Multi-objective Genetic Algorithm Based Deep Learning Model for Automated COVID-19 Detection Using Medical Image Data

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

A study on the preparation conditions of lidocaine microemulsion based on multi-objective genetic algorithm

Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17 μg/(cm2·s) and Y2 = 0.74 mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14 μg/(cm2·s) and Y2 = 0.80 mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.