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Xie RC, Wang YT, Lin XF, Lin XM, Hong XY, Zheng HJ, Zhang LF, Huang T, Ma JF. Development and validation of a clinical prediction model for early ventilator weaning in post-cardiac surgery. Heliyon 2024; 10:e28141. [PMID: 38560197 PMCID: PMC10979061 DOI: 10.1016/j.heliyon.2024.e28141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/26/2024] [Accepted: 03/12/2024] [Indexed: 04/04/2024] Open
Abstract
Background Weaning patients from mechanical ventilation is a critical clinical challenge post cardiac surgery. The effective liberation of patients from the ventilator significantly improves their recovery and survival rates. This study aimed to develop and validate a clinical prediction model to evaluate the likelihood of successful extubation in post-cardiac surgery patients. Method A predictive nomogram was constructed for extubation success in individual patients, and receiver operating characteristic (ROC) and calibration curves were generated to assess its predictive capability. The superior performance of the model was confirmed using Delong's test in the ROC analysis. A decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram. Results Among 270 adults included in our study, 107 (28.84%) experienced delayed extubation. A predictive nomogram system was derived based on five identified risk factors, including the proportion of male patients, EuroSCORE II, operation time, pump time, bleeding during operation, and brain natriuretic peptide (BNP) level. Based on the predictive system, five independent predictors were used to construct a full nomogram. The area under the curve values of the nomogram were 0.880 and 0.753 for the training and validation cohorts, respectively. The DCA and clinical impact curves showed good clinical utility of this model. Conclusion Delayed extubation and weaning failure, common and potentially hazardous complications following cardiac surgery, vary in timing based on factors such as sex, EuroSCORE II, pump duration, bleeding, and postoperative BNP reduction. The nomogram developed and validated in this study can accurately predict when extubation should occur in these patients. This tool is vital for assessing risks on an individual basis and making well-informed clinical decisions.
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Affiliation(s)
- Rong-Cheng Xie
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Yu-Ting Wang
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Xue-Feng Lin
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Xiao-Ming Lin
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Xiang-Yu Hong
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Hong-Jun Zheng
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Lian-Fang Zhang
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Ting Huang
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
| | - Jie-Fei Ma
- Department of Critical Care Medicine, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 361015, Fujian province, PR China
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 310000, PR China
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Stivi T, Padawer D, Dirini N, Nachshon A, Batzofin BM, Ledot S. Using Artificial Intelligence to Predict Mechanical Ventilation Weaning Success in Patients with Respiratory Failure, Including Those with Acute Respiratory Distress Syndrome. J Clin Med 2024; 13:1505. [PMID: 38592696 PMCID: PMC10934889 DOI: 10.3390/jcm13051505] [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: 02/01/2024] [Revised: 02/29/2024] [Accepted: 03/03/2024] [Indexed: 04/10/2024] Open
Abstract
The management of mechanical ventilation (MV) remains a challenge in intensive care units (ICUs). The digitalization of healthcare and the implementation of artificial intelligence (AI) and machine learning (ML) has significantly influenced medical decision-making capabilities, potentially enhancing patient outcomes. Acute respiratory distress syndrome, an overwhelming inflammatory lung disease, is common in ICUs. Most patients require MV. Prolonged MV is associated with an increased length of stay, morbidity, and mortality. Shortening the MV duration has both clinical and economic benefits and emphasizes the need for better MV weaning management. AI and ML models can assist the physician in weaning patients from MV by providing predictive tools based on big data. Many ML models have been developed in recent years, dealing with this unmet need. Such models provide an important prediction regarding the success of the individual patient's MV weaning. Some AI models have shown a notable impact on clinical outcomes. However, there are challenges in integrating AI models into clinical practice due to the unfamiliar nature of AI for many physicians and the complexity of some AI models. Our review explores the evolution of weaning methods up to and including AI and ML as weaning aids.
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Affiliation(s)
- Tamar Stivi
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Dan Padawer
- Department of Pulmonary Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel;
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
| | - Noor Dirini
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Akiva Nachshon
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Baruch M. Batzofin
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
| | - Stephane Ledot
- Department of Anesthesia, Critical Care and Pain Medicine, Hadassah Medical Center, Ein Kerem, POB 12000, Jerusalem 9112001, Israel; (N.D.); (A.N.); (B.M.B.); (S.L.)
- Faculty of Medicine, Hebrew University of Jerusalem, Campus Ein Kerem, Jerusalem 9112102, Israel
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Qiu X, Tan X, Wang C, Chen S, Du B, Huang J. A long short-temory relation network for real-time prediction of patient-specific ventilator parameters. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14756-14776. [PMID: 37679157 DOI: 10.3934/mbe.2023660] [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: 09/09/2023]
Abstract
Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment.
