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Yadav S, Vora DS, Sundar D, Dhanjal JK. TCR-ESM: Employing protein language embeddings to predict TCR-peptide-MHC binding. Comput Struct Biotechnol J 2024; 23:165-173. [PMID: 38146434 PMCID: PMC10749252 DOI: 10.1016/j.csbj.2023.11.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 11/19/2023] [Accepted: 11/20/2023] [Indexed: 12/27/2023] Open
Abstract
Cognate target identification for T-cell receptors (TCRs) is a significant barrier in T-cell therapy development, which may be overcome by accurately predicting TCR interaction with peptide-bound major histocompatibility complex (pMHC). In this study, we have employed peptide embeddings learned from a large protein language model- Evolutionary Scale Modeling (ESM), to predict TCR-pMHC binding. The TCR-ESM model presented outperforms existing predictors. The complementarity-determining region 3 (CDR3) of the hypervariable TCR is located at the center of the paratope and plays a crucial role in peptide recognition. TCR-ESM trained on paired TCR data with both CDR3α and CDR3β chain information performs significantly better than those trained on data with only CDR3β, suggesting that both TCR chains contribute to specificity, the relative importance however depends on the specific peptide-MHC targeted. The study illuminates the importance of MHC information in TCR-peptide binding which remained inconclusive so far and was thought dependent on the dataset characteristics. TCR-ESM outperforms existing approaches on external datasets, suggesting generalizability. Overall, the potential of deep learning for predicting TCR-pMHC interactions and improving the understanding of factors driving TCR specificity are highlighted. The prediction model is available at http://tcresm.dhanjal-lab.iiitd.edu.in/ as an online tool.
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Affiliation(s)
- Shashank Yadav
- Department of Biomedical Engineering, University of Arizona, Tucson 85721, AZ, USA
| | - Dhvani Sandip Vora
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
| | - Durai Sundar
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Jaspreet Kaur Dhanjal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Delhi, New Delhi 110020, India
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2
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Nayar G, Altman RB. Heterogeneous network approaches to protein pathway prediction. Comput Struct Biotechnol J 2024; 23:2727-2739. [PMID: 39035835 PMCID: PMC11260399 DOI: 10.1016/j.csbj.2024.06.022] [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: 03/01/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/23/2024] Open
Abstract
Understanding protein-protein interactions (PPIs) and the pathways they comprise is essential for comprehending cellular functions and their links to specific phenotypes. Despite the prevalence of molecular data generated by high-throughput sequencing technologies, a significant gap remains in translating this data into functional information regarding the series of interactions that underlie phenotypic differences. In this review, we present an in-depth analysis of heterogeneous network methodologies for modeling protein pathways, highlighting the critical role of integrating multifaceted biological data. It outlines the process of constructing these networks, from data representation to machine learning-driven predictions and evaluations. The work underscores the potential of heterogeneous networks in capturing the complexity of proteomic interactions, thereby offering enhanced accuracy in pathway prediction. This approach not only deepens our understanding of cellular processes but also opens up new possibilities in disease treatment and drug discovery by leveraging the predictive power of comprehensive proteomic data analysis.
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Affiliation(s)
- Gowri Nayar
- Department of Biomedical Data Science, Stanford University, United States
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, United States
- Department of Genetics, Stanford University, United States
- Department of Medicine, Stanford University, United States
- Department of Bioengineering, Stanford University, United States
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3
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Picard M, Scott-Boyer MP, Bodein A, Leclercq M, Prunier J, Périn O, Droit A. Target repositioning using multi-layer networks and machine learning: The case of prostate cancer. Comput Struct Biotechnol J 2024; 24:464-475. [PMID: 38983753 PMCID: PMC11231507 DOI: 10.1016/j.csbj.2024.06.012] [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: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Julien Prunier
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Transformation and Innovation Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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4
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Aplakidou E, Vergoulidis N, Chasapi M, Venetsianou NK, Kokoli M, Panagiotopoulou E, Iliopoulos I, Karatzas E, Pafilis E, Georgakopoulos-Soares I, Kyrpides NC, Pavlopoulos GA, Baltoumas FA. Visualizing metagenomic and metatranscriptomic data: A comprehensive review. Comput Struct Biotechnol J 2024; 23:2011-2033. [PMID: 38765606 PMCID: PMC11101950 DOI: 10.1016/j.csbj.2024.04.060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024] Open
Abstract
The fields of Metagenomics and Metatranscriptomics involve the examination of complete nucleotide sequences, gene identification, and analysis of potential biological functions within diverse organisms or environmental samples. Despite the vast opportunities for discovery in metagenomics, the sheer volume and complexity of sequence data often present challenges in processing analysis and visualization. This article highlights the critical role of advanced visualization tools in enabling effective exploration, querying, and analysis of these complex datasets. Emphasizing the importance of accessibility, the article categorizes various visualizers based on their intended applications and highlights their utility in empowering bioinformaticians and non-bioinformaticians to interpret and derive insights from meta-omics data effectively.
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Affiliation(s)
- Eleni Aplakidou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nikolaos Vergoulidis
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Chasapi
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Nefeli K. Venetsianou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Maria Kokoli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Eleni Panagiotopoulou
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Department of Informatics and Telecommunications, Data Science and Information Technologies program, University of Athens, 15784 Athens, Greece
| | - Ioannis Iliopoulos
- Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Evangelos Pafilis
- Institute of Marine Biology, Biotechnology and Aquaculture (IMBBC), Hellenic Centre for Marine Research (HCMR), Heraklion, Greece
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Nikos C. Kyrpides
- DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
- Center of New Biotechnologies & Precision Medicine, Department of Medicine, School of Health Sciences, National and Kapodistrian University of Athens, Greece
- Hellenic Army Academy, 16673 Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
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5
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Jin C, Jia C, Hu W, Xu H, Shen Y, Yue M. Predicting antimicrobial resistance in E. coli with discriminative position fused deep learning classifier. Comput Struct Biotechnol J 2024; 23:559-565. [PMID: 38274998 PMCID: PMC10809114 DOI: 10.1016/j.csbj.2023.12.041] [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: 10/04/2023] [Revised: 12/26/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Escherichia coli (E. coli) has become a particular concern due to the increasing incidence of antimicrobial resistance (AMR) observed worldwide. Using machine learning (ML) to predict E. coli AMR is a more efficient method than traditional laboratory testing. However, further improvement in the predictive performance of existing models remains challenging. In this study, we collected 1937 high-quality whole genome sequencing (WGS) data from public databases with an antimicrobial resistance phenotype and modified the existing workflow by adding an attention mechanism to enable the modified workflow to focus more on core single nucleotide polymorphisms (SNPs) that may significantly lead to the development of AMR in E. coli. While comparing the model performance before and after adding the attention mechanism, we also performed a cross-comparison among the published models using random forest (RF), support vector machine (SVM), logistic regression (LR), and convolutional neural network (CNN). Our study demonstrates that the discriminative positional colors of Chaos Game Representation (CGR) images can selectively influence and highlight genome regions without prior knowledge, enhancing prediction accuracy. Furthermore, we developed an online tool (https://github.com/tjiaa/E.coli-ML/tree/main) for assisting clinicians in the rapid prediction of the AMR phenotype of E. coli and accelerating clinical decision-making.
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Affiliation(s)
- Canghong Jin
- School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China
| | - Chenghao Jia
- Institute of Preventive Veterinary Sciences and Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou 310058, China
| | - Wenkang Hu
- School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Haidong Xu
- School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China
| | - Yanyi Shen
- School of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, China
| | - Min Yue
- Institute of Preventive Veterinary Sciences and Department of Veterinary Medicine, Zhejiang University College of Animal Sciences, Hangzhou 310058, China
- Hainan Institute of Zhejiang University, Sanya 572000, China
- Zhejiang Provincial Key Laboratory of Preventive Veterinary Medicine, Hangzhou 310058, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, China
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6
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Zhang Q, He Y, Lu YP, Wei QH, Zhang HY, Quan Y. GETdb: A comprehensive database for genetic and evolutionary features of drug targets. Comput Struct Biotechnol J 2024; 23:1429-1438. [PMID: 38616961 PMCID: PMC11015738 DOI: 10.1016/j.csbj.2024.04.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 03/25/2024] [Accepted: 04/01/2024] [Indexed: 04/16/2024] Open
Abstract
The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.
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Affiliation(s)
- Qi Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Yang He
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Ya-Ping Lu
- Sinopharm Genomics Technology Co., Ltd., Wuhan 430030, PR China
- Sinopharm Medical Laboratory (Wuhan) Co., Ltd., Wuhan 430030, PR China
| | - Qi-Hao Wei
- Sinopharm (Wuhan) Precision Medical Technology Co., Ltd., Wuhan 430030, PR China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
| | - Yuan Quan
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, PR China
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7
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Bizzotto E, Zampieri G, Treu L, Filannino P, Di Cagno R, Campanaro S. Classification of bioactive peptides: A systematic benchmark of models and encodings. Comput Struct Biotechnol J 2024; 23:2442-2452. [PMID: 38867723 PMCID: PMC11168199 DOI: 10.1016/j.csbj.2024.05.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/10/2024] [Accepted: 05/22/2024] [Indexed: 06/14/2024] Open
Abstract
Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on time-consuming in vitro tests. While bioinformatic tools for the identification of bioactive peptides are available, they are focused on specific functional classes and have not been systematically tested on realistic settings. To tackle this problem, bioactive peptide sequences and functions were here gathered from a variety of databases to generate a unified collection of bioactive peptides from microbial fermentation. This collection was organized into nine functional classes including some previously studied and some unexplored such as immunomodulatory, opioid and cardiovascular peptides. Upon assessing their sequence properties, four alternative encoding methods were tested in combination with a multitude of machine learning algorithms, from basic classifiers like logistic regression to advanced algorithms like BERT. Tests on a total of 171 models showed that, while some functions are intrinsically easier to detect, no single combination of classifiers and encoders worked universally well for all classes. For this reason, we unified all the best individual models for each class and generated CICERON (Classification of bIoaCtive pEptides fRom micrObial fermeNtation), a classification tool for the functional classification of peptides. State-of-the-art classifiers were found to underperform on our realistic benchmark dataset compared to the models included in CICERON. Altogether, our work provides a tool for real-world peptide classification and can serve as a benchmark for future model development.
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Affiliation(s)
- Edoardo Bizzotto
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
| | - Guido Zampieri
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
| | - Laura Treu
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
| | - Pasquale Filannino
- Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Via G. Amendola 165/a, Bari 70126, Italy
| | - Raffaella Di Cagno
- Faculty of Agricultural, Environmental and Food Sciences, Free University of Bolzano, Piazza Universita, 5, Bolzano 39100, Italy
| | - Stefano Campanaro
- Department of Biology, University of Padua, Via U. Bassi 58/b, Padova 35131, Italy
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8
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Zheng F, Jiang X, Wen Y, Yang Y, Li M. Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength. Comput Struct Biotechnol J 2024; 23:460-472. [PMID: 38235359 PMCID: PMC10792694 DOI: 10.1016/j.csbj.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 01/19/2024] Open
Abstract
The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.
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Affiliation(s)
- Feifan Zheng
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Xin Jiang
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yuhao Wen
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Yan Yang
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
| | - Minghui Li
- MOE Key Laboratory of Geriatric Diseases and Immunology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, Jiangsu Province 215123, China
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9
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Li F, Shen J, Yang Q, Wei Y, Zuo Y, Wang Y, Ning J, Li L. Monitoring quality changes in green tea during storage: A hyperspectral imaging method. Food Chem X 2024; 23:101538. [PMID: 39071927 PMCID: PMC11280024 DOI: 10.1016/j.fochx.2024.101538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/19/2024] [Accepted: 06/04/2024] [Indexed: 07/30/2024] Open
Abstract
The quality of green tea deteriorates the longer it is stored. However, there is a lack of accurate and rapid methods for determining the storage period of tea. In this study, hyperspectral imaging (HSI) was used to determine the storage period of green teas stored at 4 °C (set 1) and 25 °C (set 2), and to quantify and visualize the main chemical components (e.g. catechins). In this study, three prediction algorithms were compared, in which partial least squares discriminant analysis outperformed the other models in qualitative discrimination, with 98% and 96% correct discrimination for two sets, respectively. Moreover, quantitative models for ester catechins, simple catechins, and total catechins were developed with Rp > 0.90 and RPD > 1.0, indicating that the models were reliable. Further, a more intuitive visualization of catechin content was achieved. In conclusion, the HSI provides a rapid, non-destructive method to determine the freshness of stored green tea.
