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Dowrick JM, Roy NC, Bayer S, Frampton CMA, Talley NJ, Gearry RB, Angeli-Gordon TR. Unsupervised machine learning highlights the challenges of subtyping disorders of gut-brain interaction. Neurogastroenterol Motil 2024:e14898. [PMID: 39119757 DOI: 10.1111/nmo.14898] [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: 04/17/2024] [Revised: 07/17/2024] [Accepted: 07/31/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND Unsupervised machine learning describes a collection of powerful techniques that seek to identify hidden patterns in unlabeled data. These techniques can be broadly categorized into dimension reduction, which transforms and combines the original set of measurements to simplify data, and cluster analysis, which seeks to group subjects based on some measure of similarity. Unsupervised machine learning can be used to explore alternative subtyping of disorders of gut-brain interaction (DGBI) compared to the existing gastrointestinal symptom-based definitions of Rome IV. PURPOSE This present review aims to familiarize the reader with fundamental concepts of unsupervised machine learning using accessible definitions and provide a critical summary of their application to the evaluation of DGBI subtyping. By considering the overlap between Rome IV clinical definitions and identified clusters, along with clinical and physiological insights, this paper speculates on the possible implications for DGBI. Also considered are algorithmic developments in the unsupervised machine learning community that may help leverage increasingly available omics data to explore biologically informed definitions. Unsupervised machine learning challenges the modern subtyping of DGBI and, with the necessary clinical validation, has the potential to enhance future iterations of the Rome criteria to identify more homogeneous, diagnosable, and treatable patient populations.
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Affiliation(s)
- Jarrah M Dowrick
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
| | - Nicole C Roy
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- Department of Human Nutrition, University of Otago, Dunedin, New Zealand
- Riddet Institute, Massey University, Palmerston North, New Zealand
| | - Simone Bayer
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Chris M A Frampton
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Nicholas J Talley
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
| | - Richard B Gearry
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Timothy R Angeli-Gordon
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- High-Value Nutrition National Science Challenge, Auckland, New Zealand
- Riddet Institute, Massey University, Palmerston North, New Zealand
- Department of Surgery, University of Auckland, Auckland, New Zealand
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Huang H, Wu F, Yu Y, Xu B, Chen D, Huo Y, Li S. Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma. Front Immunol 2024; 15:1436131. [PMID: 39176099 PMCID: PMC11338809 DOI: 10.3389/fimmu.2024.1436131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/26/2024] [Indexed: 08/24/2024] Open
Abstract
Background Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive. Methods In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA). Results Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model. Conclusions Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making.
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Affiliation(s)
| | | | | | | | | | | | - Shaoqiang Li
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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Zhang G, Wang Z, Tong Z, Qin Z, Su C, Li D, Xu S, Li K, Zhou Z, Xu Y, Zhang S, Wu R, Li T, Zheng Y, Zhang J, Cheng K, Tang J. AI hybrid survival assessment for advanced heart failure patients with renal dysfunction. Nat Commun 2024; 15:6756. [PMID: 39117613 PMCID: PMC11310499 DOI: 10.1038/s41467-024-50415-9] [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: 09/18/2023] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system's robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.
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Affiliation(s)
- Ge Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China
| | - Zeyu Wang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China
| | - Zhuang Tong
- Henan Academy of Medical Big Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Zhen Qin
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China
| | - Chang Su
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China
| | - Demin Li
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China
| | - Shuai Xu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China
| | - Kaixiang Li
- Henan Academy of Medical Big Data, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Zhaokai Zhou
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
- Department of Pharmacy, The Second Xiangya Hospital of Central South University, Changsha, Hunan, 410011, China
| | - Yudi Xu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Shiqian Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Ruhao Wu
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Teng Li
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Youyang Zheng
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China
| | - Jinying Zhang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China.
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China.
| | - Ke Cheng
- Department of Biomedical Engineering, Columbia University, New York City, New York, 10032, NY, USA.
| | - Junnan Tang
- Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, China.
- Henan Province Key Laboratory of Cardiac Injury and Repair, Zhengzhou, Henan, 450052, China.
- Henan Province Clinical Research Center for Cardiovascular Diseases, Zhengzhou, 450052, Henan, China.
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Farooq MA, Gao S, Hassan MA, Huang Z, Rasheed A, Hearne S, Prasanna B, Li X, Li H. Artificial intelligence in plant breeding. Trends Genet 2024:S0168-9525(24)00167-7. [PMID: 39117482 DOI: 10.1016/j.tig.2024.07.001] [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: 04/30/2024] [Revised: 07/06/2024] [Accepted: 07/12/2024] [Indexed: 08/10/2024]
Abstract
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.
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Affiliation(s)
- Muhammad Amjad Farooq
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Shang Gao
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Muhammad Adeel Hassan
- Adaptive Cropping Systems Laboratory, Beltsville Agricultural Research Center, US Department of Agriculture, Beltsville, MD 20705, USA; Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA
| | - Zhangping Huang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Sarah Hearne
- CIMMYT, KM 45 Carretera Mexico-Veracruz, El Batan, Texcoco 56237, Mexico
| | - Boddupalli Prasanna
- CIMMYT, International Centre for Research in Agroforestry (ICRAF) House, Nairobi 00100, Kenya
| | - Xinhai Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China
| | - Huihui Li
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.
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Xu C, Yu X, Ding Z, Fang C, Gao M, Liu W, Liu X, Yin C, Gu R, Liu L, Li W, Wu SN, Cao B. Artificial intelligence-assisted metastasis and prognosis model for patients with nodular melanoma. PLoS One 2024; 19:e0305468. [PMID: 39110691 PMCID: PMC11305581 DOI: 10.1371/journal.pone.0305468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 05/23/2024] [Indexed: 08/10/2024] Open
Abstract
OBJECTIVE The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms. METHODS The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed. RESULTS Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness. CONCLUSION The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.
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Affiliation(s)
- Chan Xu
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China
| | - Xiaoyu Yu
- Department of Oncology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, China
| | - Zhendong Ding
- Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Caixia Fang
- Department of Pharmacy, Qingyang City People’s Hospital, Qingyang, China
| | - Murong Gao
- Beijing Rehabilitation Hospital Affiliated to Capital Medical University, Beijing, China
| | - Wencai Liu
- Department of Orthopaedics, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Xiaozhu Liu
- Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chengliang Yin
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Renjun Gu
- School of Chinese Medicine & School of Integrated Chinese and Western Medicine, Nanjing, University of Chinese Medicine, Nanjing, China
| | - Lu Liu
- Department of Dermatology, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Institute of Dermatology, Anhui Medical University, Hefei, Anhui, China
| | - Wenle Li
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China
| | - Shi-Nan Wu
- Eye Institute of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Bei Cao
- Department of Thyroid Surgery, The Affiliated Hospital, Southwest Medical University, Luzhou, China
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Peng S, Rajjou L. Advancing plant biology through deep learning-powered natural language processing. PLANT CELL REPORTS 2024; 43:208. [PMID: 39102077 DOI: 10.1007/s00299-024-03294-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 07/19/2024] [Indexed: 08/06/2024]
Abstract
The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.
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Affiliation(s)
- Shuang Peng
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000, Versailles, France
| | - Loïc Rajjou
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000, Versailles, France.
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Ahmad Z, Shareen, Ganie IB, Firdaus F, Ramakrishnan M, Shahzad A, Ding Y. Enhancing Withanolide Production in the Withania Species: Advances in In Vitro Culture and Synthetic Biology Approaches. PLANTS (BASEL, SWITZERLAND) 2024; 13:2171. [PMID: 39124289 PMCID: PMC11313931 DOI: 10.3390/plants13152171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/12/2024]
Abstract
Withanolides are naturally occurring steroidal lactones found in certain species of the Withania genus, especially Withania somnifera (commonly known as Ashwagandha). These compounds have gained considerable attention due to their wide range of therapeutic properties and potential applications in modern medicine. To meet the rapidly growing demand for withanolides, innovative approaches such as in vitro culture techniques and synthetic biology offer promising solutions. In recent years, synthetic biology has enabled the production of engineered withanolides using heterologous systems, such as yeast and bacteria. Additionally, in vitro methods like cell suspension culture and hairy root culture have been employed to enhance withanolide production. Nevertheless, one of the primary obstacles to increasing the production of withanolides using these techniques has been the intricacy of the biosynthetic pathways for withanolides. The present article examines new developments in withanolide production through in vitro culture. A comprehensive summary of viable traditional methods for producing withanolide is also provided. The development of withanolide production in heterologous systems is examined and emphasized. The use of machine learning as a potent tool to model and improve the bioprocesses involved in the generation of withanolide is then discussed. In addition, the control and modification of the withanolide biosynthesis pathway by metabolic engineering mediated by CRISPR are discussed.
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Affiliation(s)
- Zishan Ahmad
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| | - Shareen
- Department of Environmental Engineering, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, China;
| | - Irfan Bashir Ganie
- Department of Botany, Aligarh Muslim University, Aligarh 202002, India; (I.B.G.); (A.S.)
| | - Fatima Firdaus
- Chemistry Department, Lucknow University, Lucknow 226007, India;
| | - Muthusamy Ramakrishnan
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
| | - Anwar Shahzad
- Department of Botany, Aligarh Muslim University, Aligarh 202002, India; (I.B.G.); (A.S.)
| | - Yulong Ding
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Centre for Sustainable Forestry in Southern China, Bamboo Research Institute, Key Laboratory of National Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, School of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; (Z.A.); (M.R.)
