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Bermudez C, Kerley CI, Ramadass K, Farber-Eger EH, Lin YC, Kang H, Taylor WD, Wells QS, Landman BA. Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia. Magn Reson Imaging 2024; 109:49-55. [PMID: 38430976 DOI: 10.1016/j.mri.2024.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.
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Affiliation(s)
- Camilo Bermudez
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Cailey I Kerley
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Karthik Ramadass
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Eric H Farber-Eger
- Department of Cardiology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Ya-Chen Lin
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Warren D Taylor
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Cardiology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bennett A Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Psychiatry, Vanderbilt University Medical Center, Nashville, TN, USA.
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Tang XE, Lu T, Zhou YC, Zhan MJ, Chen W, Peng Z, Liu JH, Gui YF, Deng ZH, Fan F. Adult age estimation from the sternum using maximum intensity projection images of CT and data mining in a Chinese population. Int J Legal Med 2024; 138:961-970. [PMID: 38240839 DOI: 10.1007/s00414-024-03161-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 01/08/2024] [Indexed: 04/11/2024]
Abstract
This study aimed to explore and develop data mining models for adult age estimation based on CT reconstruction images from the sternum. Maximum intensity projection (MIP) images of chest CT were retrospectively collected from a modern Chinese population, and data from 2700 patients (1349 males and 1351 females) aged 20 to 70 years were obtained. A staging technique within four indicators was applied. Several data mining models were established, and mean absolute error (MAE) was the primary comparison parameter. The intraobserver and interobserver agreement levels were good. Within internal validation, the optimal data mining model obtained the lowest MAE of 9.08 in males and 10.41 in females. For the external validation (N = 200), MAEs were 7.09 in males and 7.15 in females. In conclusion, the accuracy of our model for adult age estimation was among similar studies. MIP images of the sternum could be a potential age indicator. However, it should be combined with other indicators since the accuracy level is still unsatisfactory.
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Affiliation(s)
- Xian-E Tang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Ting Lu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yu-Chi Zhou
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Meng-Jun Zhan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Wang Chen
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Zhao Peng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Jun-Hong Liu
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Yu-Fan Gui
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China
| | - Zhen-Hua Deng
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
| | - Fei Fan
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, People's Republic of China.
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Liu Y, Shen T, Liu J, Yu X, Li Q, Chen T, Jiang T. CFHR1 involvement in bile duct carcinoma: Insights from a data mining study. Anal Biochem 2024; 688:115474. [PMID: 38286352 DOI: 10.1016/j.ab.2024.115474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 01/25/2024] [Accepted: 01/26/2024] [Indexed: 01/31/2024]
Abstract
The aim of this study is to investigate the role of CFHR1 in bile duct carcinoma (BDC) and its mechanism of action, and we hope that our analysis and research will contribute to a better understanding of cholangiocarcinoma (BDC) disease genesis, progression and the development of new therapeutic strategies. The prognostic receiver operating characteristic curve of CFHR1 was generated using survival ROC. The ROC curve for CFHR1 showed that there is a correlation between CFHR1 expression and clinicopathological parameters and has an impact on poor prognosis. STRING was used to predict the protein-protein interaction network of the identified genes, and the Microenvironment Cell Populations counter algorithm was used to analyze immune cell infiltration within the BDC. The combined analysis showed that CFHR1 was found to be upregulated in BDC tissues, along with a total of 20 related differentially expressed genes (DEGs) (8 downregulated and 12 upregulated genes). Also, the results showed that the expression of CFHR1 is correlated with immune cell infiltration in tumor and immune cell markers in BDC (P < 0.05). In addition, we have verified experimentally the biological function of CFHR1. These findings suggest that CFHR1 may be a prognostic marker and a potential therapeutic target for BDC. Information regarding the detailed roles of CFHR1 in BDC could be valuable for improving the diagnosis and treatment of this rare cancer.
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Affiliation(s)
- Yan Liu
- Oncology Intervention Department, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China; Institute of Tumor Intervention, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 20062, China
| | - Tianhao Shen
- Oncology Intervention Department, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Jianming Liu
- Oncology Intervention Department, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Xue Yu
- Oncology Intervention Department, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Qiuying Li
- Oncology Intervention Department, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China
| | - Tingsong Chen
- Department of Oncology, Shanghai Seventh People's Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200137, China.
| | - Tinghui Jiang
- Oncology Intervention Department, Putuo Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, 200062, China.
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Guo H, Song Y, Chu Y, He Y, Gao W, Guan X. Data-mining framework for in-depth quantitative study of influences on low-wind-velocity area from morphological parameters of cuboid-form buildings. Heliyon 2024; 10:e29137. [PMID: 38623228 PMCID: PMC11016623 DOI: 10.1016/j.heliyon.2024.e29137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/31/2024] [Accepted: 04/01/2024] [Indexed: 04/17/2024] Open
Abstract
Wind environment is important in architectural sustainable design, as existing studies show that it can be considerably influenced by building morphologies. This study aimed to develop a data-mining framework to quantitatively evaluate and compare influences on Low-Wind-Velocity Area (LWVA) of common cuboid-form buildings with typical morphological parameters. The data-mining framework was originally developed by integrating multiple computational methods for rapid in-depth iterative analyses, including the generation of building models using parametric modelling, the big data generation based on hybrid model, and the statistical metric analysis method. The hybrid model was created by combining the CFD model and machine learning model. The accuracy and efficiency of the framework were fully demonstrated through the comprehensive validation and analyses of different models. The data of more than fifty thousand building cases with different morphological parameters and relevant wind conditions were generated and analyzed. Influences on LWVA of morphological parameters of cuboid-form building was comprehensively evaluated, including the visualization of multiple parameters, calculation and comparison of several correlation coefficients. It suggested that the reduction of height and width on the windward side would significantly decrease the LWVA and promote the outdoor ventilation. The change of depth would have relatively limited influence on the LWVA. Multivariate regression model-fit and variance analyses were further implemented, and it was found that there was a relatively significant linear correlation between the LWVA and morphological parameters. The equation of multivariate regression model was provided for extra rapid prediction. The study outcome could contribute to efficient evaluation of LWVA and provide useful information for sustainable design.
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Affiliation(s)
- Han Guo
- Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources of China, Shenzhen, China
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
- School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, China
| | - Yehao Song
- School of Architecture, Tsinghua University, Beijing, China
| | - Yingnan Chu
- School of Architecture, Tsinghua University, Beijing, China
| | - Yi He
- School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, China
- National Center of Technology Innovation for Digital Construction, Wuhan, China
| | - Weizhi Gao
- School of Architecture, Tsinghua University, Beijing, China
| | - Xiaoqing Guan
- School of Architecture, Tsinghua University, Beijing, China
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Wang W, Chen S, Chen L, Wang L, Chao Y, Shi Z, Lin D, Yang K. Drivers distinguishing of PAHs heterogeneity in surface soil of China using deep learning coupled with geo-statistical approach. J Hazard Mater 2024; 468:133840. [PMID: 38394897 DOI: 10.1016/j.jhazmat.2024.133840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/16/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
Although numerous studies have reported the influencing factors of polycyclic aromatic hydrocarbons (PAHs) in surface soil from source, process or soil perspectives, the mechanism of PAHs heterogeneity in surface soil are still not well understood. In this study, the effects of 16 PAHs in surface soil of China sampled between 2003 and 2020 with their 17 "source-process-sink" factors at 1 km resolution (N = 660)) were explored using deep learning (eXtreme Gradient Boosting) to mine key information from complex dataset under the optimized parameters (i.e., learning rate = 0.05, maximum depth = 5, sub-sample = 0.8). It was observed that top five factors of 16 PAH had the largest cumulative contribution (i.e., from 84.8% to 98.1%) on their soil concentrations. PAH emission was the predominant driver, and its effect on soil PAH increases with increasing logKow. Soil was the second driver, in which clay can promote the partition of PAHs with low or middle logKow. However, sand can accumulate those congeners with high logKow. Moreover, the deep learning plus geo-statistical models (with low deviation for testing dataset (N = 283)) were capable of predicting soil PAH concentrations using their drivers with high accuracy. This study improved the understanding of the environmental fate and spatial variability of soil PAHs, as well as provided a novel technique (i.e., deep learning coupled with geo-statistics) for accurate prediction of soil pollutants.
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Affiliation(s)
- Weiwei Wang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; Key Laboratory of Environmental Pollution and Ecological Health of Ministry of Education, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Songchao Chen
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China
| | - Lu Chen
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China
| | - Lingwen Wang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China
| | - Yang Chao
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China
| | - Zhou Shi
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Environmental Pollution and Ecological Health of Ministry of Education, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Daohui Lin
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Environmental Pollution and Ecological Health of Ministry of Education, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China
| | - Kun Yang
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China; ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China; Key Laboratory of Environmental Pollution and Ecological Health of Ministry of Education, Hangzhou 310058, China; Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou 310058, China.