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Affiliation(s)
| | | | - Qianfeng Yang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yongning Wei
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yifan Zuo
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Yujie Wang
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Jingming Ning
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
| | - Luqing Li
- State Key Laboratory of Tea Plant Biology and Utilization, Anhui Agricultural University, Hefei 230036, China
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10
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Becchi PP, Lolli V, Zhang L, Pavanello F, Caligiani A, Lucini L. Integration of LC-HRMS and 1H NMR metabolomics data fusion approaches for classification of Amarone wine based on withering time and yeast strain. Food Chem X 2024; 23:101607. [PMID: 39071933 PMCID: PMC11279979 DOI: 10.1016/j.fochx.2024.101607] [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: 01/02/2024] [Revised: 06/14/2024] [Accepted: 07/01/2024] [Indexed: 07/30/2024] Open
Abstract
Two untargeted metabolomics approaches (LC-HRMS and 1H NMR) were combined to classify Amarone wines based on grape withering time and yeast strain. The study employed a multi-omics data integration approach, combining unsupervised data exploration (MCIA) and supervised statistical analysis (sPLS-DA). The results revealed that the multi-omics pseudo-eigenvalue space highlighted a limited correlation between the datasets (RV-score = 16.4%), suggesting the complementarity of the assays. Furthermore, the sPLS-DA models correctly classified wine samples according to both withering time and yeast strains, providing a much broader characterization of wine metabolome with respect to what was obtained from the individual techniques. Significant variations were notably observed in the accumulation of amino acids, monosaccharides, and polyphenolic compounds throughout the withering process, with a lower error rate in sample classification (7.52%). In conclusion, this strategy demonstrated a high capability to integrate large omics datasets and identify key metabolites able to discriminate wine samples based on their characteristics.
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Affiliation(s)
- Pier Paolo Becchi
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Veronica Lolli
- Department of Food and Drug, University of Parma, Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Leilei Zhang
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | | | - Augusta Caligiani
- Department of Food and Drug, University of Parma, Parco Area delle Scienze 27/A, 43124 Parma, Italy
| | - Luigi Lucini
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
- CRAST research centre, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
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11
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Meng L, Zhou B, Liu H, Chen Y, Yuan R, Chen Z, Luo S, Chen H. Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174201. [PMID: 38936709 DOI: 10.1016/j.scitotenv.2024.174201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
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Affiliation(s)
- Lingxuan Meng
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Beihai Zhou
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haijun Liu
- School of Resources and Environment, Anqing Normal University, Anqing, China.
| | - Yuefang Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Rongfang Yuan
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhongbing Chen
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| | - Shuai Luo
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huilun Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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12
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Luo C, Mao B, Wu Y, He Y. The research hotspots and theme trends of artificial intelligence in nurse education: A bibliometric analysis from 1994 to 2023. NURSE EDUCATION TODAY 2024; 141:106321. [PMID: 39084073 DOI: 10.1016/j.nedt.2024.106321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 07/09/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024]
Abstract
OBJECTIVES To explore research hotspots and theme trends in artificial intelligence in nurse education using bibliometric analysis. DESIGN Bibliometric analysis. DATA SOURCES Literature from the Web of Science Core Collection from the time of construction to October 31, 2023 was searched. REVIEW METHODS Analyses of countries, authors, institutions, journals, and keywords were conducted using Bibliometrix (based on R language), CiteSpace, the online analysis platform (bibliometric), Vosviewer, and Pajek. RESULTS A total of 135 articles with a straight upward trend over the last three years were retrieved. By fitting the curve R2 = 0.6022 (R2 > 0.4), we predicted that the number of annual articles is projected to grow in the coming years. The United States (n = 38), the National University of Singapore (n = 16), Professor Jun Ota (n = 8), and Nurse Education Today (n = 14) are the countries, institutions, authors, and journals that contributed to the most publications, respectively. Collaborative network analysis revealed that 32 institutional and 64 author collaborative teams were established. We identified ten high-frequency keywords and nine clusters. We categorized the research hotspots of artificial intelligence in nurse education into three areas: (1) Artificial intelligence-enhanced simulation robots, (2) machine learning and data mining, and (3) large language models based on natural language processing and deep learning. By analyzing the temporal and spatial evolution of keywords and burst detection, we found that future research trends may include (1) expanding and deepening the application of AI technology, (2) assessment of behavioral intent and educational outcomes, and (3) moral and ethical considerations. CONCLUSIONS Future research should be conducted on technology applications, behavioral intent, ethical policy, international cooperation, interdisciplinary cooperation, and sustainability to promote the continued development and innovation of AI in nurse education.
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Affiliation(s)
- Chuhong Luo
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Bin Mao
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Ying Wu
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China
| | - Ying He
- School of Medicine, Hunan Normal University, Changsha, Hunan, People's Republic of China.
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13
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Zhang L, Vaccari F, Bandini F, Puglisi E, Trevisan M, Lucini L. The short-term effect of microplastics in lettuce involves size- and dose-dependent coordinate shaping of root metabolome, exudation profile and rhizomicrobiome. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:174001. [PMID: 38879040 DOI: 10.1016/j.scitotenv.2024.174001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 06/02/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024]
Abstract
Micro- and nano-plastics (MNPs) in the soil can impact the microbial diversity within rhizospheres and induce modifications in plants' morphological, physiological, and biochemical parameters. However, a significant knowledge gap still needs to be addressed regarding the specific effects of varying particle sizes and concentrations on the comprehensive interplay among soil dynamics, root exudation, and the overall plant system. In this sense, different omics techniques were employed to clarify the mechanisms of the action exerted by four different particle sizes of polyethylene plastics considering four different concentrations on the soil-roots exudates-plant system was studied using lettuce (Lactuca sativa L. var. capitata) as a model plant. The impact of MNPs was investigated using a multi-omics integrated approach, focusing on the tripartite interaction between the root metabolic process, exudation pattern, and rhizosphere microbial modulation. Our results showed that particle size and their concentrations significantly modulated the soil-roots exudates-plant system. Untargeted metabolomics highlighted that fatty acids, amino acids, and hormone biosynthesis pathways were significantly affected by MNPs. Additionally, they were associated with the reduction of rhizosphere bacterial α-diversity, following a size-dependent trend for specific taxa. The omics data integration highlighted a correlation between Pseudomonadata and Actinomycetota phyla and Bacillaceae family (Peribacillus simplex) and the exudation of flavonoids, phenolic acids, and lignans in lettuce exposed to increasing sizes of MNPs. This study provides a novel insight into the potential effects of different particle sizes and concentrations of MNPs on the soil-plant continuum, providing evidence about size- and concentration-dependent effects, suggesting the need for further investigation focused on medium- to long-term exposure.
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Affiliation(s)
- Leilei Zhang
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Filippo Vaccari
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Francesca Bandini
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Edoardo Puglisi
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Marco Trevisan
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Luigi Lucini
- Department for Sustainable Food Process, Università Cattolica del Sacro Cuore, Piacenza, Italy.
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14
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Kuligowski J, Moreno-Torres M, Quintás G. Improving insights from metabolomic functional analysis combining multivariate tools. Anal Chim Acta 2024; 1323:343062. [PMID: 39182979 DOI: 10.1016/j.aca.2024.343062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/31/2024] [Accepted: 08/05/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Metabolomics is a scientific field that relies on the comprehensive analysis of metabolites to provide direct insights into functional processes in biological systems. Metabolomic data provides valuable insights into the functional processes of biological systems, often analyzed through univariate and multivariate approaches, and well as with functional or pathway analysis using different methods such as mummichog. Yet, the integration of results from these sources to aid the interpretation of their biological significance remains challenging. This represents a significant bottleneck limiting the applicability of multivariate analysis of metabolomic data, despite its potential for providing deep biological insights. RESULTS In this work we propose two straightforward methods to facilitate the interpretation of results from multivariate analysis and functional metabolic analysis using: i) p-values from multivariate tests as input in functional analysis, and ii) cluster-CV to assess the impact on the predictive performance of a multivariate model at the pathway level. Four simulated data sets were analyzed including a data set with no class separation, and three data sets with a statistically significant discrimination between classes by including either univariate, multivariate, or both types of discriminant effects. The data sets were analyzed using univariate tests and OPLS-DA. Furthermore, p-values for each feature estimated by univariate analysis and OPLS-DA were used as input for functional analysis in mummichog. Cluster-CV was then used to assess the effect of detected metabolic pathways on the class separation observed by OPLS-DA. SIGNIFICANCE Through simulated data, we show how these approaches enhance the interpretation of biological effects driving multivariate models and support the identification of altered pathways not detected by univariate analysis. By providing a deeper understanding of metabolic phenotypes, these methods might improve the biological insights derived from statistical and functional analysis of future or previous studies.
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Affiliation(s)
- Julia Kuligowski
- Neonatal Research Group, Health Research Institute La Fe (IISLAFE), Avda Fernando Abril Martorell 106, 46026, Valencia, Spain; Primary Care Interventions to Prevent Maternal and Child Chronic Diseases of Perinatal and Developmental Origin Network (RICORS-SAMID), Instituto de Salud Carlos III, Madrid, Spain; Servicio de Análisis de Vesículas Extracelulares (SAVE), Health Research Institute La Fe (IISLAFE), Avda Fernando Abril Martorell 106, 46026, Valencia, Spain
| | - Marta Moreno-Torres
- Departamento de Bioquímica y Biología Molecular, Universidad de Valencia, Valencia, Spain; Unit for Experimental Hepatology, Health Research Institute La Fe (IISLAFE), Valencia, Spain; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain
| | - Guillermo Quintás
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, Madrid, Spain; Health and Biomedicine Department, Leitat Technological Center, Barcelona, Spain.
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15
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Amanollahi M, Jameie M, Looha MA, A Basti F, Cattarinussi G, Moghaddam HS, Di Camillo F, Akhondzadeh S, Pigoni A, Sambataro F, Brambilla P, Delvecchio G. Machine learning applied to the prediction of relapse, hospitalization, and suicide in bipolar disorder using neuroimaging and clinical data: A systematic review. J Affect Disord 2024; 361:778-797. [PMID: 38908556 DOI: 10.1016/j.jad.2024.06.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/22/2024] [Accepted: 06/17/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is associated with increased morbidity/mortality. Adverse outcome prediction might help with the management of patients with BD. METHODS We systematically reviewed the performance of machine learning (ML) studies in predicting adverse outcomes (relapse or recurrence, hospital admission, and suicide-related events) in patients with BD. Demographic, clinical, and neuroimaging-related poor outcome predictors were also reviewed. Three databases (PubMed, Scopus, and Web of Science) were explored from inception to July 2023. RESULTS Eighteen studies, accounting for >30,000 patients, were included. Support vector machine, decision trees, random forest, and logistic regression were the most frequently used ML algorithms. ML models' area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity ranged from 0.71 to 0.98, 72.7-92.8 %, and 59.0-95.2 % for relapse/recurrence prediction (4 studies (3 on relapses and 1 on recurrences). The corresponding values were 0.78-0.88, 21.4-100 %, and 77.0-99.7 % for hospital admissions (3 studies, 21,266 patients), and 0.71-0.99, 44.4-97.9 %, and 38.9-95.0 % for suicide-related events (10 studies, 5558 patients). Also, one study addressed a combination of the interest outcomes. Adverse outcome predictors included early onset BD, BD type I, comorbid psychiatric or substance use disorder, circadian rhythm disruption, hospitalization characteristics, and neuroimaging parameters, including increased dynamic amplitude of low-frequency fluctuation, decreased frontolimbic functional connectivity and aberrant dynamic functional connectivity in corticostriatal circuitry. CONCLUSIONS ML models can predict adverse outcomes of BD with relatively acceptable performance measures. Future studies with larger samples and nested cross-validation validation should be conducted to reach more reliable results.
