. Then, we introduce a new two-stage learning framework for TinyBERT, which performs Transformer distillation at both the pretraining and task-specific learning stages. This framework ensures that TinyBERT can capture the general-domain as well as the task-specific knowledge in BERT. TinyBERT 41 with 4 layers is empirically effective and achieves more than 96.8% the performance of its teacher BERT BASE on GLUE benchmark, while being 7.5x smaller and 9.4x faster on inference. TinyBERT 4 is also significantly better than 4-layer state-of-the-art baselines on BERT distillation, with only ∼28% parameters and ∼31% inference time of them. Moreover, TinyBERT 6 with 6 layers performs on-par with its teacher BERT BASE .

Microbial activities shape the biogeochemistry of the planet and macroorganism health. Determining the metabolic processes performed by microbes is important both for understanding and for manipulating ecosystems (for example, disruption of key processes that lead to disease, conservation of environmental services, and so on). Describing microbial function is hampered by the inability to culture most microbes and by high levels of genomic plasticity. Metagenomic approaches analyse microbial communities to determine the metabolic processes that are important for growth and survival in any given environment. Here we conduct a metagenomic comparison of almost 15 million sequences from 45 distinct microbiomes and, for the first time, 42 distinct viromes and show that there are strongly discriminatory metabolic profiles across environments. Most of the functional diversity was maintained in all of the communities, but the relative occurrence of metabolisms varied, and the differences between metagenomes predicted the biogeochemical conditions of each environment. The magnitude of the microbial metabolic capabilities encoded by the viromes was extensive, suggesting that they serve as a repository for storing and sharing genes among their microbial hosts and influence global evolutionary and metabolic processes.

In our previous study we reported that the interaction of nanoparticles with cells can be influenced by particle shape, but until now the effect of particle shape on in vivo behavior remained poorly understood. In the present study, we control the fabrication of fluorescent mesoporous silica nanoparticles (MSNs) by varying the concentration of reaction reagents especially to design a series of shapes. Two different shaped fluorescent MSNs (aspect ratios, 1.5, 5) were specially designed, and the effects of particle shape on biodistribution, clearance and biocompatibility in vivo were investigated. Organ distributions show that intravenously administrated MSNs are mainly present in the liver, spleen and lung (>80%) and there is obvious particle shape effects on in vivo behaviors. Short-rod MSNs are easily trapped in the liver, while long-rod MSNs distribute in the spleen. MSNs with both aspect ratios have a higher content in the lung after PEG modification. We also found MSNs are mainly excreted by urine and feces, and the clearance rate of MSNs is primarily dependent on the particle shape, where short-rod MSNs have a more rapid clearance rate than long-rod MSNs in both excretion routes. Hematology, serum biochemistry, and histopathology results indicate that MSNs would not cause significant toxicity in vivo, but there is potential induction of biliary excretion and glomerular filtration dysfunction. These findings may provide useful information for the design of nanoscale delivery systems and the environmental fate of nanoparticles.