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Hu Z, Hu Y, Zhang S, Dong L, Chen X, Yang H, Su L, Hou X, Huang X, Shen X, Ye C, Tu W, Chen Y, Chen Y, Cai S, Zhong J, Dong L. Machine-learning-based models assist the prediction of pulmonary embolism in autoimmune diseases: A retrospective, multicenter study. Chin Med J (Engl) 2024; 137:1811-1822. [PMID: 38863118 DOI: 10.1097/cm9.0000000000003025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD. METHODS In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort. RESULTS In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone. CONCLUSION ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. TRIAL REGISTRATION Chictr.org.cn : ChiCTR2200059599.
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Affiliation(s)
- Ziwei Hu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yangyang Hu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shuoqi Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Li Dong
- Department of Rheumatology and Immunology, Jingzhou Central Hospital, Yangtze University, Jinzhou, Hubei 434020, China
| | - Xiaoqi Chen
- Department of Rheumatology and Immunology, Zhongnan Hospital, Wuhan University, Wuhan, Hubei 430071, China
| | - Huiqin Yang
- Department of Rheumatology, Wuhan No.1 Hospital, Wuhan, Hubei 430022, China
| | - Linchong Su
- Department of Rheumatology, Minda Hospital of Hubei Minzu University, Enshi, Hubei 445000, China
| | - Xiaoqiang Hou
- Department of Rheumatology and Immunology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443003, China
| | - Xia Huang
- Department of Rheumatology, Minda Hospital of Hubei Minzu University, Enshi, Hubei 445000, China
| | - Xiaolan Shen
- Department of Rheumatology and Immunology, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei 443003, China
| | - Cong Ye
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Wei Tu
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yu Chen
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yuxue Chen
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Shaozhe Cai
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Jixin Zhong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Lingli Dong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
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Thümmel L, Tintner-Olifiers J, Amendt J. A methodological approach to age estimation of the intra-puparial period of the forensically relevant blow fly Calliphora vicina via Fourier transform infrared spectroscopy. MEDICAL AND VETERINARY ENTOMOLOGY 2024. [PMID: 39093723 DOI: 10.1111/mve.12748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024]
Abstract
Estimating the age of immature blow flies is of great importance for forensic entomology. However, no gold-standard technique for an accurate determination of the intra-puparial age has yet been established. Fourier transform infrared (FTIR) spectroscopy is a method to (bio-)chemically characterise material based on the absorbance of electromagnetic energy by functional groups of molecules. In recent years, it also has become a powerful tool in forensic and life sciences, as it is a fast and cost-effective way to characterise all kinds of material and biological traces. This study is the first to collect developmental reference data on the changes in absorption spectra during the intra-puparial period of the forensically important blow fly Calliphora vicina Robineau-Desvoidy (Diptera: Calliphoridae). Calliphora vicina was reared at constant 20°C and 25°C and specimens were killed every other day throughout their intra-puparial development. In order to investigate which part yields the highest detectable differences in absorption spectra throughout the intra-puparial development, each specimen was divided into two different subsamples: the pupal body and the former cuticle of the third instar, that is, the puparium. Absorption spectra were collected with a FTIR spectrometer coupled to an attenuated total reflection (ATR) unit. Classification accuracies of different wavenumber regions with two machine learning models, i.e., random forests (RF) and support vector machines (SVMs), were tested. The best age predictions for both temperature settings and machine learning models were obtained by using the full spectral range from 3700 to 600 cm-1. While SVMs resulted in better accuracies for C. vicina reared at 20°C, RFs performed almost as good as SVMs for data obtained from 25°C. In terms of sample type, the pupal body gave smoother spectra and usually better classification accuracies than the puparia. This study shows that FTIR spectroscopy is a promising technique in forensic entomology to support the estimation of the minimum post-mortem interval (PMImin), by estimating the age of a given insect specimen.
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Affiliation(s)
- Luise Thümmel
- Goethe-University Frankfurt, University Hospital, Institute of Legal Medicine, Frankfurt am Main, Germany
- Department of Aquatic Ecotoxicology, Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany
| | | | - Jens Amendt
- Goethe-University Frankfurt, University Hospital, Institute of Legal Medicine, Frankfurt am Main, Germany
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60
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Zhang Y, Liu LH, Xu B, Zhang Z, Yang M, He Y, Chen J, Zhang Y, Hu Y, Chen X, Sun Z, Ge Q, Wu S, Lei W, Li K, Cui H, Yang G, Zhao X, Wang M, Xia J, Cao Z, Jiang A, Wu YR. Screening antimicrobial peptides and probiotics using multiple deep learning and directed evolution strategies. Acta Pharm Sin B 2024; 14:3476-3492. [PMID: 39234615 PMCID: PMC11372459 DOI: 10.1016/j.apsb.2024.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/25/2024] [Accepted: 05/06/2024] [Indexed: 09/06/2024] Open
Abstract
Owing to their limited accuracy and narrow applicability, current antimicrobial peptide (AMP) prediction models face obstacles in industrial application. To address these limitations, we developed and improved an AMP prediction model using Comparing and Optimizing Multiple DEep Learning (COMDEL) algorithms, coupled with high-throughput AMP screening method, finally reaching an accuracy of 94.8% in test and 88% in experiment verification, surpassing other state-of-the-art models. In conjunction with COMDEL, we employed the phage-assisted evolution method to screen Sortase in vivo and developed a cell-free AMP synthesis system in vitro, ultimately increasing AMPs yields to a range of 0.5-2.1 g/L within hours. Moreover, by multi-omics analysis using COMDEL, we identified Lactobacillus plantarum as the most promising candidate for AMP generation among 35 edible probiotics. Following this, we developed a microdroplet sorting approach and successfully screened three L. plantarum mutants, each showing a twofold increase in antimicrobial ability, underscoring their substantial industrial application values.
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Affiliation(s)
- Yu Zhang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Li-Hua Liu
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
- Biology Department and Institute of Marine Sciences, College of Science, Shantou University, Shantou 515063, China
| | - Bo Xu
- School of Basic Medical Sciences, Hubei University of Science and Technology, Xianning 437100, China
| | - Zhiqian Zhang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Min Yang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Yiyang He
- School of Education, Jianghan University, Wuhan 430056, China
| | - Jingjing Chen
- Yeasen Biotechnology (Shanghai) Co., Ltd., Shanghai 200000, China
| | - Yang Zhang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Yucheng Hu
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Xipeng Chen
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Zitong Sun
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Qijun Ge
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Song Wu
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Wei Lei
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Kaizheng Li
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Hua Cui
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Gangzhu Yang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Xuemei Zhao
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Man Wang
- Yeasen Biotechnology (Shanghai) Co., Ltd., Shanghai 200000, China
| | - Jiaqi Xia
- School of Basic Medicine, Jiamusi University, Jiamusi 154000, China
| | - Zhen Cao
- Yeasen Biotechnology (Shanghai) Co., Ltd., Shanghai 200000, China
| | - Ao Jiang
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
| | - Yi-Rui Wu
- Tidetron Bioworks Technology (Guangzhou) Co., Ltd., Guangzhou Qianxiang Bioworks Co., Ltd., Guangzhou 510000, China
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61
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Kieran TJ, Maines TR, Belser JA. Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold. PLoS Pathog 2024; 20:e1012460. [PMID: 39208339 PMCID: PMC11361667 DOI: 10.1371/journal.ppat.1012460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Affiliation(s)
- Troy J. Kieran
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Taronna R. Maines
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
| | - Jessica A. Belser
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
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62
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Guo Z, Wang S, Wang Y, Wang Z, Ou G. A machine learning enhanced EMS mutagenesis probability map for efficient identification of causal mutations in Caenorhabditis elegans. PLoS Genet 2024; 20:e1011377. [PMID: 39186782 PMCID: PMC11379379 DOI: 10.1371/journal.pgen.1011377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 09/06/2024] [Accepted: 07/27/2024] [Indexed: 08/28/2024] Open
Abstract
Chemical mutagenesis-driven forward genetic screens are pivotal in unveiling gene functions, yet identifying causal mutations behind phenotypes remains laborious, hindering their high-throughput application. Here, we reveal a non-uniform mutation rate caused by Ethyl Methane Sulfonate (EMS) mutagenesis in the C. elegans genome, indicating that mutation frequency is influenced by proximate sequence context and chromatin status. Leveraging these factors, we developed a machine learning enhanced pipeline to create a comprehensive EMS mutagenesis probability map for the C. elegans genome. This map operates on the principle that causative mutations are enriched in genetic screens targeting specific phenotypes among random mutations. Applying this map to Whole Genome Sequencing (WGS) data of genetic suppressors that rescue a C. elegans ciliary kinesin mutant, we successfully pinpointed causal mutations without generating recombinant inbred lines. This method can be adapted in other species, offering a scalable approach for identifying causal genes and revitalizing the effectiveness of forward genetic screens.