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Affiliation(s)
- Xihe Qiu
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Xiaoyu Tan
- INF Technology (Shanghai) Company Limited, Shanghai 201203, China
| | - Chenghao Wang
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Shaotao Chen
- School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
| | - Bin Du
- Yanshan Electronics of Beijing, Beijing 100192, China
| | - Jingjing Huang
- ENT institute and Department of Otorhinolaryngology, Fudan University, Shanghai 200031, China
- Shanghai Municipal Key Clinical Specialty, Shanghai 200031, China
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Zhou Y, Hu Z, Sun Q, Dong Y. 5-methyladenosine regulators play a crucial role in development of chronic hypersensitivity pneumonitis and idiopathic pulmonary fibrosis. Sci Rep 2023; 13:5941. [PMID: 37045913 PMCID: PMC10097674 DOI: 10.1038/s41598-023-32452-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/28/2023] [Indexed: 04/14/2023] Open
Abstract
5-methyladenosine (m5C) modification regulates gene expression and biological functions in oncologic areas. However, the effect of m5C modification in chronic hypersensitivity pneumonitis (CHP) and idiopathic pulmonary fibrosis (IPF) remains unknown. Expression data for 12 significant m5C regulators were obtained from the interstitial lung disease dataset. Five candidate m5C regulators, namely tet methylcytosine dioxygenase 2, NOP2/Sun RNA methyltransferase 5, Y-box binding protein 1, tRNA aspartic acid methyltransferase 1, and NOP2/Sun RNA methyltransferase 3 were screened using random forest and nomogram models to predict risks of pulmonary fibrosis. Next, we applied the consensus clustering method to stratify the samples with different m5C patterns into two groups (cluster A and B). Finally, we calculated immune cell infiltration scores via single-sample gene set enrichment analysis, then compared immune cell infiltration, related functions as well as the expression of programmed cell death 1 (PD-1, PDCD1) and programmed death protein ligand-1 (PD-L1, CD274) between the two clusters. Principal component analysis of m5C-related scores across the 288 samples revealed that cluster A had higher immune-related expression than B. Notably, T helper cell (Th) 2 type cytokines and Th1 signatures were more abundant in clusters A and B, respectively. Our results suggest that m5C is associated with and plays a crucial role in development of pulmonary fibrosis. These m5C patterns could be potential biomarkers for identification of CHP and IPF, and guide future development of immunotherapy or other new drugs strategies for pulmonary fibrosis.
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Affiliation(s)
- Yiyi Zhou
- Department of Respiratory and Critical Care Medicine, Changhai Hospital, Shanghai, China
| | - Zhenli Hu
- Department of Respiratory and Critical Care Medicine, Changhai Hospital, Shanghai, China
| | - Qinying Sun
- Department of Respiratory and Critical Care Medicine, Changhai Hospital, Shanghai, China
| | - Yuchao Dong
- Department of Respiratory and Critical Care Medicine, Changhai Hospital, Shanghai, China.
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Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJV, Dewitz B, Bibo R, Sabouniaghdam P, Tootooni MS, Bundschuh RA, Lichtenberg A, Aubin H, Schmid F. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Front Med (Lausanne) 2023; 10:1109411. [PMID: 37064042 PMCID: PMC10102653 DOI: 10.3389/fmed.2023.1109411] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 03/10/2023] [Indexed: 04/03/2023] Open
Abstract
Background Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare.
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Affiliation(s)
- Sobhan Moazemi
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Sahar Vahdati
- Institute for Applied Informatics (InfAI), Dresden, Germany
| | - Jason Li
- Institute for Applied Informatics (InfAI), Dresden, Germany
| | - Sebastian Kalkhoff
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Luis J. V. Castano
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Bastian Dewitz
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Roman Bibo
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | | | - Mohammad S. Tootooni
- Department of Health Informatics and Data Science, Loyola University Chicago, Chicago, IL, United States
| | - Ralph A. Bundschuh
- Nuclear Medicine, Medical Faculty, University Augsburg, Augsburg, Germany
| | - Artur Lichtenberg
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Hug Aubin
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
| | - Falko Schmid
- Digital Health Lab Düsseldorf, Department of Cardiovascular Surgery, Medical Faculty and University Hospital Düsseldorf, Düsseldorf, Germany
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Liu CF, Hung CM, Ko SC, Cheng KC, Chao CM, Sung MI, Hsing SC, Wang JJ, Chen CJ, Lai CC, Chen CM, Chiu CC. An artificial intelligence system to predict the optimal timing for mechanical ventilation weaning for intensive care unit patients: A two-stage prediction approach. Front Med (Lausanne) 2022; 9:935366. [PMID: 36465940 PMCID: PMC9715756 DOI: 10.3389/fmed.2022.935366] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/11/2022] [Indexed: 11/03/2023] Open
Abstract