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Affiliation(s)
- Mobina Amanollahi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Melika Jameie
- Neuroscience Research Center, Iran University of Medical Sciences, Tehran, Iran; Iranian Center of Neurological Research, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehdi Azizmohammad Looha
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Fatemeh A Basti
- Islamic Azad University, Tehran Medical Branch, Tehran, Iran
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Hossein Sanjari Moghaddam
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Fabio Di Camillo
- Department of Neuroscience (DNS), University of Padova, Padua, Italy
| | - Shahin Akhondzadeh
- Psychiatry and Psychology Research Center, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Fabio Sambataro
- Department of Neuroscience (DNS), University of Padova, Padua, Italy; Padova Neuroscience Center, University of Padova, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
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16
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Zhang L, Yang H, Zhou C, Li Y, Long Z, Li Q, Zhang J, Qin X. Artificial intelligence-driven multiomics predictive model for abdominal aortic aneurysm subtypes to identify heterogeneous immune cell infiltration and predict disease progression. Int Immunopharmacol 2024; 138:112608. [PMID: 38981221 DOI: 10.1016/j.intimp.2024.112608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/23/2024] [Accepted: 06/29/2024] [Indexed: 07/11/2024]
Abstract
BACKGROUND Abdominal aortic aneurysm (AAA) poses a significant health risk and is influenced by various compositional features. This study aimed to develop an artificial intelligence-driven multiomics predictive model for AAA subtypes to identify heterogeneous immune cell infiltration and predict disease progression. Additionally, we investigated neutrophil heterogeneity in patients with different AAA subtypes to elucidate the relationship between the immune microenvironment and AAA pathogenesis. METHODS This study enrolled 517 patients with AAA, who were clustered using k-means algorithm to identify AAA subtypes and stratify the risk. We utilized residual convolutional neural network 200 to annotate and extract contrast-enhanced computed tomography angiography images of AAA. A precise predictive model for AAA subtypes was established using clinical, imaging, and immunological data. We performed a comparative analysis of neutrophil levels in the different subgroups and immune cell infiltration analysis to explore the associations between neutrophil levels and AAA. Quantitative polymerase chain reaction, Western blotting, and enzyme-linked immunosorbent assay were performed to elucidate the interplay between CXCL1, neutrophil activation, and the nuclear factor (NF)-κB pathway in AAA pathogenesis. Furthermore, the effect of CXCL1 silencing with small interfering RNA was investigated. RESULTS Two distinct AAA subtypes were identified, one clinically more severe and more likely to require surgical intervention. The CNN effectively detected AAA-associated lesion regions on computed tomography angiography, and the predictive model demonstrated excellent ability to discriminate between patients with the two identified AAA subtypes (area under the curve, 0.927). Neutrophil activation, AAA pathology, CXCL1 expression, and the NF-κB pathway were significantly correlated. CXCL1, NF-κB, IL-1β, and IL-8 were upregulated in AAA. CXCL1 silencing downregulated NF-κB, interleukin-1β, and interleukin-8. CONCLUSION The predictive model for AAA subtypes demonstrated accurate and reliable risk stratification and clinical management. CXCL1 overexpression activated neutrophils through the NF-κB pathway, contributing to AAA development. This pathway may, therefore, be a therapeutic target in AAA.
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Affiliation(s)
- Lin Zhang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Han Yang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Chenxing Zhou
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Yao Li
- Liuzhou People's Hospital, Liuzhou, Guangxi, PR China
| | - Zhen Long
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Que Li
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Jiangfeng Zhang
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China
| | - Xiao Qin
- The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, PR China.
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17
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Adebanji AO, Asare C, Gyamerah SA. Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective. Int J Med Inform 2024; 189:105529. [PMID: 38905958 DOI: 10.1016/j.ijmedinf.2024.105529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 06/11/2024] [Accepted: 06/18/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND Recent studies reveal that around 1.9 million stillbirths occur annually worldwide, with Sub-Saharan Africa having among the highest cases. Some Sub-Saharan African countries, including Ghana, failed to meet Millennium Development Goal 5 (MDG5) by 2015 and may struggle to meet Sustainable Development Goal 3 (SDG3) despite maternal healthcare interventions. Concerns arise about Ghana's ability to achieve the World Health Organization's neonatal mortality goal of 12 per 1000 live births by 2030. This study aims to identify key factors influencing childbirth outcomes and create a predictive method for high-risk pregnancies. METHODS We compared four machine learning classifiers (Extreme Gradient Boosting, Random Forest, Logistic Regression, and Artificial Neural Network) in predicting childbirth outcomes using data from a tertiary health facility in Ghana. To address class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). RESULTS Our findings show that fetal heartbeat, gestation age at birth are the most influential factors on birth outcome (stillbirth or live birth), while there is no significant association with maternal age, number of babies, and type of delivery method. Among the machine learning models considered, Random Forest emerged as the optimal model achieving an accuracy, F1-score, and AUC values of approximately 0.98, 0.99, and 0.90 respectively. CONCLUSION Our study identifies key factors affecting childbirth outcomes and highlights the potential of machine learning for early high-risk pregnancy detection in clinical settings. These findings are crucial for Ghana and other Sub-Saharan African countries striving to meet maternal and neonatal healthcare goals. Further research and policy initiatives can use these results to improve healthcare in the region and work toward the World Health Organization's objectives by 2030.
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Affiliation(s)
- Atinuke Olusola Adebanji
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Clement Asare
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
| | - Samuel Asante Gyamerah
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
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18
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Spoladore D, Stella F, Tosi M, Lorenzini EC, Bettini C. A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy. Comput Biol Med 2024; 180:109001. [PMID: 39126791 DOI: 10.1016/j.compbiomed.2024.109001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 07/12/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
BACKGROUND Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems. METHOD This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient's phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface. RESULTS Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively). CONCLUSIONS Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.
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Affiliation(s)
- Daniele Spoladore
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (Cnr), Lecco, Italy.
| | - Francesco Stella
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (Cnr), Lecco, Italy; Department of Computer Science, University of Milan, Milan, Italy.
| | - Martina Tosi
- Department of Health Sciences, University of Milan, Milan, Italy.
| | | | - Claudio Bettini
- Department of Computer Science, University of Milan, Milan, Italy.
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Dang CC, Jin YZ, Tan X, Nie WB, Lu Y, Liu BF, Xing DF, Ren NQ, Xie GJ. Nitrite-driven anaerobic ethane oxidation. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 21:100438. [PMID: 39036799 PMCID: PMC11259786 DOI: 10.1016/j.ese.2024.100438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/08/2024] [Accepted: 06/08/2024] [Indexed: 07/23/2024]
Abstract
Ethane, the second most abundant gaseous hydrocarbon in vast anoxic environments, is an overlooked greenhouse gas. Microbial anaerobic oxidation of ethane can be driven by available electron acceptors such as sulfate and nitrate. However, despite nitrite being a more thermodynamically feasible electron acceptor than sulfate or nitrate, little is known about nitrite-driven anaerobic ethane oxidation. In this study, a microbial culture capable of nitrite-driven anaerobic ethane oxidation was enriched through the long-term operation of a nitrite-and-ethane-fed bioreactor. During continuous operation, the nitrite removal rate and the theoretical ethane oxidation rate remained stable at approximately 25.0 mg NO2 -N L-1 d-1 and 11.48 mg C2H6 L-1 d-1, respectively. Batch tests demonstrated that ethane is essential for nitrite removal in this microbial culture. Metabolic function analysis revealed that a species affiliated with a novel genus within the family Rhodocyclaceae, designated as 'Candidatus Alkanivoras nitrosoreducens', may perform the nitrite-driven anaerobic ethane oxidation. In the proposed metabolic model, despite the absence of known genes for ethane conversion to ethyl-succinate and succinate-CoA ligase, 'Ca. A. nitrosoreducens' encodes a prospective fumarate addition pathway for anaerobic ethane oxidation and a complete denitrification pathway for nitrite reduction to nitrogen. These findings advance our understanding of nitrite-driven anaerobic ethane oxidation, highlighting the previously overlooked impact of anaerobic ethane oxidation in natural ecosystems.
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Affiliation(s)
- Cheng-Cheng Dang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Yin-Zhu Jin
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Xin Tan
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Wen-Bo Nie
- Key Laboratory of the Three Gorges Region's Eco-Environment, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing, 400044, China
| | - Yang Lu
- Water Innovation and Smart Environment Laboratory, School of Civil and Environmental Engineering, Faculty of Engineering, Queensland University of Technology, Brisbane, Queensland, 4001, Australia
| | - Bing-Feng Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - De-Feng Xing
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Nan-Qi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Guo-Jun Xie
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
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Tian J, Zhao B, Wang J, Du W, Ma W, Xia B, Xu H, Chen T, He X, Qin M. The short-term impact of comprehensive caries treatment on the supragingival microbiome of severe early childhood caries. Int J Paediatr Dent 2024; 34:505-515. [PMID: 38173170 DOI: 10.1111/ipd.13151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/28/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Children affected by severe early childhood caries (S-ECC) usually need comprehensive caries treatment due to the extensive of caries. How the oral microbiome changes after caries therapy within the short-term warrant further study. AIM This study aimed to investigate the short-term impact of comprehensive caries treatment on the supragingival plaque microbiome of S-ECC children. DESIGN Thirty-three children aged 2-4 years with severe caries (dt > 7) were recruited. Comprehensive caries treatment was performed under general anesthesia in one session and included restoration, pulp treatment, extraction, and fluoride application. Supragingival plaque was sampled pre- and 1-month posttreatment. The genomic DNA of the supragingival plaque was extracted, and bacterial 16S ribosomal RNA gene sequencing was performed. RESULTS Our data showed that the microbial community evenness significantly decreased posttreatment. Furthermore, comprehensive caries treatment led to more diverse microbial structures among the subjects. The interbacterial interactions reflected by the microbial community's co-occurrence network tended to be less complex posttreatment. Caries treatment increased the relative abundance of Corynebacterium matruchotii, Corynebacterium durum, Actinomyces naeslundii, and Saccharibacteria HMT-347, as well as Aggregatibacter HMT-458 and Haemophilus influenzae. Meanwhile, the relative abundance of Streptococcus mutans, three species from Leptotrichia, Neisseria bacilliformis, and Provotella pallens significantly decreased posttreatment. CONCLUSION Our results suggested that comprehensive caries treatment may contribute to the reconstruction of a healthier supragingival microbiome.
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Affiliation(s)
- Jing Tian
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - Bingqian Zhao
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - Jingyan Wang
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - Wenbin Du
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China
| | - Wenli Ma
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - Bin Xia
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - He Xu
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
| | - Tsute Chen
- Department of Microbiology, The Forsyth Institute, Cambridge, Massachusetts, USA
- Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - Xuesong He
- Department of Microbiology, The Forsyth Institute, Cambridge, Massachusetts, USA
- Department of Oral Medicine, Infection and Immunity, Harvard School of Dental Medicine, Boston, Massachusetts, USA
| | - Man Qin
- Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Laboratory for Digital and Material Technology of Stomatology, Beijing Key Laboratory of Digital Stomatology, Beijing, China
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Ferdowsi M, Hasan MM, Habib W. Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 254:108289. [PMID: 38905988 DOI: 10.1016/j.cmpb.2024.108289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 06/10/2024] [Accepted: 06/16/2024] [Indexed: 06/23/2024]
Abstract
BACKGROUND AND OBJECTIVE Cardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In healthcare, artificial intelligence (AI) holds promise for advancing disease risk assessment and treatment outcome prediction. However, machine learning (ML) evolution raises concerns about data privacy and biases, especially in sensitive healthcare applications. The objective is to develop and implement a responsible AI model for CD prediction that prioritize patient privacy, security, ensuring transparency, explainability, fairness, and ethical adherence in healthcare applications. METHODS To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions. RESULTS Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development. CONCLUSIONS Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.