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Affiliation(s)
- Zhengyang Guo
- Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, McGovern Institute for Brain Research, State Key Laboratory of Membrane Biology, School of Life Sciences and MOE Key Laboratory for Protein Science, Tsinghua University, Beijing, China
| | - Shimin Wang
- Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, McGovern Institute for Brain Research, State Key Laboratory of Membrane Biology, School of Life Sciences and MOE Key Laboratory for Protein Science, Tsinghua University, Beijing, China
| | - Yang Wang
- Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, McGovern Institute for Brain Research, State Key Laboratory of Membrane Biology, School of Life Sciences and MOE Key Laboratory for Protein Science, Tsinghua University, Beijing, China
| | - Zi Wang
- Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, McGovern Institute for Brain Research, State Key Laboratory of Membrane Biology, School of Life Sciences and MOE Key Laboratory for Protein Science, Tsinghua University, Beijing, China
| | - Guangshuo Ou
- Tsinghua-Peking Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, McGovern Institute for Brain Research, State Key Laboratory of Membrane Biology, School of Life Sciences and MOE Key Laboratory for Protein Science, Tsinghua University, Beijing, China
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63
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Kieran TJ, Sun X, Maines TR, Belser JA. Machine learning approaches for influenza A virus risk assessment identifies predictive correlates using ferret model in vivo data. Commun Biol 2024; 7:927. [PMID: 39090358 PMCID: PMC11294530 DOI: 10.1038/s42003-024-06629-0] [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: 01/16/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
In vivo assessments of influenza A virus (IAV) pathogenicity and transmissibility in ferrets represent a crucial component of many pandemic risk assessment rubrics, but few systematic efforts to identify which data from in vivo experimentation are most useful for predicting pathogenesis and transmission outcomes have been conducted. To this aim, we aggregated viral and molecular data from 125 contemporary IAV (H1, H2, H3, H5, H7, and H9 subtypes) evaluated in ferrets under a consistent protocol. Three overarching predictive classification outcomes (lethality, morbidity, transmissibility) were constructed using machine learning (ML) techniques, employing datasets emphasizing virological and clinical parameters from inoculated ferrets, limited to viral sequence-based information, or combining both data types. Among 11 different ML algorithms tested and assessed, gradient boosting machines and random forest algorithms yielded the highest performance, with models for lethality and transmission consistently better performing than models predicting morbidity. Comparisons of feature selection among models was performed, and highest performing models were validated with results from external risk assessment studies. Our findings show that ML algorithms can be used to summarize complex in vivo experimental work into succinct summaries that inform and enhance risk assessment criteria for pandemic preparedness that take in vivo data into account.
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Affiliation(s)
- Troy J Kieran
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
| | - Xiangjie Sun
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Taronna R Maines
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Jessica A Belser
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA.
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64
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Huang B, Guo L, Yin H, Wu Y, Zeng Z, Xu S, Lou Y, Ai Z, Zhang W, Kan X, Yu Q, Du S, Li C, Wu L, Huang X, Wang S, Wang X. Deep learning enhancing guide RNA design for CRISPR/Cas12a-based diagnostics. IMETA 2024; 3:e214. [PMID: 39135699 PMCID: PMC11316927 DOI: 10.1002/imt2.214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 08/15/2024]
Abstract
Rapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a-based detection system has emerged as a promising solution for on-site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a-based detection remains challenging and time-consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a-mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a-based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web-based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a-based detection, which could serve as a valuable tool for clinical diagnostics and research applications.
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Affiliation(s)
| | | | | | - Yue Wu
- Zhejiang LabHangzhouChina
| | | | | | - Yufeng Lou
- Department of Laboratory Medicine, The First Affiliated HospitalZhejiang University School of MedicineHangzhouChina
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang ProvinceHangzhouChina
- Institute of Laboratory MedicineZhejiang UniversityHangzhouChina
| | | | | | | | | | | | - Chao Li
- Department of Applied Mathematics and Theoretical PhysicsUniversity of CambridgeCambridgeUK
- School of Medicine, School of Science and EngineeringUniversity of Dundee, NethergateDundeeUK
| | - Lina Wu
- School of Food Science and Pharmaceutical EngineeringNanjing Normal UniversityNanjingChina
| | | | | | - Xinjie Wang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenChina
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65
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Liu R, Zhu G, Gao Y, Li D. An rs-fMRI based neuroimaging marker for adult absence epilepsy. Epilepsy Res 2024; 204:107400. [PMID: 38954950 DOI: 10.1016/j.eplepsyres.2024.107400] [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: 03/12/2024] [Revised: 06/21/2024] [Accepted: 06/27/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVE Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE. METHODS This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing. RESULTS Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %. SIGNIFICANCE According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.
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Affiliation(s)
- Ruoshi Liu
- Department of Neurology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Guozhong Zhu
- Department of Medical Imaging, Heilongjiang Provincial Hospital, Harbin, China
| | - Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dongbin Li
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China; Department of Neurology and Neuroscience Center, Heilongjiang Provincial Hospital, Harbin, China.
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66
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Feng R, Xu T, Xie X, Zhang ZK, Liu C, Zhan XX. A hyper-distance-based method for hypernetwork comparison. CHAOS (WOODBURY, N.Y.) 2024; 34:083120. [PMID: 39146451 DOI: 10.1063/5.0221267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Accepted: 07/23/2024] [Indexed: 08/17/2024]
Abstract
Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks; however, the comparison of the difference between two hypernetworks has received less attention. This paper proposes a hyper-distance (HD)-based method for comparing hypernetworks. The method is based on higher-order information, i.e, the higher-order distance between nodes and Jensen-Shannon divergence. Experiments carried out on synthetic hypernetworks have shown that HD is capable of distinguishing between hypernetworks generated with different parameters, and it is successful in the classification of hypernetworks. Furthermore, HD outperforms current state-of-the-art baselines to distinguish empirical hypernetworks when hyperedges are randomly perturbed.
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Affiliation(s)
- Ruonan Feng
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, People's Republic of China
| | - Tao Xu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, People's Republic of China
| | - Xiaowen Xie
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, People's Republic of China
| | - Zi-Ke Zhang
- College of Media and International Culture, Zhejiang University, Hangzhou 310058, People's Republic of China
| | - Chuang Liu
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, People's Republic of China
| | - Xiu-Xiu Zhan
- Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, People's Republic of China
- College of Media and International Culture, Zhejiang University, Hangzhou 310058, People's Republic of China
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67
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Song Y, Prather KLJ. Strategies in engineering sustainable biochemical synthesis through microbial systems. Curr Opin Chem Biol 2024; 81:102493. [PMID: 38971129 DOI: 10.1016/j.cbpa.2024.102493] [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: 04/16/2024] [Revised: 05/24/2024] [Accepted: 06/05/2024] [Indexed: 07/08/2024]
Abstract
Growing environmental concerns and the urgency to address climate change have increased demand for the development of sustainable alternatives to fossil-derived fuels and chemicals. Microbial systems, possessing inherent biosynthetic capabilities, present a promising approach for achieving this goal. This review discusses the coupling of systems and synthetic biology to enable the elucidation and manipulation of microbial phenotypes for the production of chemicals that can substitute for petroleum-derived counterparts and contribute to advancing green biotechnology. The integration of artificial intelligence with metabolic engineering to facilitate precise and data-driven design of biosynthetic pathways is also discussed, along with the identification of current limitations and proposition of strategies for optimizing biosystems, thereby propelling the field of chemical biology towards sustainable chemical production.
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Affiliation(s)
- Yoseb Song
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Kristala L J Prather
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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68
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Yang XL, Zeng Z, Wang C, Wang GY, Zhang FQ. Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms. Immunol Res 2024; 72:851-863. [PMID: 38755433 DOI: 10.1007/s12026-024-09492-7] [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: 12/03/2023] [Accepted: 05/09/2024] [Indexed: 05/18/2024]
Abstract
This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.
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Affiliation(s)
- Xi-Lin Yang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Zheng Zeng
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Chen Wang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Guang-Yu Wang
- Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Fu-Quan Zhang
- Department of Radiation Oncology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
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69
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An X, Zhao S, Fang J, Li Q, Yue C, Jing C, Zhang Y, Zhang J, Zhou J, Chen C, Qu H, Ma Q, Lin Q. Identification of genetic susceptibility for Chinese migraine with depression using machine learning. Front Neurol 2024; 15:1418529. [PMID: 39144710 PMCID: PMC11322385 DOI: 10.3389/fneur.2024.1418529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 07/11/2024] [Indexed: 08/16/2024] Open
Abstract
Background Migraine is a common primary headache that has a significant impact on patients' quality of life. The co-occurrence of migraine and depression is frequent, resulting in more complex symptoms and a poorer prognosis. The evidence suggests that depression and migraine comorbidity share a polygenic genetic background. Objective The aim of this study is to identify related genetic variants that contribute to genetic susceptibility to migraine with and without depression in a Chinese cohort. Methods In this case-control study, 263 individuals with migraines and 223 race-matched controls were included. Eight genetic polymorphism loci selected from the GWAS were genotyped using Sequenom's MALDI-TOF iPLEX platform. Results In univariate analysis, ANKDD1B rs904743 showed significant differences in genotype and allele distribution between migraineurs and controls. Furthermore, a machine learning approach was used to perform multivariate analysis. The results of the Random Forest algorithm indicated that ANKDD1B rs904743 was a significant risk factor for migraine susceptibility in China. Additionally, subgroup analysis by the Boruta algorithm showed a significant association between this SNP and migraine comorbid depression. Migraineurs with depression have been observed to have worse scores on the Beck Anxiety Inventory (BAI) and the Migraine Disability Assessment Scale (MIDAS). Conclusion The study indicates that there is an association between ANKDD1B rs904743 and susceptibility to migraine with and without depression in Chinese patients.
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Affiliation(s)
- Xingkai An
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Shanshan Zhao
- Department of Pediatrics, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Jie Fang
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Qingfang Li
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Cen Yue
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Chuya Jing
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Yidan Zhang
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Jiawei Zhang
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Jie Zhou
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Caihong Chen
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Hongli Qu
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Qilin Ma
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
| | - Qing Lin
- Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China
- Xiamen Key Laboratory of Brain Center, Xiamen, China
- Xiamen Medical Quality Control Center for Neurology, Xiamen, China
- Fujian Provincial Clinical Research Center for Brain Diseases, Fuzhou, China
- Xiamen Clinical Research Center for Neurological Diseases, Xiamen, China
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Zheng S, He H, Zheng J, Zhu X, Lin N, Wu Q, Wei E, Weng C, Chen S, Huang X, Jian C, Guan S, Yang C. Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer. Sci Rep 2024; 14:17679. [PMID: 39085446 PMCID: PMC11291988 DOI: 10.1038/s41598-024-68706-y] [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: 04/19/2024] [Accepted: 07/26/2024] [Indexed: 08/02/2024] Open
Abstract
Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal cancer. Limited research has been conducted on how CRLM develops. RNA sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.