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Salimi N, Gere B, Shahab A. State-Federal Vocational Rehabilitation Services, Demographic Characteristics and Employment Outcomes for Native Americans with Mental Illnesses. Community Ment Health J 2024; 60:442-456. [PMID: 37828363 DOI: 10.1007/s10597-023-01191-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 09/13/2023] [Indexed: 10/14/2023]
Abstract
There were 9.7 million Native Americans (American Indian, Alaska Native-AI/AN- these acronyms will be used interchangeably with Native Americans throughout the paper) in 2019 comprising 2.9% of the U.S. population. Native American populations have disproportionately higher rates of mental illnesses compared to other racial groups in the U.S. Mental health is a significant public health concern for this population, impacting different areas of their lives including employment. Additionally, Native Americans continue to experience significant disparities in access to Vocational Rehabilitation (VR) services and have poor employment outcomes. However, little is known about the relationships among demographic factors, vocational rehabilitation services, and employment outcomes of Native Americans with mental illness. Consequently, the current study examined how demographic factors and VR services are related to successful employment outcomes for Native American VR clients with mental illnesses using data from the Rehabilitation Services Administration (RSA) program year (2019) Case Service Report (9-11). Both descriptive analysis and data mining approaches were used to answer the research questions. Chi-square Automatic Interaction Detector (CHAID) analysis was used to determine which of the VR services could best predict the successful employment outcome of Native Americans with mental illness. The findings of the data mining approach revealed that among all the vocational rehabilitation services, job placement assistance was the strongest predictor of successful employment among Native American clients with mental illnesses. The second most important service predicting successful employment for those who received job placement assistance was shown to be maintenance. Implications for rehabilitation counselors and future research are discussed.
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Affiliation(s)
- Nahal Salimi
- Rehabilitation Counseling & Disability Services, School of Interdisciplinary Health Professions, College of Health & Human Sciences, Northern Illinois University, 353 Wirtz Hall, DeKalb, IL, 60115, USA.
| | - Bryan Gere
- Department of Rehabilitation, School of Pharmacy and Health Professions, University of Maryland Eastern Shore, Hazel Hall #1109, Princess Anne, MD, 21853, USA
| | - Amin Shahab
- Department of Computer Science and Operations Research, Université de Montréal, Québec City, Canada
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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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Zhang S, Wang Y, Qi Z, Tong S, Zhu D. Data mining and analysis of adverse event signals associated with teprotumumab using the Food and Drug Administration adverse event reporting system database. Int J Clin Pharm 2024; 46:471-479. [PMID: 38245664 DOI: 10.1007/s11096-023-01676-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Teprotumumab was approved by the US Food and Drug Administration (FDA) for the treatment of thyroid eye disease in 2020. However, its adverse events (AEs) have not been investigated in real-world settings. AIM This study aimed to detect and evaluate AEs associated with teprotumumab in the real-world setting by conducting a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) database. METHOD Reporting odds ratio (ROR) was used to detect risk signals from the data from January 2020 to March 2023 in the FAERS database. RESULTS A total of 3,707,269 cases were retrieved, of which 1542 were related to teprotumumab. The FAERS analysis identified 99 teprotumumab-related AE signals in 14 System Organ Classes (SOCs). The most frequent AEs were muscle spasms (n = 287), fatigue (n = 174), blood glucose increase (n = 121), alopecia (n = 120), nausea (n = 118), hyperacusis (n = 117), and headache (n = 117). The AEs with strongest signal strengths were autophony (ROR = 14,475.49), deafness permanent (ROR = 1853.35), gingival recession (ROR = 190.74), deafness neurosensory (ROR = 129.89), nail growth abnormal (ROR = 103.67), onychoclasis (ROR = 73.58), ear discomfort (ROR = 72.88), and deafness bilateral (ROR = 62.46). Eleven positive AE signals were found at the standardized MedDRA queries (SMQs) level, of which the top five SMQs were hyperglycemia/new-onset diabetes mellitus, hearing impairment, gastrointestinal nonspecific symptoms and therapeutic procedures, noninfectious diarrhea, and hypertension. Age significantly increased the risk of hearing impairment. CONCLUSION This study identified potential new and unexpected AE signals of teprotumumab. Our findings emphasize the importance of pharmacovigilance analysis in the real world to identify and manage AEs effectively, ultimately improving patient safety during teprotumumab treatment.
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Affiliation(s)
- Sha Zhang
- Department of Pharmacy, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yidong Wang
- Department of Pharmacy, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhan Qi
- Department of Pharmacy, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Shanshan Tong
- Department of Pharmacy, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Deqiu Zhu
- Department of Pharmacy, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
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He T, Wang D, Zhang X, Liu J, Fang S, Zhang Z, Liu H. Dose-response relationship of levodopa with dyskinesia in Parkinson's disease: A systematic review and meta-analysis. Heliyon 2024; 10:e27956. [PMID: 38515703 PMCID: PMC10955298 DOI: 10.1016/j.heliyon.2024.e27956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
Despite existing evidence linking dyskinesia to levodopa, the primary treatment for Parkinson's, the dose-response relationship and risk factors remain uncertain. In this study, the risk for dyskinesia in patients with Parkinson's disease receiving levodopa was evaluated via meta-analysis and meta-regression approaches to examine dyskinesia risk factors more reliably and improve treatment strategies and patient care. The PubMed and Embase databases were searched to identify randomized controlled trials comparing levodopa with other anti-Parkinson's drugs published in English before June 31, 2023. The primary outcome was dyskinesia, and a risk of bias assessment was performed. In total, 24 studies met the inclusion criteria; 21 had a low risk of bias, and 3 had a high risk of bias. These studies included 4698 patients with Hoehn and Yahr Grade I-III Parkinson's disease. Our meta-analysis showed that the risk of dyskinesia was higher for levodopa than for other anti-Parkinson's drugs (odds ratio: 2.52 [95% confidence interval: 1.84-3.46]). Dyskinesia was not related to age (slope coefficient: 0.185 [0.095]; P = 0.061), disease duration (slope coefficient: 0.011 [0.018]; P = 0.566), or treatment duration (slope coefficient: 0.008 [0.007]; P = 0.216). The mean levodopa equivalent dose (slope coefficient: 0.004 [0.001]; P = 0.001) in the experimental group and the differences in drug doses between the experimental and control groups were correlated with the risk of dyskinesia. Results of randomized controlled trials supported an association between the levodopa dose and dyskinesia in patients with Parkinson's disease. Compared with levodopa users, users of other anti-Parkinson's drugs had a lower incidence of dyskinesia. Age, disease duration, and treatment duration were not correlated with dyskinesia. These findings suggest that anti-Parkinson's drugs other than levodopa, particularly in cases of early-stage Parkinson's disease, should be considered to reduce the risk of dyskinesia.