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Affiliation(s)
- Mahbuba Ferdowsi
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman (UTAR), Kajang, Selangor 43200, Malaysia.
| | - Md Mahmudul Hasan
- School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, NSW 2052, Australia
| | - Wafa Habib
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya (UM), Kuala Lumpur 50603, Malaysia
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Pham DT, Tran TD. Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes. Comput Biol Med 2024; 179:108888. [PMID: 39047507 DOI: 10.1016/j.compbiomed.2024.108888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 06/15/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024]
Abstract
There are no tools to identify driver nodes of large-scale networks in approach of competition-based controllability. This study proposed a novel method for this computation of large-scale networks. It implemented the method in a new Cytoscape plug-in app called Drivergene.net. Experiments of the software on large-scale biomolecular networks have shown outstanding speed and computing power. Interestingly, 86.67% of the top 10 driver nodes found on these networks are anticancer drug target genes that reside mostly at the innermost K-cores of the networks. Finally, compared method with those of five other researchers and confirmed that the proposed method outperforms the other methods on identification of anticancer drug target genes. Taken together, Drivergene.net is a reliable tool that efficiently detects not only drug target genes from biomolecular networks but also driver nodes of large-scale complex networks. Drivergene.net with a user manual and example datasets are available https://github.com/tinhpd/Drivergene.git.
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Affiliation(s)
- Duc-Tinh Pham
- Complex Systems and Bioinformatics Lab, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam; Graduate University of Science and Technology, Academy of Science and Technology Viet Nam, 18 Hoang Quoc Viet Street, Cau Giay District, Hanoi, Viet Nam
| | - Tien-Dzung Tran
- Complex Systems and Bioinformatics Lab, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam; Faculty of Information and Communication Technology, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam.
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23
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Zhu C, Zhang M, Gong S, Du J, Ma L, Liu Y, Li Y, Yu J, Liu N. Identification of Matrine as a Kirsten rats Arcomaviral oncogene homolog inhibitor alleviating chemotherapy-induced neuropathic pain. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2024; 132:155841. [PMID: 38971025 DOI: 10.1016/j.phymed.2024.155841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 06/13/2024] [Accepted: 06/20/2024] [Indexed: 07/08/2024]
Abstract
BACKGROUND Chemotherapy-induced peripheral neuropathy (CIPN) represents a prevailing and severe clinical concern, characterized by limited availability of clinically effective treatment strategies. Current evidence endorses matrine's potential as a neuroprotective and analgesic agent for CIPN. Nevertheless, the precise targets and mechanisms of action of matrine remain insufficiently explored, impeding comprehensive pharmacological investigation and clinical application. OBJECTIVE This study endeavors to elucidate the analgesic and neuroprotective effects of matrine in mice with vincristine-induced neuropathic pain. A focal point is the identification of matrine's specific target and the underlying molecular mechanisms governing its analgesic and neuroprotective actions. METHODS To discern matrine's analgesic effects in CIPN mice, we conducted behavioral experiments encompassing the Von Frey filament test and Hargreaves Test. Furthermore, we conducted electrophysiological and histopathological assessments involving HE staining, Nissl staining, and Fluoro-Jade B staining to evaluate matrine's effects on neuroprotection within dorsal root ganglia and the spinal cord of CIPN mice. Sequentially, thermal shift assay, GTP hydrolysis assay, and nucleotide exchange assay were executed to validate matrine's inhibitory effects on KRAS. Molecular docking and site-directed mutagenesis experiments were implemented to identify the precise binding pocket of matrine on KRAS. Lastly, matrine's inhibitory effects on downstream signaling pathways of KRAS were confirmed through experiments conducted at animal model. RESULTS Matrine exhibited a notable increase in mechanical withdrawal threshold and thermal withdrawal latency in vincristine-treated mice. This compound substantially ameliorated the neurofunctional blockade associated with sensory and motor functions induced by vincristine. Moreover, matrine mitigated pathological damage within DRG and the L4-L5 spinal cord regions. The study's MST experiments indicated matrine's substantial elevation of KRAS's melting temperature. The GTP hydrolysis and nucleotide exchange assays revealed concentration-dependent inhibition of KRAS activity by matrine. Molecular docking provided insight into the binding mode of matrine with KRAS, while site-directed mutagenesis verified the specific binding site of matrine on KRAS. Lastly, matrine's inhibition of downstream Raf/Erk1/2 and PI3K/Akt/mTOR signaling pathways of KRAS was confirmed in VCR mice. CONCLUSION Compared to previous studies, our research has identified matrine as a natural inhibitor of the elusive protein KRAS, often considered "undruggable." Furthermore, this study has revealed that matrine exerts its therapeutic effects on chemotherapy-induced peripheral neuropathy (CIPN) by inhibiting KRAS activation, subsequently suppressing downstream signaling pathways such as Raf/Erk1/2 and PI3K/Akt/mTOR. This investigation signifies the discovery of a novel target for matrine, thus expanding the potential scope of its involvement in KRAS-related biological functions and diseases. These findings hold the promise of providing a crucial experimental foundation for forthcoming drug development initiatives centered around matrine, thereby advancing the field of pharmaceutical research.
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Affiliation(s)
- Chunhao Zhu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, PR China; Medicinal Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, PR China
| | - Mengting Zhang
- People's Hospital of Ningxia Hui Autonomous Region, Yinchuan 750004, PR China
| | - Shuaishuai Gong
- Department of Pharmaceutics, China Pharmaceutical University, Nanjing 210009, PR China
| | - Juan Du
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, PR China
| | - Lin Ma
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, PR China
| | - Yue Liu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, PR China
| | - Yuxiang Li
- School of nursing, Ningxia Medical University, Yinchuan 750004, Ningxia, PR China
| | - Jianqiang Yu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, PR China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan 750004, PR China.
| | - Ning Liu
- School of Pharmacy, Ningxia Medical University, Yinchuan 750004, PR China; Ningxia Key Laboratory of Drug Development and Generic Drug Research, Ningxia Medical University, Yinchuan 750004, PR China.
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24
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Ferrer Florensa A, Almagro Armenteros J, Nielsen H, Aarestrup F, Clausen P. SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects. NAR Genom Bioinform 2024; 6:lqae106. [PMID: 39157582 PMCID: PMC11327874 DOI: 10.1093/nargab/lqae106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 07/26/2024] [Accepted: 08/05/2024] [Indexed: 08/20/2024] Open
Abstract
The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database partition method for machine learning that can scale to most biological sequences (genes, proteins and genomes) in order to avoid data leakage between sets. We also explore the effect of not restraining similarity between sets by reproducing the development of two state-of-the-art models on bioinformatics, not only confirming the consequences of randomly splitting databases on the model assessment, but expanding those repercussions to the model development. SpanSeq is available at https://github.com/genomicepidemiology/SpanSeq.
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Affiliation(s)
- Alfred Ferrer Florensa
- Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
| | - Jose Juan Almagro Armenteros
- Informatics and Predictive Sciences Research, Bristol Myers Squibb Company, Calle Isaac Newton 4, 41092 Sevilla, Spain
| | - Henrik Nielsen
- Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
| | - Frank Møller Aarestrup
- Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
| | - Philip Thomas Lanken Conradsen Clausen
- Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark
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Bogdanova EA, Novoseletsky VN. ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes. Proteins 2024; 92:1127-1136. [PMID: 38722047 DOI: 10.1002/prot.26700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/22/2024] [Accepted: 04/26/2024] [Indexed: 08/07/2024]
Abstract
Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.
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26
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Elnabwy MT, Alshahri AH, El-Gamal AA. An integrated deep learning approach for modeling dissolved oxygen concentration at coastal inlets based on hydro-climatic parameters. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 367:122018. [PMID: 39111007 DOI: 10.1016/j.jenvman.2024.122018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 07/15/2024] [Accepted: 07/26/2024] [Indexed: 08/15/2024]
Abstract
Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this phenomenon, predicting and modeling DO variation is a challenging process. Accordingly, this study introduces an innovative Deep Learning Neural Network (DLNN) methodology to model and predict DO concentrations for the Egyptian Rashid coastal inlet, leveraging field-recorded WQ and hydroclimatic datasets. Initially, statistical and exploratory data analyses are performed to provide a thorough understanding of the relationship between DO fluctuations and associated WQ and hydroclimatic stressors. As an initial step towards developing an effective DO predictive model, conventional Machine Learning (ML) approaches such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR) are employed. Subsequently, a DLNN approach is utilized to validate the prediction capabilities of the investigated conventional ML approaches. Finally, a sensitivity analysis is conducted to evaluate the impact of WQ and hydroclimatic parameters on predicted DO. The outcomes demonstrate that DLNN significantly improves DO prediction accuracy by 4% compared to the best-performing ML approach, achieving a Correlation Coefficient of 0.95 with a root mean square error (RMSE) of 0.42 mg/l. Solar radiation (SR), pH, water levels (WL), and atmospheric pressure (P) emerge as the most significant hydroclimatic parameters influencing DO fluctuations. Ultimately, the developed models could serve as effective indicators for coastal authorities to monitor DO changes resulting from accelerated climate change along the Egyptian coast.
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Affiliation(s)
- Mohamed T Elnabwy
- Coastal Research Institute (CORI), National Water Research Center, Alexandria 21415, Egypt; Civil Engineering Department., Faculty of Engineering, Damietta University., New Damietta 34517, Egypt.
| | - Abdullah H Alshahri
- Department of Civil Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif City 21974, Saudi Arabia.
| | - Ayman A El-Gamal
- Department of Marine Geology, Coastal Research Institute (CoRI), National Water Research Center, Alexandria 21415 Egypt.
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Promkhun K, Sinpru P, Bunnom R, Molee W, Kubota S, Uimari P, Molee A. Jejunal transcriptomic profiling of carnosine synthesis precursor-related genes and pathways in slow-growing Korat chicken. Poult Sci 2024; 103:104046. [PMID: 39033572 PMCID: PMC11326888 DOI: 10.1016/j.psj.2024.104046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 06/13/2024] [Accepted: 06/25/2024] [Indexed: 07/23/2024] Open
Abstract
Carnosine is a physiologically important molecule in normal human body functions. Chicken meat is an excellent source of carnosine; especially slow-growing Korat chicken (KR) females have a high carnosine content in their meat. The carnosine content of chicken meat can be increased by dietary supplementation of β-alanine (βA) and L-histidine (L-His). Our objective was to reveal the pathways and genes through jejunal transcriptomic profiling related to βA and L-His absorption and transportation. We collected whole jejunum samples from 5 control and 5 experimental KR chicken, fed with 1% βA and 0.5% L-His supplementation. A total of 407 differentially expressed genes (P < 0.05, log2 fold change ≥2) were identified, 272 of which were down-regulated and 135 up-regulated in the group with dietary supplementation compared to the control group. Based on the integrated analysis of the protein-protein interaction network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway maps, 87 gene ontology terms were identified and 6 KEGG pathways were significantly (P < 0.05) enriched in the jejunum. The analyses revealed 6 key genes, KCND3, OPRM1, CCK, GCG, TRH, and GABBR2, that are related to neuroactive ligand-receptor interaction and the calcium signaling pathway. These findings give insight regarding the molecular mechanism related to carnosine precursor absorption and transportation in the jejunum and help to identify useful molecular markers for improving the carnosine content in slow-growing KR chicken meat.
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Affiliation(s)
- Kasarat Promkhun
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Panpradub Sinpru
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Rujjira Bunnom
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Wittawat Molee
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Satoshi Kubota
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Pekka Uimari
- Department of Agricultural Sciences, Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, 00790, Finland
| | - Amonrat Molee
- School of Animal Technology and Innovation, Institute of Agricultural Technology, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand.
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28
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Lechien JR, Rameau A. Applications of ChatGPT in Otolaryngology-Head Neck Surgery: A State of the Art Review. Otolaryngol Head Neck Surg 2024; 171:667-677. [PMID: 38716790 DOI: 10.1002/ohn.807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/01/2024] [Accepted: 04/19/2024] [Indexed: 08/28/2024]
Abstract
OBJECTIVE To review the current literature on the application, accuracy, and performance of Chatbot Generative Pre-Trained Transformer (ChatGPT) in Otolaryngology-Head and Neck Surgery. DATA SOURCES PubMED, Cochrane Library, and Scopus. REVIEW METHODS A comprehensive review of the literature on the applications of ChatGPT in otolaryngology was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. CONCLUSIONS ChatGPT provides imperfect patient information or general knowledge related to diseases found in Otolaryngology-Head and Neck Surgery. In clinical practice, despite suboptimal performance, studies reported that the model is more accurate in providing diagnoses, than in suggesting the most adequate additional examinations and treatments related to clinical vignettes or real clinical cases. ChatGPT has been used as an adjunct tool to improve scientific reports (referencing, spelling correction), to elaborate study protocols, or to take student or resident exams reporting several levels of accuracy. The stability of ChatGPT responses throughout repeated questions appeared high but many studies reported some hallucination events, particularly in providing scientific references. IMPLICATIONS FOR PRACTICE To date, most applications of ChatGPT are limited in generating disease or treatment information, and in the improvement of the management of clinical cases. The lack of comparison of ChatGPT performance with other large language models is the main limitation of the current research. Its ability to analyze clinical images has not yet been investigated in otolaryngology although upper airway tract or ear images are an important step in the diagnosis of most common ear, nose, and throat conditions. This review may help otolaryngologists to conceive new applications in further research.