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Affiliation(s)
- Shiyao Zheng
- Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Hongxin He
- Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Jianfeng Zheng
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Xingshu Zhu
- Department of General Surgery, 900TH Hospital of Joint Logistics Support Force, Fuzhou, 350025, People's Republic of China
| | - Nan Lin
- Department of General Surgery, 900TH Hospital of Joint Logistics Support Force, Fuzhou, 350025, People's Republic of China
- Fuzong Clinical Medical College of Fujian Medical University, Department of General Surgery, 900th Hospital of Joint Logistics Support Force, PLA, Fuzhou, 350025, People's Republic of China
| | - Qing Wu
- Department of Oncology, Molecular Oncology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People's Republic of China
| | - Enhao Wei
- Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Caiming Weng
- Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, 350002, People's Republic of China
| | - Shuqian Chen
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, 350001, People's Republic of China
| | - Xinxiang Huang
- Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China
| | - Chenxing Jian
- School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, People's Republic of China.
- Department of Anorectal Surgery, Afliated Hospital of Putian University, Putian, 351106, People's Republic of China.
| | - Shen Guan
- Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China.
| | - Chunkang Yang
- Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China.
- Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, 350014, People's Republic of China.
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71
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Ding N, Yuan Z, Ma Z, Wu Y, Yin L. AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors. Molecules 2024; 29:3512. [PMID: 39124917 PMCID: PMC11313831 DOI: 10.3390/molecules29153512] [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: 06/27/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.
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Affiliation(s)
- Nana Ding
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China;
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Zenan Yuan
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China;
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
| | - Zheng Ma
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection & Quarantine, College of Life Sciences, China Jiliang University, Hangzhou 310018, China;
| | - Yefei Wu
- Zhejiang Qianjiang Biochemical Co., Ltd., Haining 314400, China;
| | - Lianghong Yin
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China;
- Zhejiang Provincial Key Laboratory of Resources Protection and Innovation of Traditional Chinese Medicine, Zhejiang A&F University, Hangzhou 311300, China
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72
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de Almeida OGG, von Zeska Kress MR. Harnessing Machine Learning to Uncover Hidden Patterns in Azole-Resistant CYP51/ERG11 Proteins. Microorganisms 2024; 12:1525. [PMID: 39203367 PMCID: PMC11356363 DOI: 10.3390/microorganisms12081525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Revised: 07/21/2024] [Accepted: 07/22/2024] [Indexed: 09/03/2024] Open
Abstract
Fungal resistance is a public health concern due to the limited availability of antifungal resources and the complexities associated with treating persistent fungal infections. Azoles are thus far the primary line of defense against fungi. Specifically, azoles inhibit the conversion of lanosterol to ergosterol, producing defective sterols and impairing fluidity in fungal plasmatic membranes. Studies on azole resistance have emphasized specific point mutations in CYP51/ERG11 proteins linked to resistance. Although very insightful, the traditional approach to studying azole resistance is time-consuming and prone to errors during meticulous alignment evaluation. It relies on a reference-based method using a specific protein sequence obtained from a wild-type (WT) phenotype. Therefore, this study introduces a machine learning (ML)-based approach utilizing molecular descriptors representing the physiochemical attributes of CYP51/ERG11 protein isoforms. This approach aims to unravel hidden patterns associated with azole resistance. The results highlight that descriptors related to amino acid composition and their combination of hydrophobicity and hydrophilicity effectively explain the slight differences between the resistant non-wild-type (NWT) and WT (nonresistant) protein sequences. This study underscores the potential of ML to unravel nuanced patterns in CYP51/ERG11 sequences, providing valuable molecular signatures that could inform future endeavors in drug development and computational screening of resistant and nonresistant fungal lineages.
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Affiliation(s)
| | - Marcia Regina von Zeska Kress
- Faculdade de Ciências Farmacêuticas de Ribeirao Preto, Universidade de São Paulo, Ribeirão Preto 14040-903, SP, Brazil;
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Zhang S, Li P, Wang S, Zhu J, Huang Z, Cai F, Freidel S, Ling F, Schwarz E, Chen J. BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data. Brief Bioinform 2024; 25:bbae384. [PMID: 39126426 PMCID: PMC11316398 DOI: 10.1093/bib/bbae384] [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: 03/06/2024] [Revised: 06/15/2024] [Accepted: 07/24/2024] [Indexed: 08/12/2024] Open
Abstract
Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratifications of predictor variables, leading to algorithms that are both more accurate and generalizable. However, the wider availability of machine learning tools capable of incorporating such biological knowledge remains limited. Addressing this gap, we introduce BioM2, a novel R package designed for biologically informed multistage machine learning. BioM2 uniquely leverages biological information to effectively stratify and aggregate high-dimensional biological data in the context of machine learning. Demonstrating its utility with genome-wide DNA methylation and transcriptome-wide gene expression data, BioM2 has shown to enhance predictive performance, surpassing traditional machine learning models that operate without the integration of biological knowledge. A key feature of BioM2 is its ability to rank predictor variables within biological categories, specifically Gene Ontology pathways. This functionality not only aids in the interpretability of the results but also enables a subsequent modular network analysis of these variables, shedding light on the intricate systems-level biology underpinning the predictive outcome. We have proposed a biologically informed multistage machine learning framework termed BioM2 for phenotype prediction based on omics data. BioM2 has been incorporated into the BioM2 CRAN package (https://cran.r-project.org/web/packages/BioM2/index.html).
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Affiliation(s)
- Shunjie Zhang
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Pan Li
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, No. 6, 2nd Nanjiang Road, Nansha District, 511462 Guangzhou, China
| | - Shenghan Wang
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, No. 6, 2nd Nanjiang Road, Nansha District, 511462 Guangzhou, China
| | - Jijun Zhu
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, No. 6, 2nd Nanjiang Road, Nansha District, 511462 Guangzhou, China
| | - Zhongting Huang
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, No. 6, 2nd Nanjiang Road, Nansha District, 511462 Guangzhou, China
| | - Fuqiang Cai
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Sebastian Freidel
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, M7, Mannheim 68161, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim 68159, Germany
| | - Fei Ling
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou, China
| | - Emanuel Schwarz
- Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, M7, Mannheim 68161, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, Mannheim 68159, Germany
| | - Junfang Chen
- Center for Intelligent Medicine, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, No. 6, 2nd Nanjiang Road, Nansha District, 511462 Guangzhou, China
- Center for Evolutionary Biology, School of Life Sciences, Fudan University, Shanghai, China
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Liu X, Shi J, Jiao Y, An J, Tian J, Yang Y, Zhuo L. Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. Brief Bioinform 2024; 25:bbae364. [PMID: 39082652 PMCID: PMC11289682 DOI: 10.1093/bib/bbae364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 06/20/2024] [Accepted: 07/17/2024] [Indexed: 08/03/2024] Open
Abstract
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.
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Affiliation(s)
| | | | | | | | | | | | - Li Zhuo
- Corresponding author. Department of Nephrology, China-Japan Friendship Hospital, Beijing 100029, China; China-Japan Friendship Clinic Medical College, Beijing University of Chinese Medicine, 100029 Beijing, China. E-mail:
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Li Y, Cai Y, Ji L, Wang B, Shi D, Li X. Machine learning and bioinformatics analysis of diagnostic biomarkers associated with the occurrence and development of lung adenocarcinoma. PeerJ 2024; 12:e17746. [PMID: 39071134 PMCID: PMC11276766 DOI: 10.7717/peerj.17746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 06/24/2024] [Indexed: 07/30/2024] Open
Abstract
Objective Lung adenocarcinoma poses a major global health challenge and is a leading cause of cancer-related deaths worldwide. This study is a review of three molecular biomarkers screened by machine learning that are not only important in the occurrence and progression of lung adenocarcinoma but also have the potential to serve as biomarkers for clinical diagnosis, prognosis evaluation and treatment guidance. Methods Differentially expressed genes (DEGs) were identified using comprehensive GSE1987 and GSE18842 gene expression databases. A comprehensive bioinformatics analysis of these DEGs was conducted to explore enriched functions and pathways, relative expression levels, and interaction networks. Random Forest and LASSO regression analysis techniques were used to identify the three most significant target genes. The TCGA database and quantitative polymerase chain reaction (qPCR) experiments were used to verify the expression levels and receiver operating characteristic (ROC) curves of these three target genes. Furthermore, immune invasiveness, pan-cancer, and mRNA-miRNA interaction network analyses were performed. Results Eighty-nine genes showed increased expression and 190 genes showed decreased expression. Notably, the upregulated DEGs were predominantly associated with organelle fission and nuclear division, whereas the downregulated DEGs were mainly associated with genitourinary system development and cell-substrate adhesion. The construction of the DEG protein-protein interaction network revealed 32 and 19 hub genes with the highest moderate values among the upregulated and downregulated genes, respectively. Using random forest and LASSO regression analyses, the hub genes were employed to identify three most significant target genes.TCGA database and qPCR experiments were used to verify the expression levels and ROC curves of these three target genes, and immunoinvasive analysis, pan-cancer analysis and mRNA-miRNA interaction network analysis were performed. Conclusion Three target genes identified by machine learning: BUB1B, CENPF, and PLK1 play key roles in LUAD development of lung adenocarcinoma.