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Affiliation(s)
- Taozhi He
- School of Medicine, Jinan University, 601 West Huangpu Avenue, Guangzhou, 510632, China
| | - Dai Wang
- School of Medicine, Jinan University, 601 West Huangpu Avenue, Guangzhou, 510632, China
| | - Xinyu Zhang
- School of Medicine, Jinan University, 601 West Huangpu Avenue, Guangzhou, 510632, China
| | - Jiawen Liu
- School of Medicine, Jinan University, 601 West Huangpu Avenue, Guangzhou, 510632, China
| | - Shiyu Fang
- School of Medicine, Jinan University, 601 West Huangpu Avenue, Guangzhou, 510632, China
| | - Zhe Zhang
- Shandong University of Traditional Chinese Medicine, 4655 Daxue Road, Jinan, 250355, China
| | - Hongjie Liu
- School of Medicine, Jinan University, 601 West Huangpu Avenue, Guangzhou, 510632, China
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Nevoret C, Tran Y, Guendouz S, Lavenu A, Katsahian S, Damy T, Tropeano AI. Cardiovascular disease healthcare trajectories: descriptions, similarities, mortality rates of heart failure in France. ESC Heart Fail 2024. [PMID: 38509817 DOI: 10.1002/ehf2.14753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 01/31/2024] [Accepted: 02/18/2024] [Indexed: 03/22/2024] Open
Abstract
AIMS The primary objectives of this study were to analyse the nationwide healthcare trajectories of heart failure (HF) patients in France, 2 years after their first hospitalization, and to measure sequence similarities. Secondary objectives were to identify the association between trajectories and the risk of mortality. METHODS AND RESULTS A retrospective, observational study was conducted using data extracted from the Echantillon Généraliste des Bénéficiaires database, covering the period from 1 January 2008 to 31 December 2018. Follow-up concluded upon death or at the end of the study. We developed a methodology specific to healthcare data by extracting frequent healthcare trajectories and measuring their similarity for use in a survival machine learning analysis. In total, 11 488 HF patients were included and followed up for an average of 2.9 ± 1.3 years. The mean age of the patients was 78.0 ± 13.2 years. The first-year mortality rate was 31.7% and increased to 78.8% at 5 years. Fifty per cent of patients experienced re-hospitalization for reasons related to cardiovascular diseases. We identified 1707 hospitalization sequences, and 21 sequences were associated with survival, while 15 sequences were linked to mortality. In all our models, age and gender emerged as the most significant predictors of mortality (permutation feature importance: 0.099 ± 0.00078 and 0.0087 ± 0.00018, respectively; weights could be interpreted in relative terms). Specifically, the age at initial hospitalization for HF was positively associated with mortality. Gender (male: 49.5%) was associated with poorer prognoses. Healthcare trajectories, including non-surgical device treatments, valve replacements, and atrial fibrillation ablation, were associated with a better prognosis (permutation feature importance: 0.0047 ± 0.00011, 0.0014 ± 0.000073, and 0.00095 ± 0.000097, respectively), except in cases where these invasive treatments preceded or followed hospitalization for cardiac decompensation. The predominant negative prognosis sequences were mostly those that included HF-related hospitalizations before or after other-related hospitalizations (permutation feature importance: 0.0007 ± 0.000091 and 0.00011 ± 0.000045, respectively). CONCLUSIONS We highlight the value of healthcare trajectories on frequent hospitalization sequences, mortality, and prognosis and indicate the necessary prognostic value of HF re-hospitalization. Our work may be an essential tool for better identification of at-risk patients in order to increase and improve personalized care in the future.
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Affiliation(s)
- Camille Nevoret
- CEMKA, Bourg-la-Reine, France
- Clinical Research Unit, CIC-EC 1418, European Hospital Georges-Pompidou, APHP, Paris, France
| | - Yohann Tran
- Clinical Research Unit, CIC-EC 1418, European Hospital Georges-Pompidou, APHP, Paris, France
| | - Soulef Guendouz
- Referral Center for Cardiac Amyloidosis, Mondor Amyloidosis Network, GRC Amyloid Research Institute and Cardiology Department, INSERM Unit U955, Team 8, Paris-Est Creteil University, Hospital Henri Mondor, Val-de-Marne, Créteil, France
| | - Audrey Lavenu
- Univ Rennes, CIC 1414 INSERM, IRMAR, Mathematics Institute of Rennes CNRS, Rennes, France
| | - Sandrine Katsahian
- Clinical Research Unit, CIC-EC 1418, European Hospital Georges-Pompidou, APHP, Paris, France
| | - Thibaud Damy
- Referral Center for Cardiac Amyloidosis, Mondor Amyloidosis Network, GRC Amyloid Research Institute and Cardiology Department, INSERM Unit U955, Team 8, Paris-Est Creteil University, Hospital Henri Mondor, Val-de-Marne, Créteil, France
| | - Anne-Isabelle Tropeano
- Clinical Research Unit, CIC-EC 1418, European Hospital Georges-Pompidou, APHP, Paris, France
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11
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Yang H, Liu X, Chu X, Xie B, Zhu G, Li H, Yang J. Optimization of tight gas reservoir fracturing parameters via gradient boosting regression modeling. Heliyon 2024; 10:e27015. [PMID: 38463839 PMCID: PMC10923685 DOI: 10.1016/j.heliyon.2024.e27015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 02/05/2024] [Accepted: 02/22/2024] [Indexed: 03/12/2024] Open
Abstract
In China, the exploitation of most unconventional oil and gas reservoirs is dependent on hydraulic fracturing, which is a key method employed when developing tight gas formations. Numerous scholars and field engineers, both domestically and internationally, have conducted extensive numerical simulations and physical experiments to study crack propagation and predict post-fracturing productivity in hydraulic fracturing. Although some progress has been reported in this regard, it is difficult to accurately predict the well productivity using mechanistic models owing to the vertical multilayered development of tight gas reservoirs. In this study, vertical fractured wells in a block of Sulige gas field were examined. The block relied on hydraulic fracturing to produce tight gases. However, as development progressed, the available reservoir environment deteriorated, large differences emerged between wells after fracturing, and the fracturing results did not meet the expectations. In this study, geological, construction, and generation data for this block that had been collected since 2007 were analyzed. After applying multiple machine-learning methods to filter outliers and fill in missing values, k-means clustering, classification enhancement, extreme gradient enhancement, and LightGBM algorithms were used to establish a regression model. The analysis results revealed that the regression accuracy of the cluster test set was as high as 70% and that the LightGBM model had the best regression effect among the 227 stripper wells in the block. After optimizing the fracturing construction parameters (fracturing fluid volume, proppant volume, liquid-nitrogen volume, and pumping rate), the average fracturing fluid and liquid-nitrogen volumes per well decreased, whereas the unit reservoir proppant and liquid-nitrogen volumes increased. The results also revealed that 182 wells showed an improved initial production capacity during fracturing. The average gas production index per meter increased by 22.04%. This approach enabled rapid and efficient production forecasting and construction optimization. Moreover, this represents a novel fracture design method that is applicable to onsite engineers in tight gas production fields in the Ordos region.
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Affiliation(s)
- Huohai Yang
- School of Petroleum Engineering, Southwest Petroleum University, Chengdu, China
| | - Xuanyu Liu
- CNPC Greatwall Drilling Company, Beijing, China
| | - Xiangshu Chu
- Sinopec Shengli Oilfield Hekou Oil Production Plant, Dongying, China
| | - Binghong Xie
- Shunan Gas Field, PetroChina Southwest Oil and Gas Field Company, Luzhou, China
| | - Ge Zhu
- Sinopec Shengli Oilfield Hekou Oil Production Plant, Dongying, China
| | - Hancheng Li
- School of Petroleum Engineering, Southwest Petroleum University, Chengdu, China
| | - Jun Yang
- Exploration and Development Research Institute of PetroChina Changqing Oilfield Company, Xi'an, China
- National Engineering Laboratory for Exploration and Development of Low-Permeability Oil & Gas Fields, Xi'an, China
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12
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Xie Y, Li Y, Liu D, Zou Y, Wang H, Pan L. Clinical rules of Acupoint selection for cancer pain opioid-induced constipation based on journal literature data mining: A systematic review. Heliyon 2024; 10:e26170. [PMID: 38439874 PMCID: PMC10909647 DOI: 10.1016/j.heliyon.2024.e26170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 01/30/2024] [Accepted: 02/08/2024] [Indexed: 03/06/2024] Open
Abstract
Objective To analyse and summarise the regularity of acupoint selection in the treatment of opioid-induced constipation (OIC) in patients with cancer pain using a data mining technique and provide a reference for clinical practice and more valuable treatment options. Methods The acupoint prescription database for the treatment of OIC-related cancer pain was established by searching the relevant literature on randomised controlled trials involving acupoint therapy for OIC-related cancer pain in seven major databases, including the China National Knowledge Infrastructure, Wanfang and VIP Chinese scientific journal databases, from database establishment to December 31, 2022. The main therapeutic measures of acupoint prescription, frequency of acupoint use and its subordinate meridians and subordinate sites were then analysed. Through systematic clustering and association rule analysis, the core acupoint prescriptions and most commonly used acupoint compatibility of acupoint therapy for OIC-related cancer pain were obtained. Results A total of 649 articles were retrieved, with 72 articles included after screening. The treatment measures were found to be mainly acupoint applications involving 28 acupoints, with a total frequency of 234. The three most frequently used acupoints were Shenque, Tianshu and Zusanli. The number of points used in the Foot-Yangming stomach meridian was the highest. Commonly used acupoints were mainly distributed in the abdomen. The compatibility of two commonly used acupoints was obtained through systematic clustering. Through association rule analysis, it was found that in the compatibility of acupoints, the strongest correlation was between Tianshu and Zusanli, and their frequency of application was the highest. Conclusion Tianshu and Zusanli are the core acupoints for acupoint therapy in the treatment of OIC-related cancer pain, and the Shangjuxu-Zhigou-Zusanli, Qihai-Guanyuan and Zhongwan-Tianshu acupoints exhibit the highest compatibility. This study provides a reference for the clinical acupoint selection programme of acupuncture and moxibustion in the treatment of OIC-related cancer pain.
