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Hoang ATP, Do MC, Kim KW. Environmental risk assessment of selected pharmaceuticals in hospital wastewater in nothern Vietnam. CHEMOSPHERE 2024; 356:141973. [PMID: 38608777 DOI: 10.1016/j.chemosphere.2024.141973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 04/06/2024] [Accepted: 04/09/2024] [Indexed: 04/14/2024]
Abstract
Pharmaceuticals are progressively employed in both human and veterinary medicine and increasingly recognized as environmental contaminants. This study investigated the occurrence of selected pharmaceuticals in influent and effluent of wastewater treatment plants of 12 hospitals in Hanoi and 3 northern cities of Vietnam during dry and rainy seasons. In addition, environmental risk of pharmaceuticals in both hospital influents and effluents were evaluated based on risk quotients (RQs). Nine selected pharmaceutical compounds including sulfamethoxazole (SMX), naproxen (NPX), diclofenac (DCF), ibuprofen (IBU), acetaminophen (ACT), carbamazepine (CBM), iopromide (IOP), atenolol (ATN), and caffeine (CAF) were frequently detected in most influent and effluent wastewaters of 12 investigated hospitals. Detected compound levels exhibited a wide range, from as low as 1 ng/L for DCF to as high as 61,772 ng/L for ACT. Among these compounds, ACT, CAF, SMX, and IOP were consistently detected at substantial concentrations in both influents and effluents. This investigation also highlighted potential risks posed by SMX, ACT, and CAF residues present in influents and effluents of hospital wastewater treatment plants (WWTPs) to aquatic ecosystem. These finding are expected to provide scientific-based evidence for the development of hospital waste management and environmental management programs in Vietnam.
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Affiliation(s)
- Anh T P Hoang
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
| | - Manh Cuong Do
- Health Environment Management Agency, Ministry of Health, 12014, Hanoi, Viet Nam
| | - Kyoung-Woong Kim
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea.
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2
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Xu X, Qi Z, Han X, Wang Y, Yu M, Geng Z. Combined-task deep network based on LassoNet feature selection for predicting the comorbidities of acute coronary syndrome. Comput Biol Med 2024; 170:107992. [PMID: 38242014 DOI: 10.1016/j.compbiomed.2024.107992] [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: 08/24/2023] [Revised: 01/03/2024] [Accepted: 01/13/2024] [Indexed: 01/21/2024]
Abstract
Acute coronary syndrome (ACS) is a multifaceted cardiovascular condition frequently accompanied by multiple comorbidities, which can have significant implications for patient outcomes and treatment approaches. Precisely predicting these comorbidities is crucial for providing personalized care and making well-informed clinical decisions. However, there is a shortage of research investigating the identification of risk factors associated with ACS comorbidities and accurately predicting their likelihood of occurrence beyond heart failure. In this study, an approach called Combined-task Deep Network based on LassoNet feature selection (CDNL) is presented for predicting ACS comorbidities, including hypertension, diabetes, hyperlipidemia, and heart failure. In order to identify crucial biomarkers associated with ACS comorbidities, the proposed framework first incorporates LassoNet, which extends Lasso regression to the deep network by adding a skip (residual) layer. Additionally, a correlation score calculation method across tasks is introduced based on measuring the overlap of identified biomarkers and their assigned importance. This method enables the development of an optimal combined-task prediction model for each ACS comorbidity, addressing the challenge of limited representations in traditional multi-task learning. Our evaluation, conducted through a meticulous cross-sectional study at a tertiary hospital in China, involved a dataset of 2941 samples with 42 clinical features. The results demonstrate that CDNL facilitates the identification of significant biomarkers and achieves an average improvement in AUC of 4.93% and 8.58% compared to deep learning multi-layer neural network (DNN) and SVM, respectively. Additionally, it shows an average improvement of 2.64% and 1.92% compared to two state-of-the-art multi-task models.
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Affiliation(s)
- Xiaolu Xu
- School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China
| | - Zitong Qi
- Department of Statistics, University of Washington, Seattle, WA 98195, USA
| | - Xiumei Han
- College of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China
| | - Yuxing Wang
- Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China
| | - Ming Yu
- Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China
| | - Zhaohong Geng
- Department of Cardiology, Second Affiliated Hospital of Dalian Medical University, Dalian 116023, China.
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3
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Denck J, Ozkirimli E, Wang K. Machine-learning-based adverse drug event prediction from observational health data: A review. Drug Discov Today 2023; 28:103715. [PMID: 37467879 DOI: 10.1016/j.drudis.2023.103715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/15/2023] [Accepted: 07/12/2023] [Indexed: 07/21/2023]
Abstract
Adverse drug events (ADEs) are responsible for a significant number of hospital admissions and fatalities. Machine learning models have been developed to assess the individual patient risk of having an ADE. In this article, we have reviewed studies addressing the prediction of ADEs in observational health data with machine learning. The field of individualised ADE prediction is rapidly emerging through the increasing availability of additional data modalities (e.g., genetic data, screening data, wearables data) and advanced deep learning models such as transformers. Consequently, personalised adverse drug event predictions are becoming more feasible and tangible.