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Affiliation(s)
- Yong Li
- Department of Clinical Laboratory, The First Affiliated Hospital of Huzhou University, The First People’s Hospital of Huzhou City, Zhejiang Province, China
- School of Medical Technology and Information Engineering, Zhejiang University of Traditional Chinese Medicine, Zhejiang Province, China
| | - Yunxiang Cai
- Department of Clinical Laboratory, The First Affiliated Hospital of Huzhou University, The First People’s Hospital of Huzhou City, Zhejiang Province, China
| | - Longfei Ji
- Department of Clinical Laboratory, The First Affiliated Hospital of Huzhou University, The First People’s Hospital of Huzhou City, Zhejiang Province, China
| | - Binyu Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Huzhou University, The First People’s Hospital of Huzhou City, Zhejiang Province, China
| | - Danfei Shi
- Department of Pathology, The First Affiliated Hospital of Huzhou University, The First People’s Hospital of Huzhou City, Zhejiang Province, China
| | - Xinmin Li
- Department of Clinical Laboratory, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
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76
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Diao B, Luo J, Guo Y. A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs. Brief Funct Genomics 2024; 23:314-324. [PMID: 38576205 DOI: 10.1093/bfgp/elae010] [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: 12/06/2023] [Revised: 02/25/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.
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Affiliation(s)
- Biyu Diao
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Jin Luo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Yu Guo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
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Li W, Guo E, Zhao H, Li Y, Miao L, Liu C, Sun W. Evaluation of transfer ensemble learning-based convolutional neural network models for the identification of chronic gingivitis from oral photographs. BMC Oral Health 2024; 24:814. [PMID: 39020332 PMCID: PMC11256452 DOI: 10.1186/s12903-024-04460-x] [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: 01/23/2024] [Accepted: 06/07/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND To evaluate the performances of several advanced deep convolutional neural network models (AlexNet, VGG, GoogLeNet, ResNet) based on ensemble learning for recognizing chronic gingivitis from screening oral images. METHODS A total of 683 intraoral clinical images acquired from 134 volunteers were used to construct the database and evaluate the models. Four deep ConvNet models were developed using ensemble learning and outperformed a single model. The performances of the different models were evaluated by comparing the accuracy and sensitivity for recognizing the existence of gingivitis from intraoral images. RESULTS The ResNet model achieved an area under the curve (AUC) value of 97%, while the AUC values for the GoogLeNet, AlexNet, and VGG models were 94%, 92%, and 89%, respectively. Although the ResNet and GoogLeNet models performed best in classifying gingivitis from images, the sensitivity outcomes were not significantly different among the ResNet, GoogLeNet, and Alexnet models (p>0.05). However, the sensitivity of the VGGNet model differed significantly from those of the other models (p < 0.001). CONCLUSION The ResNet and GoogLeNet models show promise for identifying chronic gingivitis from images. These models can help doctors diagnose periodontal diseases efficiently or based on self-examination of the oral cavity by patients.
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Affiliation(s)
- Wen Li
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China
| | - Enting Guo
- Division of Computer Science, The University of Aizu, Aizu, Japan
| | - Hong Zhao
- Division of Computer Science, The University of Aizu, Aizu, Japan
| | - Yuyang Li
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China
| | - Leiying Miao
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China
| | - Chao Liu
- Department of Orthodontic, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China.
| | - Weibin Sun
- Department of Periodontics, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Research Institute of Stomatology, Nanjing University, Nanjing, China.
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Yang Z, Yang Y, Han X, Hou J. Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis. Int J Mol Sci 2024; 25:7754. [PMID: 39062997 PMCID: PMC11277372 DOI: 10.3390/ijms25147754] [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: 06/11/2024] [Revised: 07/08/2024] [Accepted: 07/13/2024] [Indexed: 07/28/2024] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secreting and regenerative functions in the alveolus, essential for maintaining lung homeostasis. However, a comprehensive investigation into their cellular diversity, molecular features, and clinical implications is currently lacking. In this study, we conducted a comprehensive examination of single-cell RNA sequencing data from both normal and fibrotic lung tissues. We analyzed alterations in cellular composition between IPF and normal tissue and investigated differentially expressed genes across each cell population. This analysis revealed the presence of two distinct subpopulations of IPF-related alveolar epithelial type II cells (IR_AT2). Subsequently, three unique gene co-expression modules associated with the IR_AT2 subtype were identified through the use of hdWGCNA. Furthermore, we refined and identified IPF-related AT2-related gene (IARG) signatures using various machine learning algorithms. Our analysis demonstrated a significant association between high IARG scores in IPF patients and shorter survival times (p-value < 0.01). Additionally, we observed a negative correlation between the percent predicted diffusing capacity for lung carbon monoxide (% DLCO) and increased IARG scores (cor = -0.44, p-value < 0.05). The cross-validation findings demonstrated a high level of accuracy (AUC > 0.85, p-value < 0.01) in the prognostication of patients with IPF utilizing the identified IARG signatures. Our study has identified distinct molecular and biological features among AT2 subpopulations, specifically highlighting the unique characteristics of IPF-related AT2 cells. Importantly, our findings underscore the prognostic relevance of specific genes associated with IPF-related AT2 cells, offering valuable insights into the advancement of IPF.
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Affiliation(s)
| | | | - Xin Han
- Department of Biochemistry and Molecular Biology, School of Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Z.Y.); (Y.Y.)
| | - Jiwei Hou
- Department of Biochemistry and Molecular Biology, School of Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China; (Z.Y.); (Y.Y.)
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79
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Bas TG, Duarte V. Biosimilars in the Era of Artificial Intelligence-International Regulations and the Use in Oncological Treatments. Pharmaceuticals (Basel) 2024; 17:925. [PMID: 39065775 PMCID: PMC11279612 DOI: 10.3390/ph17070925] [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: 05/16/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
This research is based on three fundamental aspects of successful biosimilar development in the challenging biopharmaceutical market. First, biosimilar regulations in eight selected countries: Japan, South Korea, the United States, Canada, Brazil, Argentina, Australia, and South Africa, represent the four continents. The regulatory aspects of the countries studied are analyzed, highlighting the challenges facing biosimilars, including their complex approval processes and the need for standardized regulatory guidelines. There is an inconsistency depending on whether the biosimilar is used in a developed or developing country. In the countries observed, biosimilars are considered excellent alternatives to patent-protected biological products for the treatment of chronic diseases. In the second aspect addressed, various analytical AI modeling methods (such as machine learning tools, reinforcement learning, supervised, unsupervised, and deep learning tools) were analyzed to observe patterns that lead to the prevalence of biosimilars used in cancer to model the behaviors of the most prominent active compounds with spectroscopy. Finally, an analysis of the use of active compounds of biosimilars used in cancer and approved by the FDA and EMA was proposed.
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Affiliation(s)
- Tomas Gabriel Bas
- Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1781421, Chile;
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80
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Calogero AE, Crafa A, Cannarella R, Saleh R, Shah R, Agarwal A. Artificial intelligence in andrology - fact or fiction: essential takeaway for busy clinicians. Asian J Androl 2024:00129336-990000000-00203. [PMID: 38978280 DOI: 10.4103/aja202431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/25/2024] [Indexed: 07/10/2024] Open
Abstract
ABSTRACT Artificial intelligence (AI) is revolutionizing the current approach to medicine. AI uses machine learning algorithms to predict the success of therapeutic procedures or assist the clinician in the decision-making process. To date, machine learning studies in the andrological field have mainly focused on prostate cancer imaging and management. However, an increasing number of studies are documenting the use of AI to assist clinicians in decision-making and patient management in andrological diseases such as varicocele or sexual dysfunction. Additionally, machine learning applications are being employed to enhance success rates in assisted reproductive techniques (ARTs). This article offers the clinicians as well as the researchers with a brief overview of the current use of AI in andrology, highlighting the current state-of-the-art scientific evidence, the direction in which the research is going, and the strengths and limitations of this approach.
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Affiliation(s)
- Aldo E Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
| | - Andrea Crafa
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania 95123, Italy
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH 44195, USA
| | - Ramadan Saleh
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag 82524, Egypt
- Ajyal IVF Center, Ajyal Hospital, Sohag 82511, Egypt
| | - Rupin Shah
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Division of Andrology, Department of Urology, Lilavati Hospital and Research Centre, Mumbai 400050, India
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH 44022, USA
- Cleveland Clinic Foundation, Cleveland, OH 44195, USA
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81
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Wang J, Jia B, Miao J, Li D, Wang Y, Han L, Yuan Y, Zhang Y, Wang Y, Guo L, Jia J, Zheng F, Lai S, Niu K, Li W, Bian Y, Wang Y. An novel effective and safe model for the diagnosis of nonalcoholic fatty liver disease in China: gene excavations, clinical validations, and mechanism elucidation. J Transl Med 2024; 22:624. [PMID: 38965537 PMCID: PMC11225259 DOI: 10.1186/s12967-024-05315-3] [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: 12/07/2023] [Accepted: 05/20/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases. NAFLD leads to liver fibrosis and hepatocellular carcinoma, and it also has systemic effects associated with metabolic diseases, cardiovascular diseases, chronic kidney disease, and malignant tumors. Therefore, it is important to diagnose NAFLD early to prevent these adverse effects. METHODS The GSE89632 dataset was downloaded from the Gene Expression Omnibus database, and then the optimal genes were screened from the data cohort using lasso and Support Vector Machine Recursive Feature Elimination (SVM-RFE). The ROC values of the optimal genes for the diagnosis of NAFLD were calculated. The relationship between optimal genes and immune cells was determined using the DECONVOLUTION algorithm CIBERSORT. Finally, the specificity and sensitivity of the diagnostic genes were verified by detecting the expression of the diagnostic genes in blood samples from 320 NAFLD patients and liver samples from 12 mice. RESULTS Through machine learning we identified FOSB, GPAT3, RGCC and RNF43 were the key diagnostic genes for NAFLD, and they were further demonstrated by a receiver operating characteristic curve analysis. We found that the combined diagnosis of the four genes identified NAFLD samples well from normal samples (AUC = 0.997). FOSB, GPAT3, RGCC and RNF43 were strongly associated with immune cell infiltration. We also experimentally examined the expression of these genes in NAFLD patients and NAFLD mice, and the results showed that these genes are highly specific and sensitive. CONCLUSIONS Data from both clinical and animal studies demonstrate the high sensitivity, specificity and safety of FOSB, GPAT3, RGCC and RNF43 for the diagnosis of NAFLD. The relationship between diagnostic key genes and immune cell infiltration may help to understand the development of NAFLD. The study was reviewed and approved by Ethics Committee of Tianjin Second People's Hospital in 2021 (ChiCTR1900024415).