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Affiliation(s)
- Yuan Xie
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Yuanyuan Li
- Department of Acupuncture, Dongfang Hospital Beijing University of Chinese Medicine, Beijing, 100078, China
| | - Di Liu
- Department of Acupuncture, Dongfang Hospital Beijing University of Chinese Medicine, Beijing, 100078, China
| | - Yi Zou
- Department of Acupuncture, Dongfang Hospital Beijing University of Chinese Medicine, Beijing, 100078, China
| | - Haiying Wang
- Beijing University of Chinese Medicine, Beijing, 100029, China
| | - Liang Pan
- Department of Acupuncture, Dongfang Hospital Beijing University of Chinese Medicine, Beijing, 100078, China
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13
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Janke M, Mäder P. 7 Dimensions of software change patterns. Sci Rep 2024; 14:6141. [PMID: 38480781 PMCID: PMC10937631 DOI: 10.1038/s41598-024-54894-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 02/18/2024] [Indexed: 03/17/2024] Open
Abstract
Evolving software is a highly complex and creative problem in which a number of different strategies are used to solve the tasks at hand. These strategies and reoccurring coding patterns can offer insights into the process. However, they can be highly project or even task-specific. We aim to identify code change patterns in order to draw conclusions about the software development process. For this, we propose a novel way to calculate high-level file overarching diffs, and a novel way to parallelize pattern mining. In a study of 1000 Java projects, we mined and analyzed a total of 45,000 patterns. We present 13 patterns, showing extreme points of the 7 pattern categories we identified. We found that a large number of high-level change patterns exist and occur frequently. The majority of mined patterns were associated with a specific project and contributor, where and by whom it was more likely to be used. While a large number of different code change patterns are used, only a few, mostly unsurprising ones, are common under all circumstances. The majority of code change patterns are highly specific to different context factors that we further explore.
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Affiliation(s)
- Mario Janke
- Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Helmholtzplatz, 98693, Ilmenau, Thüringen, Germany.
| | - Patrick Mäder
- Data-intensive Systems and Visualization Group, Technische Universität Ilmenau, Helmholtzplatz, 98693, Ilmenau, Thüringen, Germany
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14
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Austin E, Makwana S, Trabelsi A, Largeron C, Zaïane OR. Uncovering Flat and Hierarchical Topics by Community Discovery on Word Co-occurrence Network. Data Sci Eng 2024; 9:41-61. [PMID: 38558962 PMCID: PMC10980674 DOI: 10.1007/s41019-023-00239-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 12/05/2023] [Accepted: 12/14/2023] [Indexed: 04/04/2024]
Abstract
Topic modeling aims to discover latent themes in collections of text documents. It has various applications across fields such as sociology, opinion analysis, and media studies. In such areas, it is essential to have easily interpretable, diverse, and coherent topics. An efficient topic modeling technique should accurately identify flat and hierarchical topics, especially useful in disciplines where topics can be logically arranged into a tree format. In this paper, we propose Community Topic, a novel algorithm that exploits word co-occurrence networks to mine communities and produces topics. We also evaluate the proposed approach using several metrics and compare it with usual baselines, confirming its good performances. Community Topic enables quick identification of flat topics and topic hierarchy, facilitating the on-demand exploration of sub- and super-topics. It also obtains good results on datasets in different languages.
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Affiliation(s)
- Eric Austin
- University of Alberta, Edmonton, AB T6G 2R3 Canada
- Alberta Machine Intelligence Institute, Edmonton, AB T5J 3B1 Canada
| | - Shraddha Makwana
- University of Alberta, Edmonton, AB T6G 2R3 Canada
- Alberta Machine Intelligence Institute, Edmonton, AB T5J 3B1 Canada
| | | | | | - Osmar R. Zaïane
- University of Alberta, Edmonton, AB T6G 2R3 Canada
- Alberta Machine Intelligence Institute, Edmonton, AB T5J 3B1 Canada
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15
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Premkumar M, Sinha G, Ramasamy MD, Sahu S, Subramanyam CB, Sowmya R, Abualigah L, Derebew B. Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems. Sci Rep 2024; 14:5434. [PMID: 38443569 PMCID: PMC10914809 DOI: 10.1038/s41598-024-55619-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 02/26/2024] [Indexed: 03/07/2024] Open
Abstract
This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.
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Affiliation(s)
- Manoharan Premkumar
- Department of Electrical & Electronics Engineering, Dayananda Sagar College of Engineering, Kumaraswamy Layout, Bengaluru, Karnataka, 560078, India.
| | - Garima Sinha
- Department of Computer Science and Engineering, Jain University, Ramanagaram, Bengaluru, Karnataka, India
| | - Manjula Devi Ramasamy
- Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India
| | - Santhoshini Sahu
- Department of Computer Science & Engineering, GMR Institute of Technology, Rajam, Srikakulam, Andhra Pradesh, India
| | | | - Ravichandran Sowmya
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Laith Abualigah
- Computer Science Department, Al al-Bayt University, Mafraq, 25113, Jordan
- Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491, Tabuk, Saudi Arabia
- Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, 19328, Jordan
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, 13-5053, Lebanon
- School of Engineering and Technology, Sunway University Malaysia, 27500, Petaling Jaya, Malaysia
- College of Engineering, Yuan Ze University, Taoyuan, Taiwan
- Department of Statistics, College of Natural and Computational Science, Mizan-Tepi University, Tepi Bushira, Ethiopia
| | - Bizuwork Derebew
- Applied science research center, Applied science private university, Amman, 11931, Jordan.
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16
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Jiang T, Lau SH, Zhang J, Chan LC, Wang W, Chan PK, Cai J, Wen C. Radiomics signature of osteoarthritis: Current status and perspective. J Orthop Translat 2024; 45:100-106. [PMID: 38524869 PMCID: PMC10958157 DOI: 10.1016/j.jot.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 03/26/2024] Open
Abstract
Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis. The translational potential of this article Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.
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Affiliation(s)
- Tianshu Jiang
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sing-Hin Lau
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lok-Chun Chan
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wei Wang
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ping-Keung Chan
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chunyi Wen
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
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17
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Nourani V, Maleki S, Najafi H, Baghanam AH. A fuzzy logic-based approach for groundwater vulnerability assessment. Environ Sci Pollut Res Int 2024; 31:18010-18029. [PMID: 36940030 DOI: 10.1007/s11356-023-26236-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Groundwater vulnerability assessment systems have been developed to protect groundwater resources. The DRASTIC model calculates the vulnerability index of the aquifer based on seven effective parameters. The application of expert opinion in rating and weighting parameters is the DRASTIC model's major weakness, which increases uncertainty. This study developed a Mamdani fuzzy logic (MFL) in combination with data mining to handle this uncertainty and predict the specific vulnerability. To highlight this approach, the susceptibility of the Qorveh-Dehgolan plain (QDP) and the Ardabil plain aquifers was investigated. The DRASTIC index was calculated between 63 and 160 for the Ardabil plain and between 39 and 146 for the QDP. Despite some similarities between vulnerability maps and nitrate concentration maps, the results of the DRASTIC model based on nitrate concentration cannot be verified according to Heidke skill score (HSS) and total accuracy (TA) criteria. Then the MFL was developed in two scenarios; the first included all seven parameters, whereas the second used only four parameters of the DRASTIC model. The results showed that, in the first scenario of the MFL modeling, TA and HSS values were respectively 0.75 and 0.51 for the Ardabil plain and 0.45 and 0.33 for the QDP. In addition, according to the TA and HSS values, the proposed model was more reliable and practical in groundwater vulnerability assessment than the traditional method, even using four input data.