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Affiliation(s)
- Jérôme R Lechien
- Research Committee of Young Otolaryngologists of the International Federation of Otorhinolaryngological Societies (IFOS), Paris, France
- Division of Laryngology and Broncho-Esophagology, Department of Otolaryngology-Head Neck Surgery, EpiCURA Hospital, UMONS Research Institute for Health Sciences and Technology, University of Mons (UMons), Mons, Belgium
- Department of Otorhinolaryngology and Head and Neck Surgery, Foch Hospital, Phonetics and Phonology Laboratory (UMR 7018 CNRS, Université Sorbonne Nouvelle/Paris 3), Paris Saclay University, Paris, France
- Department of Otorhinolaryngology and Head and Neck Surgery, CHU Saint-Pierre, Brussels, Belgium
| | - Anais Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medicine, New York City, New York, USA
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29
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Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, Luo Z, Tian Q, Qiu C, Zhao LJ, Liu A, Jiang L, Zhang X, Shen H, Zhou W, Deng HW. Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data. Comput Biol Med 2024; 179:108813. [PMID: 38955127 PMCID: PMC11324385 DOI: 10.1016/j.compbiomed.2024.108813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.
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Affiliation(s)
- Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA 30060
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Chong Wu
- Department of Biostatistics, University of Texas MD Anderson, Pickens Academic Tower, 1400 Pressler St., Houston, TX 77030
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
| | - Wu Li
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Qing Tian
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Lan Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Anqi Liu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Lindong Jiang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Xiao Zhang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931
- Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI 49931
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112
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Jepsen AH, Kampmann ML, Jacobsen SB, Børsting C, Andersen JD. Identification of individuals from low template blood samples using whole transcriptome shotgun sequencing. Forensic Sci Int Genet 2024; 72:103089. [PMID: 38905753 DOI: 10.1016/j.fsigen.2024.103089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/14/2024] [Accepted: 06/19/2024] [Indexed: 06/23/2024]
Abstract
Biological trace samples consisting of very few cells pose a challenge to conventional forensic genetic DNA analysis. RNA may be an alternative to DNA when handling low template samples. Whereas each cell only contains two copies of an autosomal DNA segment, the transcriptome retains much of the genomic variation replicated in abundant RNA fragments. In this study, we describe the development of a prototype RNA-based SNP selection set for forensic human identification from low template samples (50 pg gDNA). Whole blood from a subset of the Danish population (41 individuals) and blood stains subjected to degradation at room temperature for up to two weeks were analysed by whole transcriptome shotgun sequencing. Concordance was determined by DNA genotyping with the Infinium Omni5-4 SNP chip. In the 100 protein-coding genes with the most reads, 5214 bi-allelic SNPs with gnomAD minor allele frequencies > 0.1 in the African/African American, East Asian, and (non-Finnish) European populations were identified. Of these, 24 SNPs in 21 genes passed screening in whole blood and degraded blood stains, with a resulting mean match probability of 4.5 ∙ 10-9. Additionally, ancestry informative SNPs and SNPs in genes useful for body fluid identification were identified in the transcriptome. Consequently, shotgun sequencing of RNA from low template samples may be used for a vast host of forensic genetics purposes, including simultaneous human and body fluid identification, leading to direct donor identification in the identified body fluid.
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Affiliation(s)
- Alberte Honoré Jepsen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, Copenhagen DK-2100, Denmark.
| | - Marie-Louise Kampmann
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, Copenhagen DK-2100, Denmark
| | - Stine Bøttcher Jacobsen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, Copenhagen DK-2100, Denmark
| | - Claus Børsting
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, Copenhagen DK-2100, Denmark
| | - Jeppe Dyrberg Andersen
- Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Frederik V's Vej 11, Copenhagen DK-2100, Denmark
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Oka H, Kawahara D, Murakami Y. Radiomics-based prediction of recurrence for head and neck cancer patients using data imbalanced correction. Comput Biol Med 2024; 180:108879. [PMID: 39067154 DOI: 10.1016/j.compbiomed.2024.108879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 06/21/2024] [Accepted: 07/10/2024] [Indexed: 07/30/2024]
Abstract
OBJECTIVES To propose a radiomics-based prediction model for head and neck squamous cell carcinoma (HSNCC) recurrence after radiation therapy using a novel data imbalance correction method known as Gaussian noise upsampling (GNUS). MATERIALS AND METHODS The dataset includes 97 HNSCC patients treated with definitive radiotherapy alone or concurrent chemoradiotherapy at two institutions. We performed radiomics analysis using nine segmentations created on pretreatment positron emission tomography and computed tomography images. Feature selection was performed by the least absolute shrinkage and selection operator analysis via five-fold cross-validation. The proposed GNUS was compared with seven conventional data-imbalance correction methods. Classification models of HNSCC recurrence were constructed on oversampled features using the machine learning algorithms of linear regression. Their predictive performance was evaluated based on accuracy, sensitivity, specificity, and the area under the curve (AUC) of the receiver operating performance characteristic curve via five-fold cross-validation using the same combinations as for feature selection. RESULT The prediction model without data imbalance correction shows sensitivity, specificity, accuracy, and AUC values of 83 %, 96 %, 92 %, and 0.96, respectively. The conventional model with the best performance is the random over-sampler model, which shows sensitivity, specificity, accuracy, and AUC values of 93 %, 91 %, 92 %, 0.97, respectively, whereas the GNUS model shows values of 93 %, 94 %, 94 %, 0.98, respectively. CONCLUSION Oversampling methods can reduce sensitivity and specificity bias. The proposed GNUS can improve accuracy as well as reduce sensitivity and specificity bias.
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Affiliation(s)
- Hiroki Oka
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
| | - Daisuke Kawahara
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan.
| | - Yuji Murakami
- Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, 734-8551, Japan
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Boaro A, Azzari A, Basaldella F, Nunes S, Feletti A, Bicego M, Sala F. Machine learning allows expert level classification of intraoperative motor evoked potentials during neurosurgical procedures. Comput Biol Med 2024; 180:109032. [PMID: 39163827 DOI: 10.1016/j.compbiomed.2024.109032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 08/07/2024] [Accepted: 08/13/2024] [Indexed: 08/22/2024]
Abstract
OBJECTIVE To develop and evaluate machine learning (ML) approaches for muscle identification using intraoperative motor evoked potentials (MEPs), and to compare their performance to human experts. BACKGROUND There is an unseized opportunity to apply ML analytic techniques to the world of intraoperative neuromonitoring (IOM). MEPs are the ideal candidates given the importance of their correct interpretation during a surgical operation to the brain or the spine. In this work, we develop and test a set of different ML models for muscle identification using intraoperative MEPs and compare their performance to human experts. In addition, we provide a review of the available literature on current ML applications to IOM data in neurosurgery. METHODS We trained and tested five different ML classifiers on a MEP database developed from six different muscles in patients who underwent brain or spinal cord surgery. MEPs were obtained by both transcranial (TES) and direct cortical stimulation (DCS) protocols. The models were evaluated within a single patient and on previously unseen patients, considering signals from TES and DCS both independently and mixed. Ten expert neurophysiologists classified a set of 50 randomly selected MEPs, and their performance was compared to the best performing model. RESULTS A total of 25.423 MEPs were included in the study. Random Forest proved to be the best performing model with 99 % accuracy in the single patient dataset task and a 78 %-94 % accuracy range on previously unseen patients. The model performance was maximized by representing MEPs as a set of features typically employed in signal processing compared to traditional neurophysiological parameters. The classification ability of the Random Forest model between six different muscles and across different MEP acquisition modalities (79 %) significantly exceeded that of human experts (mean 48 %). CONCLUSIONS Carefully selected ML models proved to have reliable capacity of extracting meaningful information to classify intraoperative MEPs using a limited number of features, proving robustness across patients and signal acquisition modalities, outperforming human experts, and with the potential to act as decision support systems to the IOM team. Such encouraging results lay the path to further explore the underlying nature of clinically important signals, with the aim to continue to produce useful applications to make surgeries safer and more efficient.
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Affiliation(s)
- Alessandro Boaro
- Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.
| | - Alberto Azzari
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Sonia Nunes
- Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Alberto Feletti
- Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Manuele Bicego
- Department of Computer Science, University of Verona, Verona, Italy
| | - Francesco Sala
- Section of Neurosurgery, Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
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Xu T, Weng H, Liu F, Yang L, Luo Y, Ding Z, Wang Q. Current Status of ChatGPT Use in Medical Education: Potentials, Challenges, and Strategies. J Med Internet Res 2024; 26:e57896. [PMID: 39196640 DOI: 10.2196/57896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 06/05/2024] [Accepted: 06/29/2024] [Indexed: 08/29/2024] Open
Abstract
ChatGPT, a generative pretrained transformer, has garnered global attention and sparked discussions since its introduction on November 30, 2022. However, it has generated controversy within the realms of medical education and scientific research. This paper examines the potential applications, limitations, and strategies for using ChatGPT. ChatGPT offers personalized learning support to medical students through its robust natural language generation capabilities, enabling it to furnish answers. Moreover, it has demonstrated significant use in simulating clinical scenarios, facilitating teaching and learning processes, and revitalizing medical education. Nonetheless, numerous challenges accompany these advancements. In the context of education, it is of paramount importance to prevent excessive reliance on ChatGPT and combat academic plagiarism. Likewise, in the field of medicine, it is vital to guarantee the timeliness, accuracy, and reliability of content generated by ChatGPT. Concurrently, ethical challenges and concerns regarding information security arise. In light of these challenges, this paper proposes targeted strategies for addressing them. First, the risk of overreliance on ChatGPT and academic plagiarism must be mitigated through ideological education, fostering comprehensive competencies, and implementing diverse evaluation criteria. The integration of contemporary pedagogical methodologies in conjunction with the use of ChatGPT serves to enhance the overall quality of medical education. To enhance the professionalism and reliability of the generated content, it is recommended to implement measures to optimize ChatGPT's training data professionally and enhance the transparency of the generation process. This ensures that the generated content is aligned with the most recent standards of medical practice. Moreover, the enhancement of value alignment and the establishment of pertinent legislation or codes of practice address ethical concerns, including those pertaining to algorithmic discrimination, the allocation of medical responsibility, privacy, and security. In conclusion, while ChatGPT presents significant potential in medical education, it also encounters various challenges. Through comprehensive research and the implementation of suitable strategies, it is anticipated that ChatGPT's positive impact on medical education will be harnessed, laying the groundwork for advancing the discipline and fostering the development of high-caliber medical professionals.
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Affiliation(s)
- Tianhui Xu
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Huiting Weng
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Fang Liu
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Li Yang
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yuanyuan Luo
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Ziwei Ding
- Xiangya School of Nursing, Central South University, Changsha, China
| | - Qin Wang
- Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China
- Xiangya School of Nursing, Central South University, Changsha, China
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Shang J, Xu A, Bi M, Zhang Y, Li F, Liu JX. A review: simulation tools for genome-wide interaction studies. Brief Funct Genomics 2024:elae034. [PMID: 39173096 DOI: 10.1093/bfgp/elae034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 07/25/2024] [Accepted: 08/10/2024] [Indexed: 08/24/2024] Open
Abstract
Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.