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Affiliation(s)
- Jonas Denck
- Roche Informatics, F. Hoffmann-La Roche AG, Kaiseraugst, Switzerland.
| | - Elif Ozkirimli
- Roche Informatics, F. Hoffmann-La Roche AG, Kaiseraugst, Switzerland
| | - Ken Wang
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center, Basel, Switzerland
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Jaltotage B, Ihdayhid AR, Lan NSR, Pathan F, Patel S, Arnott C, Figtree G, Kritharides L, Shamsul Islam SM, Chow CK, Rankin JM, Nicholls SJ, Dwivedi G. Artificial Intelligence in Cardiology: An Australian Perspective. Heart Lung Circ 2023; 32:894-904. [PMID: 37507275 DOI: 10.1016/j.hlc.2023.06.703] [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: 04/28/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, while also detailing challenges such as data privacy, ethical considerations, and integration within existing health infrastructures. Overall, this paper aims to provide insights into the potential benefits of artificial intelligence in cardiology, while also acknowledging the barriers that need to be addressed to ensure safe and effective implementation into an Australian health system.
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Affiliation(s)
- Biyanka Jaltotage
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia. https://twitter.com/cardiacimager
| | - Abdul Rahman Ihdayhid
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; School of Medicine, Curtin University, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Nick S R Lan
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia
| | - Faraz Pathan
- Department of Cardiology, Nepean Hospital and Charles Perkins Centre, Nepean Clinical School, Faculty of Medicine and Health, Sydney University, Sydney, NSW, Australia
| | - Sanjay Patel
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia and The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Clare Arnott
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, NSW, Australia and The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia
| | - Gemma Figtree
- Kolling Institute, Royal North Shore Hospital and Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Leonard Kritharides
- Department of Cardiology, Concord Repatriation General Hospital and ANZAC Research Institute, University of Sydney, Sydney, NSW, Australia
| | | | - Clara K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - James M Rankin
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia
| | | | - Girish Dwivedi
- Department of Cardiology, Fiona Stanley Hospital, Perth, Australia; Harry Perkins Institute of Medical Research, School of Medicine, University of Western Australia, Perth, Australia.
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Xu M, Yang F, Shen B, Wang J, Niu W, Chen H, Li N, Chen W, Wang Q, HE Z, Ding R. A bibliometric analysis of acute myocardial infarction in women from 2000 to 2022. Front Cardiovasc Med 2023; 10:1090220. [PMID: 37576112 PMCID: PMC10416645 DOI: 10.3389/fcvm.2023.1090220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 06/01/2023] [Indexed: 08/15/2023] Open
Abstract
Background Plenty of publications had been written in the last several decades on acute myocardial infarction (AMI) in women. However, there are few bibliometric analyses in such field. In order to solve this problem, we attempted to examine the knowledge structure and development of research about AMI in women based on analysis of related publications. Method The Web of Science Core Collection was used to extract all publications regarding AMI in women, ranging from January 2000 to August 2022. Bibliometric analysis was performed using VOSviewer, Cite Space, and an online bibliometric analysis platform. Results A total of 14,853 publications related to AMI in women were identified from 2000 to 2022. Over the past 20 years, the United States had published the most articles in international research and participated in international cooperation the most frequently. The primary research institutions were Harvard University and University of Toronto. Circulation was the most cited journal and had an incontrovertible academic impact. 67,848 authors were identified, among which Harlan M Krumholz had the most significant number of articles and Thygesen K was co-cited most often. And the most common keywords included risk factors, disease, prognosis, mortality, criteria and algorithm. Conclusion The research hotspots and trends of AMI in women were identified and explored using bibliometric and visual methods. Researches about AMI in women are flourishing. Criteria and algorithms might be the focus of research in the near future, which deserved great attentions.
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Affiliation(s)
- Ming Xu
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
- Department of Cardiology, Shanghai Navy Feature Medical Center, Naval Medical University, Shanghai, China
| | - Fupeng Yang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Bin Shen
- Department of Cardiology, Shanghai Navy Feature Medical Center, Naval Medical University, Shanghai, China
| | - Jiamei Wang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Wenhao Niu
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Hui Chen
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Na Li
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Wei Chen
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Qinqin Wang
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Zhiqing HE
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
| | - Ru Ding
- Department of Cardiology, Shanghai Changzheng Hospital, Naval Medical University, Shanghai, China
- Shanghai Cardiovascular Institute of Integrative Medicine, Shanghai, China
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Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study. PLoS One 2022; 17:e0278217. [PMID: 36445881 PMCID: PMC9707772 DOI: 10.1371/journal.pone.0278217] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 11/11/2022] [Indexed: 12/02/2022] Open
Abstract
Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.
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