Inkjet Bioprinting of Biomaterials.

The inkjet technique has the capability of generating droplets in the picoliter volume range, firing thousands of times in a few seconds and printing in the noncontact manner. Since its emergence, inkjet technology has been widely utilized in the publishing industry for printing of text and pictures. As the technology developed, its applications have been expanded from two-dimensional (2D) to three-dimensional (3D) and even used to fabricate components of electronic devices. At the end of the twentieth century, researchers were aware of the potential value of this technology in life sciences and tissue engineering because its picoliter-level printing unit is suitable for depositing biological components. Currently inkjet technology has been becoming a practical tool in modern medicine serving for drug development, scaffold building, and cell depositing. In this article, we first review the history, principles and different methods of developing this technology. Next, we focus on the recent achievements of inkjet printing in the biological field. Inkjet bioprinting of generic biomaterials, biomacromolecules, DNAs, and cells and their major applications are introduced in order of increasing complexity. The current limitations/challenges and corresponding solutions of this technology are also discussed. A new concept, biopixels, is put forward with a combination of the key characteristics of inkjet printing and basic biological units to bring a comprehensive view on inkjet-based bioprinting. Finally, a roadmap of the entire 3D bioprinting is depicted at the end of this review article, clearly demonstrating the past, present, and future of 3D bioprinting and our current progress in this field.

A novel nanoplatform-based circCSNK1G3 affects CBX7 protein and promotes glioma cell growth.

Bioenvironmental and biological factors have the potential to contribute to the development of glioma, a type of brain tumor. Recent studies have suggested that a unique circular RNA called circCSNK1G3 could play a role in promoting the growth of glioma cells. It does this by stabilizing a specific microRNA called miR-181 and reducing the expression of a tumor-suppressor gene known as chromobox protein homolog 7 (CBX7). To further investigate circCSNK1G3 and its effects on glioma, we utilized a nanoplatform called adeno-associated virus (AAV)-RNAi.To explore the functional implications of circCSNK1G3, we employed siRNA to silence its expression. Along with these effects, the silencing of circCSNK1G3 led to a depletion of miR-181d and an upregulation of CBX7. When we introduced miR-181d mimics, which artificially increase the levels of miR-181d, the anti-glioma cell activity induced by circCSNK1G3 siRNA was almost completely reversed. Conversely, inhibiting miR-181d mimicked the effects of circCSNK1G3 silencing. Moreover, when we overexpressed circCSNK1G3 in glioma cells, we observed an elevation of miR-181d and a depletion of CBX7. We found that the growth of A172 xenografts (tumors) carrying circCSNK1G3 shRNA was significantly inhibited. In these xenograft tissues, we detected a depletion of circCSNK1G3 and miR-181d, as well as an upregulation of CBX7.

Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning.

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.