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Affiliation(s)
- Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Anqi Xu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Mingyuan Bi
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266114, China
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Helander ME. "Dead or Alive?" Assessment of the Binary End-of-Event Outcome Indicator for the NEMSIS Public Research Dataset. PREHOSP EMERG CARE 2024:1-10. [PMID: 39106451 DOI: 10.1080/10903127.2024.2389551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 07/23/2024] [Accepted: 07/26/2024] [Indexed: 08/09/2024]
Abstract
OBJECTIVES The National Emergency Medical Services Information System (NEMSIS) provides a robust set of data to evaluate prehospital care. However, a major limitation is that the vast majority of the records lack a definitive outcome. This study aimed to evaluate the performance of a recently proposed method ("MLB" method) to impute missing end-of-EMS-event outcomes ("dead" or "alive") for patient care reports in the NEMSIS public research dataset. METHODS This study reproduced the recently published method for patient outcome imputation in the NEMSIS database and replicated the results for years 2017 through 2022 (n = 686,075). We performed statistical analyses leveraging an array of established performance metrics for binary classification from the machine learning literature. Evaluation metrics included overall accuracy, true positive rate, true negative rate, balanced accuracy, precision, F1 score, Cohen's Kappa coefficient, Matthews' coefficient, Hamming loss, the Jaccard similarity score, and the receiver operating characteristic/area under the curve. RESULTS Extended metrics show consistently good imputation performance from year-to-year but reveal weakness in accurately indicating the minority class: e.g., after adjustments for conflicting labels, "dead" prediction accuracy is 77.7% for 2018 and 61.8% over the six-year NEMSIS sub-sample, even though overall accuracy is 98.8%. Slight over-fitting is also present. CONCLUSIONS This study found that the recently published MLB method produced reasonably good "dead" or "alive" indicators. We recommend reporting of True Positive Rate ("dead" prediction accuracy) and True Negative Rate ("alive" prediction accuracy) when applying the imputation method for analyses of NEMSIS data. More attention by EMS clinicians to complete documentation of target NEMSIS elements can further improve the method's performance.
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Affiliation(s)
- Mary E Helander
- Maxwell School of Citizenship and Public Affairs, Department of Social Science and Falk College, Department of Public Health, Syracuse University, Syracuse, New York
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Galvão G, Trefilio LM, Salvio AL, Veloso E, Alves-Leon SV, Fontes-Dantas FL, de Souza JM. Comprehensive Analysis of Novel Mutations in CCM1/KRIT1 and CCM2/MGC4607 and Their Clinical Implications in Cerebral Cavernous Malformations. J Stroke Cerebrovasc Dis 2024:107947. [PMID: 39181174 DOI: 10.1016/j.jstrokecerebrovasdis.2024.107947] [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: 05/06/2024] [Revised: 08/08/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Cerebral Cavernous Malformations (CCM) is a genetic disease characterized by vascular abnormalities in the brain and spinal cord, affecting 0.4-0.5% of the population. We identified two novel pathogenic mutations, CCM1/KRIT1 c.811delT (p.Trp271GlyfsTer5) and CCM2/MGC4607 c.613_614insGG p.Glu205GlyfsTer31), which disrupt crucial protein domains and potentially alter disease progression. OBJECTIVE The study aims to comprehensively analyze a Brazilian cohort of CCM patients, integrating genetic, clinical, and structural aspects. Specifically, we sought to identify novel mutations within the CCM complex, and explore their potential impact on disease progression. METHODS We conducted a detailed examination of neuroradiological and clinical features in both symptomatic and asymptomatic CCM patients, performing genetic analyses through sequencing of the CCM1/KRIT1, CCM2/MGC4607, and CCM3/PDCD10 genes In silico structural predictions were carried out using PolyPhen-2, SIFT, and Human Genomics Community tools. Protein-protein interactions and docking analyses were explored using the STRING database. RESULTS Genetic analysis identifies 6 pathogenic mutations, 4 likely pathogenic, 1 variants of uncertain significance, and 7 unclassified mutations, including the novel mutations in CCM1 c.811delT and CCM2 c.613_614insGG. In silico structural analysis revealed significant alterations in protein structure, supporting their pathogenicity. Protein-protein interaction analysis indicated nuanced impacts on cellular processes. Clinically, we observed a broad spectrum of symptoms, including seizures and focal neurological deficits. However, no statistically significant differences were found in lesion burden, age of first symptom onset, or sex between the identified CCM1/KRIT1 and CCM2/MGC4607 mutations among all patients studied. CONCLUSION This study enhances the understanding of CCM by linking clinical variability, genetic mutations, and structural effects. The identification of these novel mutations opens new avenues for research and potential therapeutic strategies.
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Affiliation(s)
- Gustavo Galvão
- Universidade Federal do Estado do Rio de Janeiro, Laboratório de Neurociências Translacional, Programa de Pós-Graduação em Neurologia, Rio de Janeiro RJ, Brasil; Universidade Federal do Rio de Janeiro, Hospital Universitário Clementino Fraga Filho, Departamento de Neurocirurgia, Rio de Janeiro RJ, Brasil
| | - Luisa Menezes Trefilio
- Universidade Estadual do Rio de Janeiro, Instituto de Biologia Roberto Alcântara Gomes, Departamento de Farmacologia e Psicobiologia, Rio de Janeiro RJ, Brasil; Universidade Federal do Estado do Rio de Janeiro, Instituto Biomédico, Rio de Janeiro RJ, Brasil
| | - Andreza Lemos Salvio
- Universidade Federal do Estado do Rio de Janeiro, Laboratório de Neurociências Translacional, Programa de Pós-Graduação em Neurologia, Rio de Janeiro RJ, Brasil
| | - Elielson Veloso
- Universidade Federal do Estado do Rio de Janeiro, Laboratório de Neurociências Translacional, Programa de Pós-Graduação em Neurologia, Rio de Janeiro RJ, Brasil
| | - Soniza Vieira Alves-Leon
- Universidade Federal do Estado do Rio de Janeiro, Laboratório de Neurociências Translacional, Programa de Pós-Graduação em Neurologia, Rio de Janeiro RJ, Brasil; Universidade Federal do Rio de Janeiro, Hospital Universitário Clementino Fraga Filho, Departamento de Neurologia, Rio de Janeiro RJ, Brasil
| | - Fabrícia Lima Fontes-Dantas
- Universidade Estadual do Rio de Janeiro, Instituto de Biologia Roberto Alcântara Gomes, Departamento de Farmacologia e Psicobiologia, Rio de Janeiro RJ, Brasil.
| | - Jorge Marcondes de Souza
- Universidade Federal do Rio de Janeiro, Hospital Universitário Clementino Fraga Filho, Departamento de Neurocirurgia, Rio de Janeiro RJ, Brasil.
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Li KD, Fernandez AM, Schwartz R, Rios N, Carlisle MN, Amend GM, Patel HV, Breyer BN. Comparing GPT-4 and Human Researchers in Health Care Data Analysis: Qualitative Description Study. J Med Internet Res 2024; 26:e56500. [PMID: 39167785 DOI: 10.2196/56500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 05/31/2024] [Accepted: 07/09/2024] [Indexed: 08/23/2024] Open
Abstract
BACKGROUND Large language models including GPT-4 (OpenAI) have opened new avenues in health care and qualitative research. Traditional qualitative methods are time-consuming and require expertise to capture nuance. Although large language models have demonstrated enhanced contextual understanding and inferencing compared with traditional natural language processing, their performance in qualitative analysis versus that of humans remains unexplored. OBJECTIVE We evaluated the effectiveness of GPT-4 versus human researchers in qualitative analysis of interviews with patients with adult-acquired buried penis (AABP). METHODS Qualitative data were obtained from semistructured interviews with 20 patients with AABP. Human analysis involved a structured 3-stage process-initial observations, line-by-line coding, and consensus discussions to refine themes. In contrast, artificial intelligence (AI) analysis with GPT-4 underwent two phases: (1) a naïve phase, where GPT-4 outputs were independently evaluated by a blinded reviewer to identify themes and subthemes and (2) a comparison phase, where AI-generated themes were compared with human-identified themes to assess agreement. We used a general qualitative description approach. RESULTS The study population (N=20) comprised predominantly White (17/20, 85%), married (12/20, 60%), heterosexual (19/20, 95%) men, with a mean age of 58.8 years and BMI of 41.1 kg/m2. Human qualitative analysis identified "urinary issues" in 95% (19/20) and GPT-4 in 75% (15/20) of interviews, with the subtheme "spray or stream" noted in 60% (12/20) and 35% (7/20), respectively. "Sexual issues" were prominent (19/20, 95% humans vs 16/20, 80% GPT-4), although humans identified a wider range of subthemes, including "pain with sex or masturbation" (7/20, 35%) and "difficulty with sex or masturbation" (4/20, 20%). Both analyses similarly highlighted "mental health issues" (11/20, 55%, both), although humans coded "depression" more frequently (10/20, 50% humans vs 4/20, 20% GPT-4). Humans frequently cited "issues using public restrooms" (12/20, 60%) as impacting social life, whereas GPT-4 emphasized "struggles with romantic relationships" (9/20, 45%). "Hygiene issues" were consistently recognized (14/20, 70% humans vs 13/20, 65% GPT-4). Humans uniquely identified "contributing factors" as a theme in all interviews. There was moderate agreement between human and GPT-4 coding (κ=0.401). Reliability assessments of GPT-4's analyses showed consistent coding for themes including "body image struggles," "chronic pain" (10/10, 100%), and "depression" (9/10, 90%). Other themes like "motivation for surgery" and "weight challenges" were reliably coded (8/10, 80%), while less frequent themes were variably identified across multiple iterations. CONCLUSIONS Large language models including GPT-4 can effectively identify key themes in analyzing qualitative health care data, showing moderate agreement with human analysis. While human analysis provided a richer diversity of subthemes, the consistency of AI suggests its use as a complementary tool in qualitative research. With AI rapidly advancing, future studies should iterate analyses and circumvent token limitations by segmenting data, furthering the breadth and depth of large language model-driven qualitative analyses.
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Affiliation(s)
- Kevin Danis Li
- Department of Urology, University of California San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
| | - Adrian M Fernandez
- Department of Urology, University of California San Francisco, San Francisco, CA, United States
| | - Rachel Schwartz
- Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, United States
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Natalie Rios
- Department of Urology, University of California San Francisco, San Francisco, CA, United States
| | | | - Gregory M Amend
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Hiren V Patel
- Department of Urology, University of California San Francisco, San Francisco, CA, United States
| | - Benjamin N Breyer
- Department of Urology, University of California San Francisco, San Francisco, CA, United States
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States
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Li Y, Liu X, Gu M, Xu T, Ge C, Chang P. Significance of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer: A narrative review. Cancer Radiother 2024:S1278-3218(24)00097-0. [PMID: 39174361 DOI: 10.1016/j.canrad.2024.04.003] [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: 03/27/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 08/24/2024]
Abstract
Neoadjuvant chemoradiotherapy is the standard treatment for patients with locally advanced rectal cancers owing to its ability to downstage primary tumours. Some patients can achieve pathological complete response after neoadjuvant therapy, and can adopt a "watch and wait" treatment strategy to avoid overtreatment. Therefore, it is essential to develop strategies for predicting responses to neoadjuvant therapy. Radiomics has shown great potential in extracting tumour features from high-throughput medical images for the construction of mathematics models for predicting the effects of anticancerous therapies. Herein, we explored MRI-based radiomics and found that it can predict responses of locally advanced rectal cancers to chemoradiation. Efficient radiomics model allow early-stage prediction of the effect of neoadjuvant chemoradiotherapy on locally advanced rectal cancers. It helps clinicians to make informed therapeutic decisions. In this review, we discuss the workflow of radiomics, and summarize the clinical application of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer.
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Affiliation(s)
- Y Li
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, China
| | - X Liu
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, China
| | - M Gu
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, China
| | - T Xu
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, China
| | - C Ge
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, China
| | - P Chang
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, China.
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Tanabe H, Sato M, Miyake A, Shimajiri Y, Ojima T, Narita A, Saito H, Tanaka K, Masuzaki H, Kazama JJ, Katagiri H, Tamiya G, Kawakami E, Shimabukuro M. Machine learning-based reproducible prediction of type 2 diabetes subtypes. Diabetologia 2024:10.1007/s00125-024-06248-8. [PMID: 39168869 DOI: 10.1007/s00125-024-06248-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/12/2024] [Indexed: 08/23/2024]
Abstract
AIMS/HYPOTHESIS Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time. METHODS Cohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2Dkmeans) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA1c, HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2DRF15). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications. RESULTS T2DRF15 demonstrated a 94% accuracy for predicting T2Dkmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2DRF15 showed an accuracy of 82.9% for detecting T2Dkmeans, also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan-Meier analysis, the diabetes clusters of T2DRF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5-18.3) years, similarly to the subtypes using T2Dkmeans. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2DRF15, after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2Dkmeans. CONCLUSIONS/INTERPRETATION The new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.
