Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain m...Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain multiple related targets and an appropriate combination can maximize benefits while minimizing adverse reactions.As such,it is especially important to derive mathematical models for their quantitative calculation.In this paper,we introduce mathematical rules for the synergistic,additive,and antagonistic effects of multi-drug combinations developed in our laboratory.We have established a“onebelt,one-line”model and provide examples of the quantitative calculation of the synergistic,additive,and antagonistic effects of a combination of multiple components.We also explain how to scientifically and precisely determine the intensity of these synergies,additions and antagonisms,as well as their corresponding dose ranges,thereby laying a solid theoretical foundation for market listing combinatorial drugs and foods for special medical purposes.展开更多
Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentat...Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.展开更多
文摘Multi-drug(or multi-element)combinations are often prescribed in the practice of clinical medicine and as foods for special medical purposes.The main motivations for these combinations are that most diseases contain multiple related targets and an appropriate combination can maximize benefits while minimizing adverse reactions.As such,it is especially important to derive mathematical models for their quantitative calculation.In this paper,we introduce mathematical rules for the synergistic,additive,and antagonistic effects of multi-drug combinations developed in our laboratory.We have established a“onebelt,one-line”model and provide examples of the quantitative calculation of the synergistic,additive,and antagonistic effects of a combination of multiple components.We also explain how to scientifically and precisely determine the intensity of these synergies,additions and antagonisms,as well as their corresponding dose ranges,thereby laying a solid theoretical foundation for market listing combinatorial drugs and foods for special medical purposes.
文摘Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.