Background For the intensivists, accurate assessment of the ideal timing for successful weaning from the mechanical ventilation (MV) in the intensive care unit (ICU) is very challenging. Purpose Using artificial intelligence (AI) approach to build two-stage predictive models, namely, the try-weaning stage and weaning MV stage to determine the optimal timing of weaning from MV for ICU intubated patients, and implement into practice for assisting clinical decision making. Methods AI and machine learning (ML) technologies were used to establish the predictive models in the stages. Each stage comprised 11 prediction time points with 11 prediction models. Twenty-five features were used for the first-stage models while 20 features were used for the second-stage models. The optimal models for each time point were selected for further practical implementation in a digital dashboard style. Seven machine learning algorithms including Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), K Nearest Neighbor (KNN), lightGBM, XGBoost, and Multilayer Perception (MLP) were used. The electronic medical records of the intubated ICU patients of Chi Mei Medical Center (CMMC) from 2016 to 2019 were included for modeling. Models with the highest area under the receiver operating characteristic curve (AUC) were regarded as optimal models and used to develop the prediction system accordingly. Results A total of 5,873 cases were included in machine learning modeling for Stage 1 with the AUCs of optimal models ranging from 0.843 to 0.953. Further, 4,172 cases were included for Stage 2 with the AUCs of optimal models ranging from 0.889 to 0.944. A prediction system (dashboard) with the optimal models of the two stages was developed and deployed in the ICU setting. Respiratory care members expressed high recognition of the AI dashboard assisting ventilator weaning decisions. Also, the impact analysis of with- and without-AI assistance revealed that our AI models could shorten the patients' intubation time by 21 hours, besides gaining the benefit of substantial consistency between these two decision-making strategies. Conclusion We noticed that the two-stage AI prediction models could effectively and precisely predict the optimal timing to wean intubated patients in the ICU from ventilator use. This could reduce patient discomfort, improve medical quality, and lower medical costs. This AI-assisted prediction system is beneficial for clinicians to cope with a high demand for ventilators during the COVID-19 pandemic.
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Affiliation(s)
- Chung-Feng Liu
- Department of Medical Research, Chi Mei Medical Center, Tainan, Taiwan
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Shian-Chin Ko
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Kuo-Chen Cheng
- Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chien-Ming Chao
- Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying, Taiwan
- Department of Dental Laboratory Technology, Min-Hwei College of Health Care Management, Liouying, Taiwan
| | - Mei-I Sung
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Shu-Chen Hsing
- Department of Respiratory Therapy, Chi Mei Medical Center, Tainan, Taiwan
| | - Jhi-Joung Wang
- Department of Anesthesiology, Chi Mei Medical Center, Tainan, Taiwan
- Department of Anesthesiology, National Defense Medical Center, Taipei, Taiwan
| | - Chia-Jung Chen
- Department of Information Systems, Chi Mei Medical Center, Tainan, Taiwan
| | - Chih-Cheng Lai
- Division of Hospital Medicine, Department of Internal Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chin-Ming Chen
- Department of Intensive Care Medicine, Chi Mei Medical Center, Tainan, Taiwan
| | - Chong-Chi Chiu
- Department of General Surgery, E-Da Cancer Hospital, Kaohsiung, Taiwan
- School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan
- Department of Medical Education and Research, E-Da Cancer Hospital, Kaohsiung, Taiwan
- Department of General Surgery, Chi Mei Medical Center, Tainan, Taiwan
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Zhou Y, Fang C, Sun Q, Dong Y. Relevance of RNA N6-Methyladenosine Regulators for Pulmonary Fibrosis: Implications for Chronic Hypersensitivity Pneumonitis and Idiopathic Pulmonary Fibrosis. Front Genet 2022; 13:939175. [PMID: 35910226 PMCID: PMC9329921 DOI: 10.3389/fgene.2022.939175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/16/2022] [Indexed: 12/15/2022] Open
Abstract
N6-methyladenosine (m6A) modification plays a pivotal role in post-transcriptionally regulating gene expression and biological functions. Nonetheless, the roles of m6A modification in the regulation of chronic hypersensitivity pneumonitis (CHP) and idiopathic pulmonary fibrosis (IPF) remain unclear. Twenty-two significant m6A regulators were selected from differential gene analysis between the control and treatment groups from the GSE150910 dataset. Five candidate m6A regulators (insulin-like growth factor binding protein 2, insulin-like growth factor binding protein 3, YTH domain-containing protein 1, zinc finger CCCH domain-containing protein 13, and methyltransferase-like 3) were screened by the application of a random forest model and nomogram model to predict risks of pulmonary fibrosis. The consensus clustering method was applied to divide the treatment samples into two groups with different m6A patterns (clusters A and B) based on the 22 m6A regulators. Our study performed principal component analysis to obtain the m6A-related score of the 288 samples to quantify the two m6A patterns. The study reveals that cluster A was linked to T helper cell (Th) 2-type cytokines, while the immune infiltration of Th1 cytokines was higher in cluster B. Our results suggest that m6A cluster A is likely related to pulmonary fibrosis, indicating m6A regulators play notable roles in the occurrence of pulmonary fibrosis. The m6A patterns could be considered as biomarkers to identify CHP and IPF, which will be helpful to develop immunotherapy strategies for pulmonary fibrosis in the future.
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Affiliation(s)
| | | | - Qinying Sun
- *Correspondence: Yuchao Dong, ; Qinying Sun,
| | - Yuchao Dong
- *Correspondence: Yuchao Dong, ; Qinying Sun,
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