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Affiliation(s)
- Hayato Tanabe
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan
- Department of Diabetes, Metabolism and Endocrinology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Masahiro Sato
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Akimitsu Miyake
- Department of AI and Innovative Medicine, Tohoku University School of Medicine, Miyagi, Japan
| | | | - Takafumi Ojima
- Department of AI and Innovative Medicine, Tohoku University School of Medicine, Miyagi, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Haruka Saito
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Kenichi Tanaka
- Department of Nephrology and Hypertension, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Hiroaki Masuzaki
- Division of Endocrinology and Metabolism, Second Department of Internal Medicine, University of the Ryukyus Graduate School of Medicine, Okinawa, Japan
| | - Junichiro J Kazama
- Department of Nephrology and Hypertension, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Hideki Katagiri
- Department of Diabetes, Metabolism and Endocrinology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Gen Tamiya
- Department of AI and Innovative Medicine, Tohoku University School of Medicine, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan.
| | - Michio Shimabukuro
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan.
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Xu S, Xu T, Yang Y, Chen X. Learning metabolic dynamics from irregular observations by Bidirectional Time-Series State Transfer Network. mSystems 2024; 9:e0069724. [PMID: 39057922 PMCID: PMC11334518 DOI: 10.1128/msystems.00697-24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
Modeling microbial metabolic dynamics is important for the rational optimization of both biosynthetic systems and industrial processes to facilitate green and efficient biomanufacturing. Classical approaches utilize explicit equation systems to represent metabolic networks, enabling the quantification of pathway fluxes to identify metabolic bottlenecks. However, these white-box models, despite their diverse applications, have limitations in simulating metabolic dynamics and are intrinsically inaccurate for industrial strains that lack information on network structures and kinetic parameters. On the other hand, black-box models do not rely on prior mechanistic knowledge of strains but are built upon observed time-series trajectories of biosynthetic systems in action. In practice, these observations are typically irregular, with discontinuously observed time points across multiple independent batches, each time point potentially containing missing measurements. Learning from such irregular data remains challenging for existing approaches. To address this issue, we present the Bidirectional Time-Series State Transfer Network (BTSTN) for modeling metabolic dynamics directly from irregular observations. Using evaluation data sets derived from both ideal dynamic systems and a real-world fermentation process, we demonstrate that BTSTN accurately reconstructs dynamic behaviors and predicts future trajectories. This approach exhibits enhanced robustness against missing measurements and noise, as compared to the state-of-the-art methods.IMPORTANCEIndustrial biosynthetic systems often involve strains with unclear genetic backgrounds, posing challenges in modeling their distinct metabolic dynamics. In such scenarios, white-box models, which commonly rely on inferred networks, are thereby of limited applicability and accuracy. In contrast, black-box models, such as statistical models and neural networks, are directly fitted or learned from observed time-series trajectories of biosynthetic systems in action. These methods typically assume regular observations without missing time points or measurements. If the observations are irregular, a pre-processing step becomes necessary to obtain a fully filled data set for subsequent model training, which, at the same time, inevitably introduces errors into the resulting models. BTSTN is a novel approach that natively learns from irregular observations. This distinctive feature makes it a unique addition to the current arsenal of technologies modeling metabolic dynamics.
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Affiliation(s)
- Shaohua Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
| | - Ting Xu
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuping Yang
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
| | - Xin Chen
- School of Basic Medical Sciences and the First Affiliated Hospital Department of Radiation Oncology, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Key Laboratory for Microbial Biochemistry and Metabolic Engineering, Hangzhou, China
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Alnaimat F, Al-Halaseh S, AlSamhori ARF. Evolution of Research Reporting Standards: Adapting to the Influence of Artificial Intelligence, Statistics Software, and Writing Tools. J Korean Med Sci 2024; 39:e231. [PMID: 39164055 PMCID: PMC11333804 DOI: 10.3346/jkms.2024.39.e231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 07/01/2024] [Indexed: 08/22/2024] Open
Abstract
Reporting standards are essential to health research as they improve accuracy and transparency. Over time, significant changes have occurred to the requirements for reporting research to ensure comprehensive and transparent reporting across a range of study domains and foster methodological rigor. The establishment of the Declaration of Helsinki, Consolidated Standards of Reporting Trials (CONSORT), Strengthening the Reporting of Observational Studies in Epidemiology (STROBE), and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) are just a few of the historic initiatives that have increased research transparency. Through enhanced discoverability, statistical analysis facilitation, article quality enhancement, and language barrier reduction, artificial intelligence (AI)-in particular, large language models like ChatGPT-has transformed academic writing. However, problems with errors that could occur and the need for transparency while utilizing AI tools still exist. Modifying reporting rules to include AI-driven writing tools such as ChatGPT is ethically and practically challenging. In academic writing, precautions for truth, privacy, and responsibility are necessary due to concerns about biases, openness, data limits, and potential legal ramifications. The CONSORT-AI and Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-AI Steering Group expands the CONSORT guidelines for AI clinical trials-new checklists like METRICS and CLEAR help to promote transparency in AI studies. Responsible usage of technology in research and writing software adoption requires interdisciplinary collaboration and ethical assessment. This study explores the impact of AI technologies, specifically ChatGPT, on past reporting standards and the need for revised guidelines for open, reproducible, and robust scientific publications.
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Affiliation(s)
- Fatima Alnaimat
- Division of Rheumatology, Department of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan.
| | - Salameh Al-Halaseh
- Department of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan
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Lin ET, Lu SC, Liu AS, Ko CH, Huang CH, Tsai CL, Fu LC. Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01209-4. [PMID: 39160365 DOI: 10.1007/s10278-024-01209-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/15/2024] [Accepted: 07/22/2024] [Indexed: 08/21/2024]
Abstract
Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet the need in clinical practice, physicians often rely on visual examination of specific areas, such as conjunctiva, to assess pallor. However, this method is subjective and relies on the physician's experience. Therefore, we proposed a deep learning prediction model based on three input images from different body parts, namely, conjunctiva, palm, and fingernail. By incorporating additional body part labels and employing a fusion attention mechanism, the model learns and enhances the salient features of each body part during training, enabling it to produce reliable results. Additionally, we employ a dual loss function that allows the regression model to benefit from well-established classification methods, thereby achieving stable handling of minority samples. We used a retrospective data set (EYES-DEFY-ANEMIA) to develop this model called Body-Part-Anemia Network (BPANet). The BPANet showed excellent performance in detecting anemia, with accuracy of 0.849 and an F1-score of 0.828. Our multi-body-part model has been validated on a prospectively collected data set of 101 patients in National Taiwan University Hospital. The prediction accuracy as well as F1-score can achieve as high as 0.716 and 0.788, respectively. To sum up, we have developed and validated a novel non-invasive hemoglobin prediction model based on image input from multiple body parts, with the potential of real-time use at home and in clinical settings.
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Affiliation(s)
- En-Ting Lin
- Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan
| | - Shao-Chi Lu
- Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan
| | - An-Sheng Liu
- Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan
| | - Chia-Hsin Ko
- Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan
| | - Chien-Hua Huang
- Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan
| | - Chu-Lin Tsai
- Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan.
| | - Li-Chen Fu
- Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan.
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Bundy JL, Everett LJ, Rogers JD, Nyffeler J, Byrd G, Culbreth M, Haggard DE, Word LJ, Chambers BA, Davidson-Fritz S, Harris F, Willis C, Paul-Friedman K, Shah I, Judson R, Harrill JA. High-Throughput Transcriptomics Screen of ToxCast Chemicals in U-2 OS Cells. Toxicol Appl Pharmacol 2024; 491:117073. [PMID: 39159848 DOI: 10.1016/j.taap.2024.117073] [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: 05/22/2024] [Revised: 08/14/2024] [Accepted: 08/16/2024] [Indexed: 08/21/2024]
Abstract
New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10-3 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R2 = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.
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Affiliation(s)
- Joseph L Bundy
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
| | - Logan J Everett
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Jesse D Rogers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, 37831, United States of America
| | - Jo Nyffeler
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN, 37831, United States of America
| | - Gabrielle Byrd
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37831, United States of America
| | - Megan Culbreth
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Derik E Haggard
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Laura J Word
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Bryant A Chambers
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Sarah Davidson-Fritz
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Felix Harris
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America; Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, 37831, United States of America
| | - Clinton Willis
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Katie Paul-Friedman
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Imran Shah
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Richard Judson
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
| | - Joshua A Harrill
- Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America
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Wen Y, Yi F, Zhang J, Wang Y, Zhao C, Zhao B, Wang J. Uncovering the protective mechanism of baicalin in treatment of fatty liver based on network pharmacology and cell model of NAFLD. Int Immunopharmacol 2024; 141:112954. [PMID: 39153306 DOI: 10.1016/j.intimp.2024.112954] [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: 06/12/2024] [Revised: 07/25/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Excessive nonesterified fatty acids (NEFA) impair cellular metabolism and will induce fatty liver formation in dairy cows during the periparturient. Baicalin, an active flavonoid, has great potential efficacy in alleviating lipid accumulation and ameliorating the development of fatty liver disease. Nevertheless, its mechanism remains unclear. Here, the potential mechanism of baicalin on system levels was explored using network pharmacology and in vitro experiments. Firstly, the target of baicalin and fatty liver disease was predicted, and then the protein-protein interaction (PPI) network was constructed. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG) (q-value) pathway enrichment is performed through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server. Finally, the results of the network analysis of the in vitro treatment of bovine hepatocytes by NEFA were confirmed. The results showed that 33 relevant targets of baicalin in the treatment of liver fatty were predicted by network pharmacology, and the top 20 relevant pathways were extracted by KEGG database. Baicalin treatment can reduce triglyceride (TAG) content and lipid droplet accumulation in NEFA-treated bovine hepatocytes, and the mechanism is related to inhibiting lipid synthesis and promoting lipid oxidation. The alleviating effect of baicalin on fatty liver may be related to the up-regulation of solute vector family member 4 (SLC2A4), Down-regulated AKT serine/threonine kinase 1 (AKT1), Peroxisome proliferator-activated receptor gamma (PPARG), Epidermal growth factor receptor (EGFR), tumor necrosis factor (TNF), Interleukin 6 (IL-6) were associated. These results suggested that baicalin may modulate key inflammatory markers, and lipogenesis processes to prevent fatty liver development in dairy cows.
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Affiliation(s)
- Yongqiang Wen
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Fanxuan Yi
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Jia Zhang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Yazhou Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Chenxu Zhao
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Baoyu Zhao
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Jianguo Wang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China.
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Lundervold AJ, Billing JE, Berentsen B, Lied GA, Steinsvik EK, Hausken T, Lundervold A. Decoding IBS: a machine learning approach to psychological distress and gut-brain interaction. BMC Gastroenterol 2024; 24:267. [PMID: 39148020 PMCID: PMC11325751 DOI: 10.1186/s12876-024-03355-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 08/06/2024] [Indexed: 08/17/2024] Open
Abstract
PURPOSE Irritable bowel syndrome (IBS) is a diagnosis defined by gastrointestinal (GI) symptoms like abdominal pain and changes associated with defecation. The condition is classified as a disorder of the gut-brain interaction (DGBI), and patients with IBS commonly experience psychological distress. The present study focuses on this distress, defined from reports of fatigue, anxiety, depression, sleep disturbances, and performance on cognitive tests. The aim was to investigate the joint contribution of these features of psychological distress in predicting IBS versus healthy controls (HCs) and to disentangle clinically meaningful subgroups of IBS patients. METHODS IBS patients ( n = 49 ) and HCs ( n = 28 ) completed the Chalder Fatigue Scale (CFQ), the Hamilton Anxiety and Depression Scale (HADS), and the Bergen Insomnia Scale (BIS), and performed tests of memory function and attention from the Repeatable Battery Assessing Neuropsychological Symptoms (RBANS). An initial exploratory data analysis was followed by supervised (Random Forest) and unsupervised (K-means) classification procedures. RESULTS The explorative data analysis showed that the group of IBS patients obtained significantly more severe scores than HCs on all included measures, with the strongest pairwise correlation between fatigue and a quality measure of sleep disturbances. The supervised classification model correctly predicted belongings to the IBS group in 80% of the cases in a test set of unseen data. Two methods for calculating feature importance in the test set gave mental and physical fatigue and anxiety the strongest weights. An unsupervised procedure with K = 3 showed that one cluster contained 24% of the patients and all but two HCs. In the two other clusters, their IBS members were overall more impaired, with the following differences. One of the two clusters showed more severe cognitive problems and anxiety symptoms than the other, which experienced more severe problems related to the quality of sleep and fatigue. The three clusters were not different on a severity measure of IBS and age. CONCLUSION The results showed that psychological distress is an integral component of IBS symptomatology. The study should inspire future longitudinal studies to further dissect clinical patterns of IBS to improve the assessment and personalized treatment for this and other patient groups defined as disorders of the gut-brain interaction. The project is registered at https://classic. CLINICALTRIALS gov/ct2/show/NCT04296552 20/05/2019.