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Affiliation(s)
- Jida Wang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Beitian Jia
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Jing Miao
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Dun Li
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Yin Wang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Lu Han
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Yin Yuan
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Yuan Zhang
- School of Public Health, Tianjin Medical University, Tianjin, 300070, China
| | - Yiyang Wang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Liying Guo
- Tianjin Second People's Hospital, Department of Integrated Traditional Chinese and Western Medicine, Tianjin, 300192, People's Republic of China
| | - Jianwei Jia
- Tianjin Second People's Hospital, Department of Integrated Traditional Chinese and Western Medicine, Tianjin, 300192, People's Republic of China
| | - Fang Zheng
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Sizhen Lai
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China
| | - Kaijun Niu
- Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China
| | - Weidong Li
- Department of General Surgery, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yuhong Bian
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China.
| | - Yaogang Wang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People's Republic of China.
- Public Health Science and Engineering College, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
- School of Public Health, Tianjin Medical University, Tianjin, 300070, China.
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82
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Xu Y, Liao W, Chen H, Pan M. Constructing diagnostic signature of serum microRNAs using machine learning for early pan-cancer detection. Discov Oncol 2024; 15:263. [PMID: 38965104 PMCID: PMC11224052 DOI: 10.1007/s12672-024-01139-1] [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: 12/20/2023] [Accepted: 07/01/2024] [Indexed: 07/06/2024] Open
Abstract
BACKGROUND Cancer is a major public health concern and the second leading cause of death worldwide. Various studies have reported the use of serum microRNAs (miRNAs) as non-invasive biomarkers for cancer detection. However, large-scale pan-cancer studies based on serum miRNAs have been relatively scarce. METHODS An optimized machine learning workflow, combining least absolute shrinkage and selection operator (LASSO) analyses, recursive feature elimination (RFE), and fourteen kinds of machine learning algorithms, was use to screen out candidate miRNAs from 2540 serum miRNAs and constructed a potent diagnostic signature (Cancer-related Serum miRNA Signatures) for pan-cancer detection, based on a serum miRNA expression dataset of 38,223 samples. RESULT Cancer-related Serum miRNA Signatures performed well in pan-cancer detection with an area under curve (AUC) of 0.999, 94.51% sensitivity, and 99.49% specificity in the external validation cohort, and represented an acceptable diagnostic performance for identifying early-stage tumors. Furthermore, the ability of multi-classification of tumors by serum miRNAs in pancreatic, colorectal, and biliary tract cancers was lower than that in other cancers, which showed accuracies of 59%, 58.5%, and 28.9%, respectively, indicating that the difference in serum miRNA expression profiles among a small number of tumor subtypes was not as significant as that between cancer samples and non-cancer controls. CONCLUSION We have developed a serum miRNA signature using machine learning that may be a cost-effective risk tool for pan-cancer detection. Our findings will benefit not only the predictive diagnosis of cancer but also a preventive and more personalized screening plan.
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Affiliation(s)
- Yuyan Xu
- General Surgery Center, Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Wei Liao
- Department of Hepatobiliary Surgery, The First People's Hospital of Foshan, Foshan, Guangdong, China
| | - Huanwei Chen
- Department of Hepatobiliary Surgery, The First People's Hospital of Foshan, Foshan, Guangdong, China.
| | - Mingxin Pan
- General Surgery Center, Department of Hepatobiliary Surgery II, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
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83
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Wang J, Li J. Artificial intelligence empowering public health education: prospects and challenges. Front Public Health 2024; 12:1389026. [PMID: 39022411 PMCID: PMC11252473 DOI: 10.3389/fpubh.2024.1389026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/24/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper's significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.
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Affiliation(s)
| | - Jianxiang Li
- School of Public Health, Suzhou Medical College of Soochow University, Suzhou, China
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84
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Fu X, Ma W, Zuo Q, Qi Y, Zhang S, Zhao Y. Application of machine learning for high-throughput tumor marker screening. Life Sci 2024; 348:122634. [PMID: 38685558 DOI: 10.1016/j.lfs.2024.122634] [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: 01/16/2024] [Revised: 03/26/2024] [Accepted: 04/10/2024] [Indexed: 05/02/2024]
Abstract
High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.
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Affiliation(s)
- Xingxing Fu
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Wanting Ma
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Qi Zuo
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
| | - Yanfei Qi
- Centenary Institute, The University of Sydney, Sydney, NSW 2050, Australia
| | - Shubiao Zhang
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China.
| | - Yinan Zhao
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian 116600, China
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85
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Sun H, Han X, Du Z, Chen G, Guo T, Xie F, Gu W, Shi Z. Machine learning for the identification of neoantigen-reactive CD8 + T cells in gastrointestinal cancer using single-cell sequencing. Br J Cancer 2024; 131:387-402. [PMID: 38849478 PMCID: PMC11263575 DOI: 10.1038/s41416-024-02737-0] [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: 11/13/2023] [Revised: 05/18/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND It appears that tumour-infiltrating neoantigen-reactive CD8 + T (Neo T) cells are the primary driver of immune responses to gastrointestinal cancer in patients. However, the conventional method is very time-consuming and complex for identifying Neo T cells and their corresponding T cell receptors (TCRs). METHODS By mapping neoantigen-reactive T cells from the single-cell transcriptomes of thousands of tumour-infiltrating lymphocytes, we developed a 26-gene machine learning model for the identification of neoantigen-reactive T cells. RESULTS In both training and validation sets, the model performed admirably. We discovered that the majority of Neo T cells exhibited notable differences in the biological processes of amide-related signal pathways. The analysis of potential cell-to-cell interactions, in conjunction with spatial transcriptomic and multiplex immunohistochemistry data, has revealed that Neo T cells possess potent signalling molecules, including LTA, which can potentially engage with tumour cells within the tumour microenvironment, thereby exerting anti-tumour effects. By sequencing CD8 + T cells in tumour samples of patients undergoing neoadjuvant immunotherapy, we determined that the fraction of Neo T cells was significantly and positively linked with the clinical benefit and overall survival rate of patients. CONCLUSION This method expedites the identification of neoantigen-reactive TCRs and the engineering of neoantigen-reactive T cells for therapy.
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Affiliation(s)
- Hongwei Sun
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiao Han
- KangChen Bio-tech., Ltd, ShangHai, China
| | - Zhengliang Du
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Geer Chen
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tonglei Guo
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
| | - Fei Xie
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
| | - Weiyue Gu
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China
- Chineo Medical Technology Co., Ltd, Beijing, 100101, China
| | - Zhiwen Shi
- Key Laboratory of Laboratory Medicine, Ministry of Education, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Data and Analysis Center for Genetic Diseases, Beijing Chigene Translational Medicine Research Center Co, Ltd, Tongzhou District, Beijing, China.
- Chineo Medical Technology Co., Ltd, Beijing, 100101, China.
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Yan L, Chen C, Wang L, Hong H, Wu C, Huang J, Jiang J, Chen J, Xu G, Cui Z. Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms. Exp Ther Med 2024; 28:292. [PMID: 38827468 PMCID: PMC11140288 DOI: 10.3892/etm.2024.12581] [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: 11/01/2023] [Accepted: 04/17/2024] [Indexed: 06/04/2024] Open
Abstract
Spinal cord injury (SCI) is a severe neurological complication following spinal fracture, which has long posed a challenge for clinicians. Microglia play a dual role in the pathophysiological process after SCI, both beneficial and detrimental. The underlying mechanisms of microglial actions following SCI require further exploration. The present study combined three different machine learning algorithms, namely weighted gene co-expression network analysis, random forest analysis and least absolute shrinkage and selection operator analysis, to screen for differentially expressed genes in the GSE96055 microglia dataset after SCI. It then used protein-protein interaction networks and gene set enrichment analysis with single genes to investigate the key genes and signaling pathways involved in microglial function following SCI. The results indicated that microglia not only participate in neuroinflammation but also serve a significant role in the clearance mechanism of apoptotic cells following SCI. Notably, bioinformatics analysis and lipopolysaccharide + UNC569 (a MerTK-specific inhibitor) stimulation of BV2 cell experiments showed that the expression levels of Anxa2, Myo1e and Spp1 in microglia were significantly upregulated following SCI, thus potentially involved in regulating the clearance mechanism of apoptotic cells. The present study suggested that Anxa2, Myo1e and Spp1 may serve as potential targets for the future treatment of SCI and provided a theoretical basis for the development of new methods and drugs for treating SCI.
