Formation of Large Intracellular Actin Networks Following Plasmonic Cell Fusion

Optimization study of plasmonic cell fusion

Artificial cell fusion often serves as a valuable tool for studying different applications in biology and medicine, including natural development, immune response, cancer metastasis and production of therapeutic molecules. Plasmonic cell fusion, a technique that uses specific cell labeling by gold nanoparticles and resonant femtosecond pulse irradiation for fusing neighboring cells, has been demonstrated useful for such applications, allowing high cell specificity and an overall low toxicity. Despite these advantages, the numerous experimental factors contributing to plasmonic fusion have often led to subpar fusion efficiencies, requiring repeated experiments and extensive calibration protocols for achieving optimal results. In this work we present a study that aims to improve the overall performance of plasmonic cell fusion in terms of fusion efficiency and cell viability. By varying the pulse fluence, nanoparticle concentration, incubation times, and culture handling protocols, we demonstrate up to 100% fusion of malignant epithelial cells across the entire irradiated area of the culture. We also show that some of the smaller cells may stay viable for up to several days. The results would allow plasmonic fusion to play a key role in numerous studies and applications that require specific, high-efficiency cell-cell fusion.

Multichannel CNN Model for Biomedical Entity Reorganization

Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.