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Affiliation(s)
- Astri J Lundervold
- Department of Biological and Medical Psychology, Universtity of Bergen, Bergen, 5020, Norway.
| | - Julie E Billing
- Department of Biological and Medical Psychology, Universtity of Bergen, Bergen, 5020, Norway
| | - Birgitte Berentsen
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen, 5021, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, 5020, Norway
| | - Gülen A Lied
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen, 5021, Norway
- Center for Nutrition, Department of Clinical Medicine, University of Bergen, Bergen, 5020, Norway
| | - Elisabeth K Steinsvik
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen, 5021, Norway
- Department of Clinical Medicine, University of Bergen, Bergen, 5020, Norway
| | - Trygve Hausken
- National Center for Functional Gastrointestinal Disorders, Department of Medicine, Haukeland University Hospital, Bergen, 5021, Norway
| | - Arvid Lundervold
- Department of Biomedicine, University of Bergen, Bergen, 5020, Norway
- Medical-AI, Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, 5021, Norway
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Al-Tahhan FE, Fares ME. An accurate paradigm for denoising degraded ultrasound images based on artificial intelligence systems. Microsc Res Tech 2024. [PMID: 39145424 DOI: 10.1002/jemt.24675] [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: 05/07/2024] [Revised: 07/20/2024] [Accepted: 07/26/2024] [Indexed: 08/16/2024]
Abstract
Ultrasound images are susceptible to various forms of quality degradation that negatively impact diagnosis. Common degradations include speckle noise, Gaussian noise, salt and pepper noise, and blurring. This research proposes an accurate ultrasound image denoising strategy based on firstly detecting the noise type, then, suitable denoising methods can be applied for each corruption. The technique depends on convolutional neural networks to categorize the type of noise affecting an input ultrasound image. Pre-trained convolutional neural network models including GoogleNet, VGG-19, AlexNet and AlexNet-support vector machine (SVM) are developed and trained to perform this classification. A dataset of 782 numerically generated ultrasound images across different diseases and noise types is utilized for model training and evaluation. Results show AlexNet-SVM achieves the highest accuracy of 99.2% in classifying noise types. The results indicate that, the present technique is considered one of the top-performing models is then applied to real ultrasound images with different noise corruptions to demonstrate efficacy of the proposed detect-then-denoise system. RESEARCH HIGHLIGHTS: Proposes an accurate ultrasound image denoising strategy based on detecting noise type first. Uses pre-trained convolutional neural networks to categorize noise type in input images. Evaluates GoogleNet, VGG-19, AlexNet, and AlexNet-support vector machine (SVM) models on a dataset of 782 synthetic ultrasound images. AlexNet-SVM achieves highest accuracy of 99.2% in classifying noise types. Demonstrates efficacy of the proposed detect-then-denoise system on real ultrasound images.
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Affiliation(s)
- F E Al-Tahhan
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, Egypt
| | - M E Fares
- Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, Egypt
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Wang Y, Gao Z, Zhang Y, Lu Z, Sun F. Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study. Intern Emerg Med 2024:10.1007/s11739-024-03732-2. [PMID: 39141286 DOI: 10.1007/s11739-024-03732-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 07/27/2024] [Indexed: 08/15/2024]
Abstract
Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learning's (ML) use in medical research, local validation within the Medical Information Mart for Intensive Care IV (MIMIC-IV) database is lacking. We aimed to devise a prognostic model, leveraging MIMIC-IV data, to predict sepsis mortality and validate it in a Chinese teaching hospital. MIMIC-IV provided patient data, split into training and internal validation sets. Four ML models logistic regression (LR), support vector machine (SVM), deep neural networks (DNN), and extreme gradient boosting (XGBoost) were employed. Shapley additive interpretation offered early and interpretable mortality predictions. Area under the ROC curve (AUROC) gaged predictive performance. Results were cross verified in a Chinese teaching hospital. The study included 27,134 sepsis patients from MIMIC-IV and 487 from China. After comparing, 52 clinical indicators were selected for ML model development. All models exhibited excellent discriminative ability. XGBoost surpassed others, with AUROC of 0.873 internally and 0.844 externally. XGBoost outperformed other ML models (LR: 0.829; SVM: 0.830; DNN: 0.837) and clinical scores (Simplified Acute Physiology Score II: 0.728; Sequential Organ Failure Assessment: 0.728; Oxford Acute Severity of Illness Score: 0.738; Glasgow Coma Scale: 0.691). XGBoost's hospital mortality prediction achieved AUROC 0.873, sensitivity 0.818, accuracy 0.777, specificity 0.768, and F1 score 0.551. We crafted an interpretable model for sepsis death risk prediction. ML algorithms surpassed traditional scores for sepsis mortality forecast. Validation in a Chinese teaching hospital echoed these findings.
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Affiliation(s)
- Yiping Wang
- Department of Emergency, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China
| | - Zhihong Gao
- Department of Computer Technology and Information Management, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China
| | - Yang Zhang
- Department of Computer Technology and Information Management, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China
| | - Zhongqiu Lu
- Department of Emergency, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China.
| | - Fangyuan Sun
- Department of Computer Technology and Information Management, The First Affiliated Hospital of WenZhou Medical University, Wenzhou, 325000, China.
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Lu Y, Li Q, Li T. A novel hierarchical network-based approach to unveil the complexity of functional microbial genome. BMC Genomics 2024; 25:786. [PMID: 39138557 PMCID: PMC11323692 DOI: 10.1186/s12864-024-10692-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024] Open
Abstract
Biological networks serve a crucial role in elucidating intricate biological processes. While interspecies environmental interactions have been extensively studied, the exploration of gene interactions within species, particularly among individual microorganisms, is less developed. The increasing amount of microbiome genomic data necessitates a more nuanced analysis of microbial genome structures and functions. In this context, we introduce a complex structure using higher-order network theory, "Solid Motif Structures (SMS)", via a hierarchical biological network analysis of genomes within the same genus, effectively linking microbial genome structure with its function. Leveraging 162 high-quality genomes of Microcystis, a key freshwater cyanobacterium within microbial ecosystems, we established a genome structure network. Employing deep learning techniques, such as adaptive graph encoder, we uncovered 27 critical functional subnetworks and their associated SMSs. Incorporating metagenomic data from seven geographically distinct lakes, we conducted an investigation into Microcystis' functional stability under varying environmental conditions, unveiling unique functional interaction models for each lake. Our work compiles these insights into an extensive resource repository, providing novel perspectives on the functional dynamics within Microcystis. This research offers a hierarchical network analysis framework for understanding interactions between microbial genome structures and functions within the same genus.
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Affiliation(s)
- Yuntao Lu
- University of Michigan, Ann Arbor, USA
| | - Qi Li
- The State Key Laboratory of Freshwater Ecology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
| | - Tao Li
- The State Key Laboratory of Freshwater Ecology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
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Roslund MI, Nurminen N, Oikarinen S, Puhakka R, Grönroos M, Puustinen L, Kummola L, Parajuli A, Cinek O, Laitinen OH, Hyöty H, Sinkkonen A. Skin exposure to soil microbiota elicits changes in cell-mediated immunity to pneumococcal vaccine. Sci Rep 2024; 14:18573. [PMID: 39127736 DOI: 10.1038/s41598-024-68235-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
A resilient immune system is characterized by its capacity to respond appropriately to challenges, such as infections, and it is crucial in vaccine response. Here we report a paired randomized intervention-control trial in which we evaluated the effect of microbially rich soil on immune resilience and pneumococcal vaccine response. Twenty-five age and sex matched pairs of volunteers were randomized to intervention and control groups. The intervention group rubbed hands three times a day in microbially rich soil until participants received a pneumococcal vaccine on day 14. Vaccine response, skin and gut bacteriome and blood cytokine levels were analyzed on days 0, 14 and 35. Peripheral blood mononuclear cells (PBMCs) were stimulated with vaccine components and autoclaved soil for cytokine production. Commensal bacterial community shifted only in the intervention group during the 14-day intervention period. When PBMCs collected on day 14 before the vaccination were stimulated with the vaccine components, IFN-y production increased in the intervention but not in the control group. On day 35, vaccination induced a robust antibody response in both groups. In parallel, gut bacterial community was associated with TGF-β plasma levels and TGF-β decrease in plasma was lower in the intervention group. The results indicate that exposure to microbially rich soil can modulate the cell-mediated immunity to components in pneumococcal vaccine.
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Affiliation(s)
- Marja I Roslund
- Natural Resources Institute Finland, Luke, Viikki and Turku, Finland
| | - Noora Nurminen
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland
| | - Sami Oikarinen
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland
| | - Riikka Puhakka
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, 15140, Lahti, Finland
| | - Mira Grönroos
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, 15140, Lahti, Finland
| | - Leena Puustinen
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland
| | - Laura Kummola
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland
| | - Anirudra Parajuli
- Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Niemenkatu 73, 15140, Lahti, Finland
- Department of Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Ondřej Cinek
- Department of Medical Microbiology, 2nd Faculty of Medicine, Charles University, V Úvalu 84, Praha 5, 150 06, Prague, Czech Republic
| | - Olli H Laitinen
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland
| | - Heikki Hyöty
- Faculty of Medicine and Health Technology, Tampere University, Arvo Ylpön Katu 34, 33520, Tampere, Finland
- Fimlab Laboratories, Pirkanmaa Hospital District, Tampere, Finland
| | - Aki Sinkkonen
- Natural Resources Institute Finland, Luke, Viikki and Turku, Finland.
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50
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Tang F, Cui Q. Diverse roles of aldolase enzymes in cancer development, drug resistance and therapeutic approaches as moonlighting enzymes. Med Oncol 2024; 41:224. [PMID: 39120781 DOI: 10.1007/s12032-024-02470-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Accepted: 08/01/2024] [Indexed: 08/10/2024]
Abstract
Aldolase enzymes, particularly ALDOA, ALDOB, and ALDOC, play a crucial role in the development and progression of cancer. While the aldolase family is mainly known for its involvement in the glycolysis pathway, these enzymes also have various pathological and physiological functions through distinct signaling pathways such as Wnt/β-catenin, EGFR/MAPK, Akt, and HIF-1α. This has garnered increased attention in recent years and shed light on other sides of this enzyme. Potential therapeutic strategies targeting aldolases include using siRNA, inhibitors like naphthol AS-E phosphate and TX-2098, and natural compounds such as HDPS-4II and L-carnosine. Additionally, anticancer peptides derived from ALDOA, like P04, can potentially increase cancer cells' sensitivity to chemotherapy. Aldolases also affect cancer drug resistance by different approaches, making them good therapeutic targets. In this review, we extensively explore the role of aldolase enzymes in various types of cancers in proliferation, invasion, migration, and drug resistance; we also significantly explore the possible treatment considering aldolase function.
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Affiliation(s)
- Fan Tang
- General Surgery Department, Xinhua Hospital of Yili Kazak Autonomous Prefecture, YiLi, 835000, China
| | - Qingyang Cui
- Department of Interventional Oncology, Xinhua Hospital of Yili Kazak Autonomous Prefecture, YiLi, 835000, China.
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