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Affiliation(s)
- Lei Yan
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Chu Chen
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Lingling Wang
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Hongxiang Hong
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Chunshuai Wu
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Jiayi Huang
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Jiawei Jiang
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Jiajia Chen
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Guanhua Xu
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
| | - Zhiming Cui
- The First People's Hospital of Nantong, The Second Affiliated Hospital of Nantong University, Research Institute for Spine and Spinal Cord Disease of Nantong University, Nantong, Jiangsu 226019, P.R. China
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Tang BH, Li QY, Liu HX, Zheng Y, Wu YE, van den Anker J, Hao GX, Zhao W. Machine Learning: A Potential Therapeutic Tool to Facilitate Neonatal Therapeutic Decision Making. Paediatr Drugs 2024; 26:355-363. [PMID: 38880837 DOI: 10.1007/s40272-024-00638-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/19/2024] [Indexed: 06/18/2024]
Abstract
Bacterial infection is one of the major causes of neonatal morbidity and mortality worldwide. Finding rapid and reliable methods for early recognition and diagnosis of bacterial infections and early individualization of antibacterial drug administration are essential to eradicate these infections and prevent serious complications. However, this is often difficult to perform due to non-specific clinical presentations, low accuracy of current diagnostic methods, and limited knowledge of neonatal pharmacokinetics. Although neonatal medicine has been relatively late to embrace the benefits of machine learning (ML), there have been some initial applications of ML for the early prediction of neonatal sepsis and individualization of antibiotics. This article provides a brief introduction to ML and discusses the current state of the art in diagnosing and treating neonatal bacterial infections, gaps, potential uses of ML, and future directions to address the limitations of current studies. Neonatal bacterial infections involve a combination of physiologic development, disease expression, and treatment response outcomes. To address this complex relationship, future models could consider appropriate ML algorithms to capture time series features while integrating influences from the host, microbes, and drugs to optimize antimicrobial drug use in neonates. All models require prospective clinical trials to validate their clinical utility before clinical use.
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Affiliation(s)
- Bo-Hao Tang
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Qiu-Yue Li
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Hui-Xin Liu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA
- Department of Pediatrics, Pharmacology and Physiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Departments of Genomics and Precision Medicine, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
| | - Wei Zhao
- Department of Pharmacy, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
- Department of Clinical Pharmacy, Institute of Clinical Pharmacology, Key Laboratory of Chemical Biology (Ministry of Education), NMPA Key Laboratory for Clinical Research and Evaluation of Innovative Drug, School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, China.
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Zou X, Cui N, Ma Q, Lin Z, Zhang J, Li X. Development of a machine learning model for predicting pneumothorax risk in coaxial core needle biopsy (≤3 cm). Eur J Radiol 2024; 176:111508. [PMID: 38759543 DOI: 10.1016/j.ejrad.2024.111508] [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: 12/22/2023] [Revised: 03/31/2024] [Accepted: 05/13/2024] [Indexed: 05/19/2024]
Abstract
PURPOSE The aim is to devise a machine learning algorithm exploiting preoperative clinical data to forecast the hazard of pneumothorax post-coaxial needle lung biopsy (CCNB), thereby informing clinical decision-making and enhancing perioperative care. METHOD This retrospective analysis aggregated clinical and imaging data from patients with lung nodules (≤3 cm) biopsies. Variable selection was done using univariate analysis and LASSO regression, with the dataset subsequently divided into training (80 %) and validation (20 %) subsets. Various machine learning (ML) classifiers were employed in a consolidated approach to ascertain the paramount model, which was followed by individualized risk profiling showcased through Shapley Additive eXplanations (SHAP). RESULTS Out of the 325 patients included in the study, 19.6% (64/325) experienced postoperative pneumothorax. High-risk factors determined were Cancer, Lesion_type, GOLD, Size, and Depth. The Gaussian Naive Bayes (GNB) classifier demonstrated superior prediction with an Area Under the Curve (AUC) of 0.82 (95% CI 0.71-0.94), complemented by an accuracy rate of 0.8, sensitivity of 0.71, specificity of 0.84, and an F1 score of 0.61 in the test cohort. CONCLUSION The formulated prognostic algorithm exhibited commendable efficacy in preoperatively prognosticating CCNB-induced pneumothorax, harboring the potential to refine personalized risk appraisals, steer clinical judgment, and ameliorate perioperative patient stewardship.
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Affiliation(s)
- Xugong Zou
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Ning Cui
- Medical Imaging Center, Taihe Hospital, Shiyan City, Hubei Province, China
| | - Qiang Ma
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Zhipeng Lin
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Jian Zhang
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China
| | - Xiaoqun Li
- Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China.
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89
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Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [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: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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90
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Gümüş AB, Açık M, Durmaz SE. Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study. Prev Nutr Food Sci 2024; 29:199-209. [PMID: 38974584 PMCID: PMC11223921 DOI: 10.3746/pnf.2024.29.2.199] [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: 02/14/2024] [Revised: 03/25/2024] [Accepted: 03/28/2024] [Indexed: 07/09/2024] Open
Abstract
This study aimed to compare the nutritional quality of beverages sold in Türkiye according to their labeling profiles. A total of 304 nonalcoholic beverages sold in supermarkets and online markets with the highest market capacity in Türkiye were included. Milk and dairy products, sports drinks, and beverages for children were excluded. The health star rating (HSR) was used to assess the nutritional quality of beverages. The nutritional quality of beverages was evaluated using a decision tree model according to the HSR score based on the variables presented on the beverage label. Moreover, confusion matrix tests were used to test the model's accuracy. The mean HSR score of beverages was 2.6±1.9, of which 30.2% were in the healthy category (HSR≥3.5). Fermented and 100% fruit juice beverages had the highest mean HSR scores. According to the decision tree model of the training set, the predictors of HSR quality score, in order of importance, were as follows: added sugar (46%), sweetener (28%), additives (19%), fructose-glucose syrup (4%), and caffeine (3%). In the test set, the accuracy rate and F1 score were 0.90 and 0.82, respectively, suggesting that the prediction performance of our model had the perfect fit. According to the HSR classification, most beverages were found to be unhealthy. Thus, they increase the risk of the development of obesity and other diseases because of their easy consumption. The decision tree learning algorithm could guide the population to choose healthy beverages based on their labeling information.
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Affiliation(s)
- Aylin Bayındır Gümüş
- First and Emergency Aid Program, Vocational School of Health Services, Kırıkkale University, Kırıkkale 71450, Türkiye
| | - Murat Açık
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Fırat University, Elazığ 23200, Türkiye
| | - Sevinç Eşer Durmaz
- Department of Nutrition and Dietetics, Faculty of Health Sciences, Kırıkkale University, Kırıkkale 71450, Türkiye
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91
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Holliday EG, Zhang B. Machine learning-enabled colorimetric sensors for foodborne pathogen detection. ADVANCES IN FOOD AND NUTRITION RESEARCH 2024; 111:179-213. [PMID: 39103213 DOI: 10.1016/bs.afnr.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/07/2024]
Abstract
In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.
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Affiliation(s)
- Emma G Holliday
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, United States
| | - Boce Zhang
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, United States.
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92
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Fan L, Shen Y, Lou D, Gu N. Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research. Adv Healthc Mater 2024:e2401576. [PMID: 38936401 DOI: 10.1002/adhm.202401576] [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: 04/29/2024] [Revised: 06/08/2024] [Indexed: 06/29/2024]
Abstract
Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.
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Affiliation(s)
- Lin Fan
- Medical School of Nanjing University, Nanjing, 210093, P. R. China
- School of Integrated Circuit Science and Engineering (Industry-Education Integration School), Nanjing University of Posts and Telecommunications, Nanjing, 210023, P. R. China
| | - Yilei Shen
- School of Integrated Circuit Science and Engineering (Industry-Education Integration School), Nanjing University of Posts and Telecommunications, Nanjing, 210023, P. R. China
| | - Doudou Lou
- Nanjing Institute for Food and Drug Control, Nanjing, 211198, P. R. China
| | - Ning Gu
- Medical School of Nanjing University, Nanjing, 210093, P. R. China
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93
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Han T, Bai Y, Liu Y, Dong Y, Liang C, Gao L, Zhou J, Guo J, Wu J, Hu D. Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma. Funct Integr Genomics 2024; 24:118. [PMID: 38935217 DOI: 10.1007/s10142-024-01388-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: 12/23/2023] [Revised: 04/01/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD.
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Affiliation(s)
- Tao Han
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Ying Bai
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China.
| | - Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Yunjia Dong
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Chao Liang
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Lu Gao
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institute, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui, China.
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Huainan, Anhui, China.
- Anhui Occupational Health and Safety Engineering Laboratory, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institute, Huainan, Anhui, China.
- Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, Anhui, China.
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94
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Xu L, Xiao T, Zou B, Rong Z, Yao W. Identification of diagnostic biomarkers and potential therapeutic targets for biliary atresia via WGCNA and machine learning methods. Front Pediatr 2024; 12:1339925. [PMID: 38989272 PMCID: PMC11233743 DOI: 10.3389/fped.2024.1339925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Biliary atresia (BA) is a severe and progressive biliary obstructive disease in infants that requires early diagnosis and new therapeutic targets. This study employed bioinformatics methods to identify diagnostic biomarkers and potential therapeutic targets for BA. Our analysis of mRNA expression from Gene Expression Omnibus datasets revealed 3,273 differentially expressed genes between patients with BA and those without BA (nBA). Weighted gene coexpression network analysis determined that the turquoise gene coexpression module, consisting of 298 genes, is predominantly associated with BA. The machine learning method then filtered out the top 2 important genes, CXCL8 and TMSB10, from the turquoise module. The area under receiver operating characteristic curves for TMSB10 and CXCL8 were 0.961 and 0.927 in the training group and 0.819 and 0.791 in the testing group, which indicated a high diagnostic value. Besides, combining TMSB10 and CXCL8, a nomogram with better diagnostic performance was built for clinical translation. Several studies have highlighted the potential of CXCL8 as a therapeutic target for BA, while TMSB10 has been shown to regulate cell polarity, which was related to BA progression. Our analysis with qRT PCR and immunohistochemistry also confirmed the upregulation of TMSB10 at mRNA and protein levels in BA liver samples. These findings highlight the sensitivity of CXCL8 and TMSB10 as diagnostic biomarkers and their potential as therapeutic targets for BA.