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Affiliation(s)
- Vahid Nourani
- Faculty of Civil Engineering, Center of Excellence in Hydroinformatics, University of Tabriz, P.O. Box: 51666, Tabriz, Iran
- Faculty of Civil and Environmental Engineering, Near East University, Near East Boulevard, 99138, via Mersin, Turkey
| | - Sana Maleki
- Faculty of Civil Engineering, Center of Excellence in Hydroinformatics, University of Tabriz, P.O. Box: 51666, Tabriz, Iran
| | - Hessam Najafi
- Faculty of Civil Engineering, Center of Excellence in Hydroinformatics, University of Tabriz, P.O. Box: 51666, Tabriz, Iran.
| | - Aida Hosseini Baghanam
- Faculty of Civil Engineering, Center of Excellence in Hydroinformatics, University of Tabriz, P.O. Box: 51666, Tabriz, Iran
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18
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Petit P, Gondard E, Gandon G, Moreaud O, Sauvée M, Bonneterre V. Agricultural activities and risk of Alzheimer's disease: the TRACTOR project, a nationwide retrospective cohort study. Eur J Epidemiol 2024; 39:271-287. [PMID: 38195954 PMCID: PMC10995077 DOI: 10.1007/s10654-023-01079-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 11/02/2023] [Indexed: 01/11/2024]
Abstract
Data regarding Alzheimer's disease (AD) occurrence in farming populations is lacking. This study aimed to investigate whether, among the entire French farm manager (FM) workforce, certain agricultural activities are more strongly associated with AD than others, using nationwide data from the TRACTOR (Tracking and monitoring occupational risks in agriculture) project. Administrative health insurance data (digital electronic health/medical records and insurance claims) for the entire French agricultural workforce, over the period 2002-2016, on the entire mainland France were used to estimate the risk of AD for 26 agricultural activities with Cox proportional hazards model. For each analysis (one for each activity), the exposed group included all FMs that performed the activity of interest (e.g. crop farming), while the reference group included all FMs who did not carry out the activity of interest (e.g. FMs that never farmed crops between 2002 and 2016). There were 5067 cases among 1,036,069 FMs who worked at least one year between 2002 and 2016. Analyses showed higher risks of AD for crop farming (hazard ratio (HR) = 3.72 [3.47-3.98]), viticulture (HR = 1.29 [1.18-1.42]), and fruit arboriculture (HR = 1.36 [1.15-1.62]). By contrast, lower risks of AD were found for several animal farming types, in particular for poultry and rabbit farming (HR = 0.29 [0.20-0.44]), ovine and caprine farming (HR = 0.50 [0.41-0.61]), mixed dairy and cow farming (HR = 0.46 [0.37-0.57]), dairy farming (HR = 0.67 [0.61-0.73]), and pig farming (HR = 0.30 [0.18-0.52]). This study shed some light on the association between a wide range of agricultural activities and AD in the entire French FMs population.
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Affiliation(s)
- Pascal Petit
- CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, Univ. Grenoble Alpes, 38000, Grenoble, France.
- Centre Régional de Pathologies Professionnelles et Environnementales, CHU Grenoble Alpes, 38000, Grenoble, France.
- AGEIS, Univ. Grenoble Alpes, 38000, Grenoble, France.
| | - Elise Gondard
- CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, Univ. Grenoble Alpes, 38000, Grenoble, France
| | - Gérald Gandon
- Centre Régional de Pathologies Professionnelles et Environnementales, CHU Grenoble Alpes, 38000, Grenoble, France
| | - Olivier Moreaud
- Centre Mémoire de Ressources et de Recherche, CHU Grenoble Alpes, 38000, Grenoble, France
- Laboratoire de Psychologie et Neurocognition, UMR 5105, CNRS, LPNC, Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, 38000, Grenoble, France
| | - Mathilde Sauvée
- Centre Mémoire de Ressources et de Recherche, CHU Grenoble Alpes, 38000, Grenoble, France
- Laboratoire de Psychologie et Neurocognition, UMR 5105, CNRS, LPNC, Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, 38000, Grenoble, France
| | - Vincent Bonneterre
- CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, Univ. Grenoble Alpes, 38000, Grenoble, France
- Centre Régional de Pathologies Professionnelles et Environnementales, CHU Grenoble Alpes, 38000, Grenoble, France
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19
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Chuang YN, Tang R, Jiang X, Hu X. SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization. J Biomed Inform 2024; 151:104606. [PMID: 38325698 DOI: 10.1016/j.jbi.2024.104606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 01/26/2024] [Accepted: 02/04/2024] [Indexed: 02/09/2024]
Abstract
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating instruction prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased performance variance, resulting in significantly distinct summaries even when instruction prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-BasedCalibration (SPeC) pipeline that employs soft prompts to lower variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively regulates variance across different LLMs, providing a more consistent and reliable approach to summarizing critical medical information.
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Affiliation(s)
| | - Ruixiang Tang
- Rice University, Houston, TX, United States of America
| | - Xiaoqian Jiang
- University of Texas Health Science Center, Houston, TX, United States of America
| | - Xia Hu
- Rice University, Houston, TX, United States of America.
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20
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Sahin C. Do popular apps have issues regarding energy efficiency? PeerJ Comput Sci 2024; 10:e1891. [PMID: 38435562 PMCID: PMC10909214 DOI: 10.7717/peerj-cs.1891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/29/2024] [Indexed: 03/05/2024]
Abstract
Mobile apps have become essential components of our daily lives, seamlessly integrating into routines to fulfill communication, productivity, entertainment, and commerce needs, with their diverse range categorized within app stores for easy user navigation and selection. User reviews and ratings play a crucial role in app selection, significantly influencing user decisions through the interplay between feedback and quantified satisfaction. The emphasis on energy efficiency in apps, driven by the limited battery lifespan of mobile devices, impacts app ratings by potentially prompting users to assign low scores, thereby influencing the choices of others. In this study, the presence of energy consumption issues within widely-used popular apps that have high app ratings and user interaction has been investigated through the analysis of user reviews. It is anticipated that popular apps, with high ratings, are less problematic than other apps. User reviews were collected from 32 apps across 16 diverse categories and subsequently filtered based on specific keywords. From the resulting pool of 14,064 user reviews, 8,007 reviews were manually identified as specifically addressing the app's energy consumption. The results of the study demonstrate that all 32 popular apps under consideration exhibit issues related to energy consumption. While the frequency of energy-related issues may vary, it is evident that users are concerned about app energy consumption, as evidenced by the reception of complaint reviews regarding their energy usage. App energy efficiency is important to users, including popular apps with diverse features, necessitating developers to address expectations and optimize for energy efficiency. Improving the energy efficiency of apps has the potential to enhance user satisfaction and, consequently, contribute to the overall success of the app.
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Affiliation(s)
- Cagri Sahin
- Department of Computer Engineering, Gazi University, Ankara, Turkiye
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Zubiaga A, Rosso P. Special issue on analysis and mining of social media data. PeerJ Comput Sci 2024; 10:e1909. [PMID: 38435569 PMCID: PMC10909232 DOI: 10.7717/peerj-cs.1909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 02/01/2024] [Indexed: 03/05/2024]
Abstract
This Editorial introduces the PeerJ Computer Science Special Issue on Analysis and Mining of Social Media Data. The special issue called for submissions with a primary focus on the use of social media data, for a variety of fields including natural language processing, computational social science, data mining, information retrieval and recommender systems. Of the 48 abstract submissions that were deemed within the scope of the special issue and were invited to submit a full article, 17 were ultimately accepted. These included a diverse set of articles covering, inter alia, sentiment analysis, detection and mitigation of online harms, analytical studies focused on societal issues and analysis of images surrounding news. The articles primarily use Twitter, Facebook and Reddit as data sources; English, Arabic, Italian, Russian, Indonesian and Javanese as languages; and over a third of the articles revolve around COVID-19 as the main topic of study. This article discusses the motivation for launching such a special issue and provides an overview of the articles published in the issue.
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Affiliation(s)
- Arkaitz Zubiaga
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | - Paolo Rosso
- Technical University of Valencia, Valencia, Spain
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Zhou Q, Liu J, Xin L, Fang Y, Hu Y, Qi Y, He M, Fang D, Chen X, Cong C. Association between traditional Chinese Medicine and osteoarthritis outcome: A 5-year matched cohort study. Heliyon 2024; 10:e26289. [PMID: 38390046 PMCID: PMC10881435 DOI: 10.1016/j.heliyon.2024.e26289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 02/24/2024] Open
Abstract
Objective The aim of this study was to investigate the relationship between Traditional Chinese medicine (TCM) and pain reduction, hospital readmission, and joint replacement in patients with osteoarthritis (OA). Chinese herbal medicine (CHM) prescription patterns were further analyzed to confirm the association with prognosis and quality of life in OA patients. Methods We retrospectively followed 3,850 hospitalized patients with osteoarthritis between January 2018 and December 2022 using the hospital's HIS system. Propensity score matching (PSM) was used for data matching. Cox's proportional risk model was used to assess the impact of various factors on the outcomes of patients with OA, including pain worsening, readmission, and joint replacement. The Kaplan-Meier survival curve was applied to determine the impact of TCM intervention time on patient outcomes. Data mining methods including association rules, cluster analysis, and random walks have been used to assess the efficacy of TCM. Results The utilization rate of TCM in OA patients was 67.01% (2,511/3,747). After PSM matching, 1,228 TCM non-user patients and 1,228 TCM user patients were eventually included. The outcomes of pain worsening, re-admission rate, and joint replacement rate of the TCM non-user group were observably higher than those of the TCM user group with OA (p < 0.05). Based on the Cox proportional risk model, TCM is an independent protective factor. Compared with non-TCM users, TCM users had 58.4% lower rates of pain, 51.1% lower rates of re-admission, and 42% lower rates of joint replacement. In addition, patients in the high-exposure subgroup (TCM>24 months) had a markedly lower risk of outcome events than those in the low-exposure subgroup (TCM ≤24 months). Data mining methods have shown that TCM therapy can significantly improve immune-inflammatory indices, VAS scores, and SF-36 scale scores in OA patients. Conclusion s TCM acts as a protective factor to improve the prognosis of patients with OA, and the benefits of long-term use of herbal medicines are even greater.
