Cervical cell classification with deep-learning algorithms

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks

Women tend to face many problems throughout their lives; cervical cancer is one of the most dangerous diseases that they can face, and it has many negative consequences. Regular screening and treatment of precancerous lesions play a vital role in the fight against cervical cancer. It is becoming increasingly common in medical practice to predict the early stages of serious illnesses, such as heart attacks, kidney failure, and cancer, using machine learning-based techniques. To overcome these obstacles, we propose the use of auxiliary modules and a special residual block, to record contextual interactions between object classes and to support the object reference strategy. Unlike the latest state-of-the-art classification method, we create a new architecture called the Reinforcement Learning Cancer Network, "RL-CancerNet", which diagnoses cervical cancer with incredible accuracy. We trained and tested our method on two well-known publicly available datasets, SipaKMeD and Herlev, to assess it and enable comparisons with earlier methods. Cervical cancer images were labeled in this dataset; therefore, they had to be marked manually. Our study shows that, compared to previous approaches for the assignment of classifying cervical cancer as an early cellular change, the proposed approach generates a more reliable and stable image derived from images of datasets of vastly different sizes, indicating that it will be effective for other datasets.

Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures

BACKGROUND

Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs.

RESULTS

We have developed a successful morphology-based Cascade region-based convolution neural network (Cascade R-CNN) system for detecting senescent MSCs, which can automatically locate single cells of different sizes and shapes in multicellular images and assess their senescence state. Additionally, we tested the applicability of the Cascade R-CNN system for MSC senescence and examined the correlation between morphological changes with other senescence indicators.

CONCLUSIONS

This deep learning has been applied for the first time to detect senescent MSCs, showing promising performance in both chronic and acute MSC senescence. The system can be a labor-saving and cost-effective option for screening MSC culture conditions and anti-aging drugs, as well as providing a powerful tool for non-invasive and real-time morphological image analysis integrated into cell production.