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Wang N, Chen J, Dang Y, Zhao X, Tibenda JJ, Li N, Zhu Y, Wang X, Zhao Q, Sun L. Research progress of traditional Chinese medicine in the treatment of ischemic stroke by regulating mitochondrial dysfunction. Life Sci 2024; 357:123045. [PMID: 39251017 DOI: 10.1016/j.lfs.2024.123045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/30/2024] [Accepted: 09/06/2024] [Indexed: 09/11/2024]
Abstract
Ischemic stroke (IS) is a severe cerebrovascular disease with increasing incidence and mortality rates in recent years. The pathogenesis of IS is highly complex, with mitochondrial dysfunction playing a critical role in its onset and progression. Thus, preserving mitochondrial function is a pivotal aspect of treating ischemic brain injury. In response, there has been growing interest among scholars in the regulation of mitochondrial function through traditional Chinese medicine (TCM), including herb-derived compounds, individual herbs, and herbal prescriptions. This article reviews recent research on the mechanisms of mitochondrial dysfunction in IS and explores the potential of TCM in treating this condition by targeting mitochondrial dysfunction.
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Affiliation(s)
- Niuniu Wang
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China
| | - Jun Chen
- Department of Critical Care Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yanning Dang
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China
| | - Xinlin Zhao
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China
| | - Jonnea Japhet Tibenda
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China
| | - Nuan Li
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China
| | - Yafei Zhu
- School of Nursing, Ningxia Medical University, Yinchuan, China
| | - Xiaobo Wang
- Innovative Institute of Chinese Medicine and Pharmacy/Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Qipeng Zhao
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China.
| | - Lei Sun
- School of Pharmacy, Key Laboratory of Hui Ethnic Medicine Modernization, Ministry of Education, Ningxia Medical University, Yinchuan, China.
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Dindorf C, Dully J, Konradi J, Wolf C, Becker S, Simon S, Huthwelker J, Werthmann F, Kniepert J, Drees P, Betz U, Fröhlich M. Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence. Front Bioeng Biotechnol 2024; 12:1350135. [PMID: 38419724 PMCID: PMC10899878 DOI: 10.3389/fbioe.2024.1350135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 01/22/2024] [Indexed: 03/02/2024] Open
Abstract
Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.
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Affiliation(s)
- Carlo Dindorf
- Department of Sports Science, University of Kaiserslautern-Landau, Kaiserslautern, Germany
| | - Jonas Dully
- Department of Sports Science, University of Kaiserslautern-Landau, Kaiserslautern, Germany
| | - Jürgen Konradi
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Claudia Wolf
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Stephan Becker
- Department of Sports Science, University of Kaiserslautern-Landau, Kaiserslautern, Germany
| | - Steven Simon
- Department of Sports Science, University of Kaiserslautern-Landau, Kaiserslautern, Germany
| | - Janine Huthwelker
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frederike Werthmann
- Department of Orthopedics and Trauma Surgery, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Johanna Kniepert
- Department of Orthopedics and Trauma Surgery, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Philipp Drees
- Department of Orthopedics and Trauma Surgery, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Ulrich Betz
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Michael Fröhlich
- Department of Sports Science, University of Kaiserslautern-Landau, Kaiserslautern, Germany
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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Lv Q, Zhou J, Yang Z, He H, Chen CYC. 3D graph neural network with few-shot learning for predicting drug-drug interactions in scaffold-based cold start scenario. Neural Netw 2023; 165:94-105. [PMID: 37276813 DOI: 10.1016/j.neunet.2023.05.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/15/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Abstract
Understanding drug-drug interactions (DDI) of new drugs is critical for minimizing unexpected adverse drug reactions. The modeling of new drugs is called a cold start scenario. In this scenario, Only a few structural information or physicochemical information about new drug is available. The 3D conformation of drug molecules usually plays a crucial role in chemical properties compared to the 2D structure. 3D graph network with few-shot learning is a promising solution. However, the 3D heterogeneity of drug molecules and the discretization of atomic distributions lead to spatial confusion in few-shot learning. Here, we propose a 3D graph neural network with few-shot learning, Meta3D-DDI, to predict DDI events in cold start scenario. The 3DGNN ensures rotation and translation invariance by calculating atomic pairwise distances, and incorporates 3D structure and distance information in the information aggregation stage. The continuous filter interaction module can continuously simulate the filter to obtain the interaction between the target atom and other atoms. Meta3D-DDI further develops a FSL strategy based on bilevel optimization to transfer meta-knowledge for DDI prediction tasks from existing drugs to new drugs. In addition, the existing cold start setting may cause the scaffold structure information in the training set to leak into the test set. We design scaffold-based cold start scenario to ensure that the drug scaffolds in the training set and test set do not overlap. The extensive experiments demonstrate that our architecture achieves the SOTA performance for DDI prediction under scaffold-based cold start scenario on two real-world datasets. The visual experiment shows that Meta3D-DDI significantly improves the learning for DDI prediction of new drugs. We also demonstrate how Meta3D-DDI can reduce the amount of data required to make meaningful DDI predictions.