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Affiliation(s)
- Qiao Zhou
- The Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230061, China
- Anhui University of Chinese Medicine, Hefei, Anhui, 230012, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Jian Liu
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Ling Xin
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Yanyan Fang
- The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, 230031, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Yuedi Hu
- Anhui University of Chinese Medicine, Hefei, Anhui, 230012, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Yajun Qi
- Anhui University of Chinese Medicine, Hefei, Anhui, 230012, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Mingyu He
- Anhui University of Chinese Medicine, Hefei, Anhui, 230012, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Dahai Fang
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Xiaolu Chen
- Anhui University of Chinese Medicine, Hefei, Anhui, 230012, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
| | - Chengzhi Cong
- Anhui University of Chinese Medicine, Hefei, Anhui, 230012, China
- Institute of Rheumatism Prevention and Treatment of Traditional Chinese Medicine, Anhui Academy of Chinese Medicine Sciences, Hefei, Anhui, 230031, China
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Cerono G, Chicco D. Ensemble machine learning reveals key features for diabetes duration from electronic health records. PeerJ Comput Sci 2024; 10:e1896. [PMID: 38435625 PMCID: PMC10909161 DOI: 10.7717/peerj-cs.1896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 01/30/2024] [Indexed: 03/05/2024]
Abstract
Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.
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Affiliation(s)
- Gabriel Cerono
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Davide Chicco
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Canada
- Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca, Milan, Italy
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He YJ, Fan YS, Miao FR, Zhao XY, Zhang FZ, He C, Zhang H. Acupoint selection rules of acupuncture and moxibustion in treating neurogenic bladder based on data mining. Zhen Ci Yan Jiu 2024; 49:198-207. [PMID: 38413042 DOI: 10.13702/j.1000-0607.20230018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
OBJECTIVES To explore the rules of acupoint selection in the treatment of neurogenic bladder (NB) with acupuncture and moxibustion by using data mining. METHODS The clinical research literatures on acupuncture treatment of NB were collected from PubMed, Embase, Cochrane Library, CNKI, Wanfang Database, VIP Database and China Biology Medicine from retrieved to January 1, 2023. The acupoint prescription database was established using Excel 2019. SPSS Modeler 18.0 and SPSS Statistics 26.0 softwares were used to conduct the frequency, meri-dians, locations, specific acupoints analysis and association rules analysis, factor analysis, cluster analysis, etc., to explore the characteristics and rules of acupoint selection in acupuncture and moxibustion treatment of NB. RESULTS Totally 313 papers were included, including 110 acupoints with a total frequency of 1 995. The high-frequency acupoints are Zhongji (CV3), Guanyuan (CV4), Sanyinjiao (SP6), etc. The commonly used meridians are the Bladder Meridian of Foot Taiyang and Conception Vessel. The involved acupoints are mostly located in the lumbosacral region and abdomen, and intersection acupoints, mu-front acupoints and back-shu acupoints are the majority in the specific acupoints. The core acupoints group was analyzed, and 17 groups of association rules, 7 factors and 6 effective cluster groups were obtained. CONCLUSIONS Acupuncture and moxibustion treatment of NB follows the therapeutic principles of toni-fying the kidney, invigorating the spleen, and soothing the liver. The core acupoints group is CV3-CV4-SP6.
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Affiliation(s)
- Yu-Jun He
- Faculty of Acupuncture, Moxibustion and Tuina of Guangxi University of Chinese Medicine, Nanning 530001, China
| | - Yu-Shan Fan
- Faculty of Acupuncture, Moxibustion and Tuina of Guangxi University of Chinese Medicine, Nanning 530001, China.
| | - Fu-Rui Miao
- Faculty of Acupuncture, Moxibustion and Tuina of Guangxi University of Chinese Medicine, Nanning 530001, China
| | - Xin-Yi Zhao
- Zhuang Medical College of Guangxi University of Traditional Chinese Medicine, Nanning 530001
| | - Fang-Zhi Zhang
- Faculty of Acupuncture, Moxibustion and Tuina of Guangxi University of Chinese Medicine, Nanning 530001, China
| | - Cai He
- Faculty of Acupuncture, Moxibustion and Tuina of Guangxi University of Chinese Medicine, Nanning 530001, China
| | - Hui Zhang
- Faculty of Acupuncture, Moxibustion and Tuina of Guangxi University of Chinese Medicine, Nanning 530001, China
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Zhang Y, Xu L, Guo C, Li X, Tian Y, Liao L, Dong J. High CD133 expression in proximal tubular cells in diabetic kidney disease: good or bad? J Transl Med 2024; 22:159. [PMID: 38365731 PMCID: PMC10870558 DOI: 10.1186/s12967-024-04950-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 02/03/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Proximal tubular cells (PTCs) play a critical role in the progression of diabetic kidney disease (DKD). As one of important progenitor markers, CD133 was reported to indicate the regeneration of dedifferentiated PTCs in acute kidney disease. However, its role in chronic DKD is unclear. Therefore, we aimed to investigate the expression patterns and elucidate its functional significance of CD133 in DKD. METHODS Data mining was employed to illustrate the expression and molecular function of CD133 in PTCs in human DKD. Subsequently, rat models representing various stages of DKD progression were established. The expression of CD133 was confirmed in DKD rats, as well as in human PTCs (HK-2 cells) and rat PTCs (NRK-52E cells) exposed to high glucose. The immunofluorescence and flow cytometry techniques were utilized to determine the expression patterns of CD133, utilizing proliferative and injury indicators. After overexpression or knockdown of CD133 in HK-2 cells, the cell proliferation and apoptosis were detected by EdU assay, real-time cell analysis and flow analysis. Additionally, the evaluation of epithelial, progenitor cell, and apoptotic indices was performed through western blot and quantitative RT-PCR analyses. RESULTS The expression of CD133 was notably elevated in both human and rat PTCs in DKD, and this expression increased as DKD progressed. CD133 was found to be co-expressed with CD24, KIM-1, SOX9, and PCNA, suggesting that CD133+ cells were damaged and associated with proliferation. In terms of functionality, the knockdown of CD133 resulted in a significant reduction in proliferation and an increase in apoptosis in HK-2 cells compared to the high glucose stimulus group. Conversely, the overexpression of CD133 significantly mitigated high glucose-induced cell apoptosis, but had no impact on cellular proliferation. Furthermore, the Nephroseq database provided additional evidence to support the correlation between CD133 expression and the progression of DKD. Analysis of single-cell RNA-sequencing data revealed that CD133+ PTCs potentially play a role in the advancement of DKD through multiple mechanisms, including heat damage, cell microtubule stabilization, cell growth inhibition and tumor necrosis factor-mediated signaling pathway. CONCLUSION Our study demonstrates that the upregulation of CD133 is linked to cellular proliferation and protects PTC from apoptosis in DKD and high glucose induced PTC injury. We propose that heightened CD133 expression may facilitate cellular self-protective responses during the initial stages of high glucose exposure. However, its sustained increase is associated with the pathological progression of DKD. In conclusion, CD133 exhibits dual roles in the advancement of DKD, necessitating further investigation.
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Affiliation(s)
- Yuhan Zhang
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
- Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong, China
- Shandong Key Laboratory of Endocrinology and Lipid Metabolism, Jinan, 250021, Shandong, China
| | - Lusi Xu
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Congcong Guo
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Xianzhi Li
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Yutian Tian
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China
| | - Lin Liao
- Department of Endocrinology and Metabology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, 250014, China.
| | - Jianjun Dong
- Division of Endocrinology, Department of Internal Medicine, Qilu Hospital of Shandong University, Jinan, 250012, China.
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Tripathi T, Singh DB, Tripathi T. Computational resources and chemoinformatics for translational health research. Adv Protein Chem Struct Biol 2024; 139:27-55. [PMID: 38448138 DOI: 10.1016/bs.apcsb.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.
