Micropatterned Coculture Platform for Screening Nerve-Related Anticancer Drugs

Integration of Magnetic-Field-Directed Self-Assembly-Based Cell Culture and Biosensing Platform for Improving hPSCs-Derived Neurons and Quantitative Detection of Neurotransmitter

Despite the fact that human neural cell models have played significant roles in both research and cell replacement therapies for neurological diseases, the existing techniques for obtaining neurons from human pluripotent stem cells (hPSCs) are arduous and intricate. Additionally, the evaluation of neuron quality in the natural environment remains deficient. Consequently, we have developed a comprehensive platform utilizing magnetic-field-directed self-assembly (MDSA) of MXenes@Fe3O4 (M/F) nanocomposites. This platform facilitates the cultivation and in situ analysis of differentiated dopaminergic (DA) neurons. Our results showed that the introduction of M/F enhances neurite outgrowth and leads to the development of more intricate ramifications. Moreover, with the increase of magnetic field intensity, neurite outgrowth is further enhanced, and the proportion of differentiated mature neurons from hPSCs increases. This suggests that our platform promotes the maturation of neurons, emphasizing the crucial role of biophysical cues in expediting the differentiation process. The homogenization platform formed by MDSA of M/F nanocomposites exhibits high conductivity, leading to its exceptional performance in the real-time monitoring of the release of dopamine neurotransmitter from hPSC-derived DA neurons. Hence, this platform demonstrates significant potential for monitoring cell quality. In conclusion, our integrated platform, based on MDSA of M/F nanocomposites, offers a reliable and efficient means for the in vitro generation of human neurons with a controllable quality. The as-prepared platform holds potential for enhancing neuronal maturation and ensuring consistent cell quality, showing significant implications for in vitro biological research, disease modeling, and cell replacement therapy.

Metal-Organic Frameworks in Microfluidics Enable Fast Encapsulation/Extraction of DNA for Automated and Integrated Data Storage

DNA as an exceptional data storage medium offers high information density. However, DNA storage requires specialized equipment and tightly controlled environments for storage. Fast encapsulation within minutes for enhanced DNA stability to do away with specialized equipment and fast DNA extraction remain a challenge. Here, we report a DNA microlibrary that can be encapsulated by metal-organic frameworks (MOFs) within 10 min and extracted (5 min) in a single microfluidic chip for automated and integrated DNA-based data storage. The DNA microlibrary@MOFs enhances the stability of data-encoded DNA against harsh environments. The encoded information can be read out perfectly after accelerated aging, equivalent to being readable after 10 years of storage at 25 °C, 50% relative humidity, and 10 000 lx sunlight radiation. Moreover, the library enables fast retrieval of target data via flow cytometry and can be reproduced after each access.

Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs

The optimization of drug properties in the process of cancer drug development is very important to save research and development time and cost. In order to make the anti-breast cancer drug candidates with good biological activity, this paper collected 1974 compounds, firstly, the top 20 molecular descriptors that have the most influence on biological activity were screened by using XGBoost-based data feature selection; secondly, on this basis, take pIC50 values as feature data and use a variety of machine learning algorithms to compare, soas to select a most suitable algorithm to predict the IC50 and pIC50 values. It is preliminarily found that the effects of Random Forest, XGBoost and Gradient-enhanced algorithms are good and have little difference, and the Support vector machine is the worst. Then, using the Semi-automatic parameter adjustment method to adjust the parameters of Random Forest, XGBoost and Gradient-enhanced algorithms to find the optimal parameters. It is found that the Random Forest algorithm has high accuracy and excellent anti over fitting, and the algorithm is stable. Its prediction accuracy is 0.745. Finally, the accuracy of the results is verified by training the model with the preliminarily selected data, which provides an innovative solution for the optimization of the properties of anti- breast cancer drugs, and can provide better support for the early research and development of anti-breast cancer drugs.