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Affiliation(s)
- Qiujie Lv
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Jun Zhou
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Ziduo Yang
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Haohuai He
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China
| | - Calvin Yu-Chian Chen
- School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong 518107, China; Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan.
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Ajala S, Muraleedharan Jalajamony H, Nair M, Marimuthu P, Fernandez RE. Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force. Sci Rep 2022; 12:11971. [PMID: 35831342 PMCID: PMC9279499 DOI: 10.1038/s41598-022-16114-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/05/2022] [Indexed: 11/09/2022] Open
Abstract
An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.
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Affiliation(s)
- Sunday Ajala
- Department of Engineering, Norfolk State University, Norfolk, USA
| | | | - Midhun Nair
- APJ Abdul Kalam Technological University, Thiruvananthapuram, India
| | - Pradeep Marimuthu
- Rajeev Gandhi College of Engineering and Technology, Puducherry, India
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Zhang YY, Yao YD, Cheng QQ, Huang YF, Zhou H. Establishment of a High Content Image Platform to Measure NF-κB Nuclear Translocation in LPS-Induced RAW264.7 Macrophages for Screening Anti-inflammatory Drug Candidates. Curr Drug Metab 2022; 23:394-414. [PMID: 35410593 DOI: 10.2174/1389200223666220411121614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 01/19/2022] [Accepted: 01/29/2022] [Indexed: 11/22/2022]
Abstract
BACKGROUND High content image (HCI), an automatic imaging and analysis system, provides a fast drug screening method by detecting the subcellular distribution of protein in intact cells. OBJECTIVE This study established the first standardized HCI platform for lipopolysaccharide (LPS)-induced RAW264.7 macrophages to screen anti-inflammatory compounds by measuring nuclear factor-κB (NF-κB) nuclear translocation. METHOD The influence of the cell passages, cell density, LPS induction time and concentration, antibody dilution, serum, dimethyl sulfoxide and analysis parameters on NF-κB nuclear translocation and HCI data quality was optimized. The BAY-11-7085, the positive control for inhibiting NF-κB and Western blot assay were separately employed to verify the stability and reliability of the platform. Lastly, the effect of BHA on NO release, iNOS expression, IL-1β, IL-6, and TNF-α mRNA in LPS-induced RAW264.7 cells was detected. RESULTS The optimal conditions for measuring NF-κB translocation in LPS-induced RAW264.7 cells by HCI were established. Cells that do not exceed 22 passages were seeded at a density of 10 k cells/well and pretreated with compounds following 200 ng/mL LPS for 40 min. Parameters including nuclear area of 65 μm2, cell area of 80 μm2, collar of 0.9 μm and sensitivity of 25% were recommended for image segmentation algorithms in the analysis workstation. Benzoylhypaconine from aconite was screened for the first time as an anti-inflammatory candidate by the established HCI platform. The inhibitory effect of benzoylhypaconine on NF-κB translocation was verified by Western blot. Furthermore, benzoylhypaconine reduced the release of NO, inhibited the expression of iNOS, decreased the mRNA levels of IL-1β, IL-6, and TNF-α. CONCLUSION The established HCI platform could be applied to screen anti-inflammatory compounds by measuring the NF-κB nuclear translocation in LPS-induced RAW264.7 cells.
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Affiliation(s)
- Yan-Yu Zhang
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Yun-Da Yao
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Qi-Qing Cheng
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Yu-Feng Huang
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China
| | - Hua Zhou
- Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macao, P.R. China.,Joint Laboratory for Translational Cancer Research of Chinese Medicine of the Ministry of Education of the People's Republic of China, Guangzhou University of Chinese Medicine, Guangzhou 510006, P.R. China.,Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, Zhuhai City, Guangdong Province 519000, P.R. China
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Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021; 14:245-270. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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