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Affiliation(s)
- Tripti Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Biochemistry, North-Eastern Hill University, Shillong, India
| | - Dev Bukhsh Singh
- Department of Biotechnology, Siddharth University, Kapilvastu, Siddharth Nagar, India
| | - Timir Tripathi
- Molecular and Structural Biophysics Laboratory, Department of Zoology, North-Eastern Hill University, Shillong, India.
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Petit P, Chamot S, Al-Salameh A, Cancé C, Desailloud R, Bonneterre V. Farming activity and risk of treated thyroid disorders: Insights from the TRACTOR project, a nationwide cohort study. Environ Res 2024; 249:118458. [PMID: 38365059 DOI: 10.1016/j.envres.2024.118458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Epidemiological data regarding thyroid diseases are lacking, in particular for occupationally exposed populations. OBJECTIVES To compare the risk of hypothyroidism and hyperthyroidism between farming activities within the complete population of French farm managers (FMs). METHODS Digital health data from retrospective administrative databases, including insurance claims and electronic health/medical records, was employed. This cohort data spanned the entirety of French farm managers (FMs) who had undertaken work at least once from 2002 to 2016. Survival analysis with the time to initial medication reimbursement as timescale was used to examine the association (hazard ratio, HR) between 26 specific farming activities and both treated hypothyroidism and hyperthyroidism. A distinct model was developed for each farming activity, comparing FMs who had never engaged in the specific farming activity between 2002 and 2016 with those who had. All analyses were adjusted for potential confounders (e.g., age), and sensitivity analyses were conducted. RESULTS Among 1088561 FMs (mean age 46.6 [SD 14.1]; 31% females), there were 31834 hypothyroidism cases (75% females) and 620 hyperthyroidism cases (67% females), respectively. The highest risks were observed for cattle activities for both hyperthyroidism (HR ranging from 1.75 to 2.42) and hypothyroidism (HR ranging from 1.41 to 1.44). For hypothyroidism, higher risks were also observed for several animal farming activities (pig, poultry, and rabbit), as well as fruit arboriculture (HR = 1.22 [1.14-1.31]). The lowest risks were observed for activities involving horses. Sex differences in the risk of hypothyroidism were observed for eight activities, with the risk being higher for males (HR = 1.09 [1.01-1.20]) than females in viticulture (HR = 0.97 [0.93-1.00]). The risk of hyperthyroidism was two times higher for male dairy farmers than females. DISCUSSION Our findings offer a comprehensive overview of thyroid disease risks within the FM community. Thyroid ailments might not stem from a single cause but likely arise from the combined effects of various causal agents and triggering factors (agricultural exposome). Further investigation into distinct farming activities-especially those involving cattle-is essential to pinpoint potential risk factors that could enhance thyroid disease monitoring in agriculture.
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Affiliation(s)
- Pascal Petit
- CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000, Grenoble, France; Univ. Grenoble Alpes, AGEIS, 38000, Grenoble, France.
| | - Sylvain Chamot
- Regional Center for Occupational and Environmental Diseases of Hauts-de-France, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80000, Amiens, France; Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, Chemin du Thil, 80025, Amiens, France
| | - Abdallah Al-Salameh
- Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, Chemin du Thil, 80025, Amiens, France; Department of Endocrinology, Diabetes Mellitus and Nutrition, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054, Amiens, France
| | - Christophe Cancé
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, 38000, Grenoble, France
| | - Rachel Desailloud
- Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, Chemin du Thil, 80025, Amiens, France; Department of Endocrinology, Diabetes Mellitus and Nutrition, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054, Amiens, France
| | - Vincent Bonneterre
- CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000, Grenoble, France; Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, 38000, Grenoble, France
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Abakay O, Kılıç M, Günal H, Kılıç OM. Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region. Environ Monit Assess 2024; 196:264. [PMID: 38351387 DOI: 10.1007/s10661-024-12431-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 02/02/2024] [Indexed: 02/16/2024]
Abstract
Accurate estimation of particle size distribution across a large area is crucial for proper soil management and conservation, ensuring compatibility with capabilities and enabling better selection and adaptation of precision agricultural techniques. The study investigated the performance of tree-based models, ranging from simpler options like CART to sophisticated ones like XGBoost, in predicting soil texture over a wide geographic region. Models were constructed using remotely sensed plant and soil indexes as covariates. Variable selection employed the Boruta approach. Training and testing data for machine learning models consisted of particle size distribution results from 622 surface soil samples collected in southeastern Turkey. The XGBoostClay model emerged as the most accurate predictor, with an R2 value of 0.74. Its superiority was further underlined by a 21.36% relative improvement in XGBoostClay RMSE compared to RFClay and 44.5% compared to CARTClay. Similarly, the R2 values for XGBoostSilt and XGBoostSand models reached 0.71 and 0.75 in predicting sand and silt content, respectively. Among the considered covariates, the normalized ratio vegetation index and slope angle had the highest impact on clay content (21%), followed by topographic position index and simple ratio clay index (20%), while terrain ruggedness index had the least impact (18%). These results highlight the effectiveness of Boruta approach in selecting an adequate number of variables for digital mapping, suggesting its potential as a viable option in this field. Furthermore, the findings of this study suggest that remote sensing data can effectively contribute to digital soil mapping, with tree-based model development leading to improved prediction performance.
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Affiliation(s)
- Osman Abakay
- Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Harran University, Sanliurfa, Turkey
| | - Miraç Kılıç
- Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Malatya Turgut Özal University, Malatya, Turkey.
| | - Hikmet Günal
- Faculty of Agriculture, Department of Soil Science and Plant Nutrition, Harran University, Sanliurfa, Turkey
| | - Orhan Mete Kılıç
- Gaziosmanpasa University, Arts and Science Faculty, Department of Geography, Tokat, Turkey
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Azdaki N, Salmani F, Kazemi T, Partovi N, Bizhaem SK, Moghadam MN, Moniri Y, Zarepur E, Mohammadifard N, Alikhasi H, Nouri F, Sarrafzadegan N, Moezi SA, Khazdair MR. Which risk factor best predicts coronary artery disease using artificial neural network method? BMC Med Inform Decis Mak 2024; 24:52. [PMID: 38355522 PMCID: PMC10868036 DOI: 10.1186/s12911-024-02442-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 01/28/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND Coronary artery disease (CAD) is recognized as the leading cause of death worldwide. This study analyses CAD risk factors using an artificial neural network (ANN) to predict CAD. METHODS The research data were obtained from a multi-center study, namely the Iran-premature coronary artery disease (I-PAD). The current study used the medical records of 415 patients with CAD hospitalized in Razi Hospital, Birjand, Iran, between May 2016 and June 2019. A total of 43 variables that affect CAD were selected, and the relevant data was extracted. Once the data were cleaned and normalized, they were imported into SPSS (V26) for analysis. The present study used the ANN technique. RESULTS The study revealed that 48% of the study population had a history of CAD, including 9.4% with premature CAD and 38.8% with CAD. The variables of age, sex, occupation, smoking, opium use, pesticide exposure, anxiety, sexual activity, and high fasting blood sugar were found to be significantly different among the three groups of CAD, premature CAD, and non-CAD individuals. The neural network achieved success with five hidden fitted layers and an accuracy of 81% in non-CAD diagnosis, 79% in premature diagnosis, and 78% in CAD diagnosis. Anxiety, acceptance, eduction and gender were the four most important factors in the ANN model. CONCLUSIONS The current study shows that anxiety is a high-prevalence risk factor for CAD in the hospitalized population. There is a need to implement measures to increase awareness about the psychological factors that can be managed in individuals at high risk for future CAD.
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Affiliation(s)
- Nahid Azdaki
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
- Clinical Research Development Unit, Razi Hospital, Birjand University of Medical Sciences, Birjand, Iran
| | - Fatemeh Salmani
- Department of Epidemiology and Biostatistics, School of Health, Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Toba Kazemi
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Neda Partovi
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Saeede Khosravi Bizhaem
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Masomeh Noori Moghadam
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Yoones Moniri
- Clinical Research Development Unit, Razi Hospital, Birjand University of Medical Sciences, Birjand, Iran
| | - Ehsan Zarepur
- Interventional Cardiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Noushin Mohammadifard
- Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hassan Alikhasi
- Heart Failure Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Fatemeh Nouri
- Hypertension Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Nizal Sarrafzadegan
- Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Seyyed Ali Moezi
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Mohammad Reza Khazdair
- Cardiovascular Diseases Research Center, Birjand University of Medical Sciences, Birjand, Iran.