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Affiliation(s)
- Lei Xu
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ting Xiao
- Department of Ultrasonography, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Biao Zou
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Zhihui Rong
- Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wei Yao
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
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95
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Wali R, Xu H, Cheruiyot C, Saleem HN, Janshoff A, Habeck M, Ebert A. Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy. Biol Chem 2024; 405:427-439. [PMID: 38651266 DOI: 10.1515/hsz-2024-0023] [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: 02/07/2024] [Accepted: 03/27/2024] [Indexed: 04/25/2024]
Abstract
Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92 ± 0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96 ± 0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs.
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Affiliation(s)
- Ruheen Wali
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Hang Xu
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Cleophas Cheruiyot
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Hafiza Nosheen Saleem
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
| | - Andreas Janshoff
- Institute for Physical Chemistry, Göttingen University, Tammannstraße 6, D-37077 Göttingen, Germany
| | - Michael Habeck
- Microscopic Image Analysis, 39065 Jena University Hospital , Kollegiengasse 10, D-07743 Jena, Germany
| | - Antje Ebert
- Department of Cardiology and Pneumology, Heart Research Center, University Medical Center, 27177 Göttingen University , Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
- Partner Site Göttingen, DZHK (German Center for Cardiovascular Research), Robert-Koch-Strasse 40, D-37075 Göttingen, Germany
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96
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Sun M, Sun J, Li M. Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis. Sci Rep 2024; 14:14490. [PMID: 38914641 PMCID: PMC11196279 DOI: 10.1038/s41598-024-65367-9] [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: 02/21/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024] Open
Abstract
Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767-0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.
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Affiliation(s)
- Meng Sun
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China
| | - Jikui Sun
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China.
| | - Meng Li
- Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, 250014, Shandong, China.
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97
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He Y, Huang R, Zhang R, He F, Han L, Han W. PredCoffee: A binary classification approach specifically for coffee odor. iScience 2024; 27:110041. [PMID: 38868178 PMCID: PMC11167484 DOI: 10.1016/j.isci.2024.110041] [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: 01/24/2024] [Revised: 04/26/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee-odor molecules and 9,700 non-coffee-odor molecules were collected. The Knowledge-guided Pre-training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi-layer perceptron (MLP), and message-passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.
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Affiliation(s)
- Yi He
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ruirui Huang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Ruoyu Zhang
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Fei He
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Lu Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
| | - Weiwei Han
- Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Sciences, Jilin University, 2699 Qianjin Street, Changchun 130012, China
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98
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Yurchenko A, Özkul G, van Riel NAW, van Hest JCM, de Greef TFA. Mechanism-based and data-driven modeling in cell-free synthetic biology. Chem Commun (Camb) 2024; 60:6466-6475. [PMID: 38847387 DOI: 10.1039/d4cc01289e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Cell-free systems have emerged as a versatile platform in synthetic biology, finding applications in various areas such as prototyping synthetic circuits, biosensor development, and biomanufacturing. To streamline the prototyping process, cell-free systems often incorporate a modeling step that predicts the outcomes of various experimental scenarios, providing a deeper insight into the underlying mechanisms and functions. There are two recognized approaches for modeling these systems: mechanism-based modeling, which models the underlying reaction mechanisms; and data-driven modeling, which makes predictions based on data without preconceived interactions between system components. In this highlight, we focus on the latest advancements in both modeling approaches for cell-free systems, exploring their potential for the design and optimization of synthetic genetic circuits.
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Affiliation(s)
- Angelina Yurchenko
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Gökçe Özkul
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Natal A W van Riel
- Computational Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Eindhoven MedTech Innovation Center, 5612 AX Eindhoven, The Netherlands
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jan C M van Hest
- Bio-Organic Chemistry, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
- Biomedical Engineering, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven 5600 MB, The Netherlands
| | - Tom F A de Greef
- Laboratory of Chemical Biology, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands.
- Institute for Complex Molecular Systems Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Synthetic Biology Group, Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Institute for Molecules and Materials, Radboud University, 6525 AJ Nijmegen, The Netherlands
- Center for Living Technologies, Eindhoven-Wageningen-Utrecht Alliance, 3584 CB Utrecht, The Netherlands
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Ye C, Zhu S, Yuan J, Yuan X. FPR1, as a Potential Biomarker of Diagnosis and Infliximab Therapy Responses for Crohn's Disease, is Related to Disease Activity, Inflammation and Macrophage Polarization. J Inflamm Res 2024; 17:3949-3966. [PMID: 38911989 PMCID: PMC11193993 DOI: 10.2147/jir.s459819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 06/12/2024] [Indexed: 06/25/2024] Open
Abstract
Purpose Crohn's disease (CD) represents a multifaceted inflammatory gastrointestinal condition, with a profound significance placed on unraveling its molecular pathways to enhance both diagnostic capabilities and therapeutic interventions. This study focused on identifying a robust macrophage-related signatures (MacroSig) for diagnosing CD, emphasizing the role of FPR1 in macrophage polarization and its implications in CD. Patients and Methods Expression profiles from intestinal biopsies and macrophages of 1804 CD patients were retrieved from the Gene Expression Omnibus (GEO). Utilizing CIBERSORTx, differential expression analysis, and weighted correlation network analysis to to identify macrophage-related genes (MRGs). By unsupervised clustering, distinct clusters of CD were identified. Potential biomarkers were identified via using four machine learning algorithms, leading to the establishment of MacroSig which combines insights from 12 machine learning algorithms. Furthermore, the expression of FPR1 was verified in intestinal biopsies of CD patients and two murine experimental colitis models. Finally, we further explored the role of FPR1 in macrophage polarization through single-cell analysis as well as through the study of RAW264.7 cells and peritoneal macrophages. Results Two distinct clusters with differential levels of macrophage infiltration and inflammation were identified. The MacroSig, which included FPR1 and LILRB2, exhibited high diagnostic accuracy and outperformed existing biomarkers and signatures. Clinical analysis demonstrated a strong correlation of FPR1 with disease activity, endoscopic inflammation status, and response to infliximab treatment. The expression levels of FPR1 were validated in our CD cohort by immunohistochemistry and confirmed in two colitis mouse models. Single-cell analysis indicated that FPR1 is predominantly expressed in macrophages and monocytes. In vitro studies demonstrated that FPR1 was upregulated in M1 macrophages, and its activation promoted M1 polarization. Conclusion We developed a promising diagnostic signature for CD, and targeting FPR1 to modulate macrophage polarization may represent a novel therapeutic strategy.
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Affiliation(s)
- Chenglin Ye
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Sizhe Zhu
- Department of Otolaryngology-Head and Neck Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, Hubei, People’s Republic of China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China
| | - Xiuxue Yuan
- Medical College of Wuhan University of Science and Technology, Wuhan, Hubei, People’s Republic of China
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100
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Zheng Y, Zhang C, Sun X, Kang K, Luo R, Zhao A, Wu Y. Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning. Clin Exp Med 2024; 24:132. [PMID: 38890203 PMCID: PMC11189314 DOI: 10.1007/s10238-024-01402-3] [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: 04/24/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,871 pediatric and 226,211 adult patients, identified key risk factors for pediatric lymphoma survival, developed a predictive nomogram, and utilized machine learning to predict long-term lymphoma-specific mortality risk. Between 1975 and 2018, we observed substantial increases in 1-year (19.3%), 5-year (41.9%), and 10-year (48.8%) overall survival rates in pediatric patients with lymphoma. Prognostic factors such as age, sex, race, Ann Arbor stage, lymphoma subtypes, and radiotherapy were incorporated into the nomogram. The nomogram exhibited excellent predictive performance with area under the curve (AUC) values of 0.766, 0.724, and 0.703 for one-year, five-year, and ten-year survival, respectively, in the training cohort, and AUC values of 0.776, 0.712, and 0.696 in the validation cohort. Importantly, the nomogram outperformed the Ann Arbor staging system in survival prediction. Machine learning models achieved AUC values of approximately 0.75, surpassing the conventional method (AUC = ~ 0.70) in predicting the risk of lymphoma-specific death. We also observed that pediatric lymphoma survivors had a substantially reduced risk of lymphoma after ten years b,ut faced an increasing risk of non-lymphoma diseases. The study highlights substantial improvements in pediatric lymphoma survival, offers reliable predictive tools, and underscores the importance of long-term monitoring for non-lymphoma health issues in pediatric patients.
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Grants
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 82303773, No. 82303772, No. 82204490, No. 82303694 National Natural Science Foundation of China
- No. 2023NSFSC1885 Natural Science Foundation of Sichuan Province
- No. 2023NSFSC1885 Natural Science Foundation of Sichuan Province
- No. 2023YFS0306 Key Research, Development Program of Sichuan Province
- No. 2023YFS0306 Key Research, Development Program of Sichuan Province
- No. GZB20230481 Postdoctoral Fellowship Program of CPSF
- No. 2024HXBH149, No. 2024HXBH006 Post-Doctor Research Project, West China Hospital, Sichuan University
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Affiliation(s)
- Yue Zheng
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Chunlan Zhang
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
| | - Xu Sun
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China
| | - Kai Kang
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ren Luo
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ailin Zhao
- Department of Hematology, West China Hospital, Sichuan University, Chengdu, China.
| | - Yijun Wu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
- Laboratory of Clinical Cell Therapy, West China Hospital, Sichuan University, Chengdu, China.
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