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Huang DL, Zeng Q, Xiong Y, Liu S, Pang C, Xia M, Fang T, Ma Y, Qiang C, Zhang Y, Zhang Y, Li H, Yuan Y. A Combined Manual Annotation and Deep-Learning Natural Language Processing Study on Accurate Entity Extraction in Hereditary Disease Related Biomedical Literature. Interdiscip Sci 2024:10.1007/s12539-024-00605-2. [PMID: 38340264 DOI: 10.1007/s12539-024-00605-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 02/12/2024]
Abstract
We report a combined manual annotation and deep-learning natural language processing study to make accurate entity extraction in hereditary disease related biomedical literature. A total of 400 full articles were manually annotated based on published guidelines by experienced genetic interpreters at Beijing Genomics Institute (BGI). The performance of our manual annotations was assessed by comparing our re-annotated results with those publicly available. The overall Jaccard index was calculated to be 0.866 for the four entity types-gene, variant, disease and species. Both a BERT-based large name entity recognition (NER) model and a DistilBERT-based simplified NER model were trained, validated and tested, respectively. Due to the limited manually annotated corpus, Such NER models were fine-tuned with two phases. The F1-scores of BERT-based NER for gene, variant, disease and species are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those of DistilBERT-based NER are 95.14%, 86.26%, 91.37% and 89.92%, respectively. Most importantly, the entity type of variant has been extracted by a large language model for the first time and a comparable F1-score with the state-of-the-art variant extraction model tmVar has been achieved.
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Affiliation(s)
- Dao-Ling Huang
- BGI Research, Shenzhen, 518083, China.
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China.
| | - Quanlei Zeng
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yun Xiong
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Shuixia Liu
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Chaoqun Pang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Menglei Xia
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Ting Fang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yanli Ma
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Cuicui Qiang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yi Zhang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yu Zhang
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Hong Li
- BGI-Wuhan Clinical Laboratories, BGI-Shenzhen, Wuhan, 430074, China
| | - Yuying Yuan
- Clinical Laboratory of BGI Health, BGI-Shenzhen, Shenzhen, 518083, China
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Momahhed SS, Sefiddashti SE, Minaei B, Arab M. The optimal co-insurance rate for outpatient drug expenses of Iranian health insured based on the data mining method. Int J Equity Health 2024; 23:25. [PMID: 38331790 PMCID: PMC10854021 DOI: 10.1186/s12939-023-02065-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 10/19/2023] [Indexed: 02/10/2024] Open
Abstract
OBJECTIVE A more equal allocation of healthcare funds for patients who must pay high costs of care ensures the welfare of society. This study aimed to estimate the optimal co-insurance for outpatient drug costs for health insurance. SETTING The research population includes outpatient prescription claims made by the Health Insurance Organization that outpatient prescriptions in a timely manner in 2016, 2017, 2018, and 2019 were utilized to calculate the optimal co-insurance. The study population was representative of the research sample. DESIGN At the secondary level of care, 11 features of outpatient claims were studied cross-sectionally and retrospectively using data mining. Optimal co-insurance was estimated using Westerhut and Folmer's utility model. PARTICIPANTS One hundred ninety-three thousand five hundred fifty-two individuals were created from 21 776 350 outpatient claims of health insurance. Because of cost-sharing, insured individuals in a low-income subsidy plan and those with refractory diseases were excluded. RESULTS Insureds were divided into three classes of low, middle, and high risk based on IQR and were separated to three clusters using the silhouette coefficient. For the first, second, and third clusters of the low-risk class, the optimal co-insurance estimates are 0.81, 0.76, and 0.84, respectively. It was equal to one for all middle-class clusters and 0.38, 0.45, and 0.42, respectively, for the high-risk class. The insurer's expenses were altered by $3,130,463, $3,451,194, and $ 1,069,859 profit for the first, second, and third clusters, respectively, when the optimal co-insurance strategy is used for the low-risk class. For middle risks, it was US$29,239,815, US$13,863,810, and US$ 14,573,432 while for high risks, US$4,722,099, US$ 6,339,317, and US$19,627,062, respectively. CONCLUSIONS These findings can improve vulnerable populations' access to costly medications, reduce resource waste, and help insurers distribute funds more efficiently.
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Affiliation(s)
- Shekoofeh Sadat Momahhed
- Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Sara Emamgholipour Sefiddashti
- Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Behrouz Minaei
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Maryam Arab
- National Center for Health Insurance Research (Iran Health Insurance Organization), Tehran, Iran
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Li J, Dai Y, Mu Z, Wang Z, Meng J, Meng T, Wang J. Choice of refractive surgery types for myopia assisted by machine learning based on doctors' surgical selection data. BMC Med Inform Decis Mak 2024; 24:41. [PMID: 38331788 PMCID: PMC10854042 DOI: 10.1186/s12911-024-02451-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/02/2024] [Indexed: 02/10/2024] Open
Abstract
In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist's practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.
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Affiliation(s)
- Jiajing Li
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China.
- Wangganzhicha Information Technology Inc., Nanjing, Jiangsu Province, China.
| | - Yuanyuan Dai
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
| | - Zhicheng Mu
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
| | - Zhonghai Wang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Juan Meng
- Community Health Service Center of Douhudi Town, Gongan County, Jingzhou, Hubei Province, China
| | - Tao Meng
- Wangganzhicha Information Technology Inc., Nanjing, Jiangsu Province, China
| | - Jimin Wang
- Department of Information Management, Peking University, Beijing, China
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Karimifard SA, Salehzadeh-Yazdi A, Taghizadeh-Tabarsi R, Akbari-Birgani S. Mechanical effects modulate drug resistance in MCF-7-derived organoids: Insights into the wnt/β-catenin pathway. Biochem Biophys Res Commun 2024; 695:149420. [PMID: 38154263 DOI: 10.1016/j.bbrc.2023.149420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/29/2023] [Accepted: 12/20/2023] [Indexed: 12/30/2023]
Abstract
Addressing drug resistance poses a significant challenge in cancer treatment, as cancer cells develop diverse mechanisms to evade chemotherapy drugs, leading to treatment failure and disease relapse. Three-dimensional (3D) cell culture has emerged as a valuable model for studying drug resistance, although the underlying mechanisms remain elusive. By obtaining a better understanding of drug resistance within the 3D culture environment, we can develop more effective strategies to overcome it and improve the success of cancer treatments. Notably, the physical structure undergoes notable changes in 3D culture, with mechanical effects believed to play a pivotal role in drug resistance. Hence, our study aimed to explore the influence of mechanical effects on drug resistance by analyzing data related to "drug resistance" and "mechanobiology". Through this analysis, we identified β-catenin and JNK1 as potential factors, which were further examined in MCF-7 cells cultivated under both 2D and 3D culture conditions. Our findings demonstrate that β-catenin is activated through canonical and non-canonical pathways and associated with the drug resistance, particularly in organoids obtained under 3D culture.
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Affiliation(s)
- Seyed Ali Karimifard
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | | | - Reza Taghizadeh-Tabarsi
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
| | - Shiva Akbari-Birgani
- Department of Biological Sciences, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran; Research Center for Basic Sciences and Modern Technologies (RBST), Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran.
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Wang Z, Chen Y, Tao Z, Yang M, Li D, Jiang L, Zhang W. Quantifying the Importance of Non-Suicidal Self-Injury Characteristics in Predicting Different Clinical Outcomes: Using Random Forest Model. J Youth Adolesc 2024:10.1007/s10964-023-01926-z. [PMID: 38300442 DOI: 10.1007/s10964-023-01926-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/03/2023] [Indexed: 02/02/2024]
Abstract
Existing research on non-suicidal self-injury (NSSI) among adolescents has primarily concentrated on general risk factors, leaving a significant gap in understanding the specific NSSI characteristics that predict diverse psychopathological outcomes. This study aims to address this gap by using Random Forests to discern the significant predictors of different clinical outcomes. The study tracked 348 adolescents (64.7% girls; mean age = 13.31, SD = 0.91) over 6 months. Initially, 46 characteristics of NSSI were evaluated for their potential to predict the repetition of NSSI, as well as depression, anxiety, and suicidal risks at a follow-up (T2). The findings revealed distinct predictors for each psychopathology. Specifically, psychological pain was identified as a significant predictor for depression, anxiety, and suicidal risks, while the perceived effectiveness of NSSI was crucial in forecasting its repetition. These findings imply that it is feasible to identify high-risk individuals by assessing key NSSI characteristics, and also highlight the importance of considering diverse NSSI characteristics when working with self-injurers.
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Affiliation(s)
- Zhenhai Wang
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Yanrong Chen
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Zhiyuan Tao
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Maomei Yang
- Tangxia No.2 Junior High School, Dongguan, Guangdong, China
| | - Dongjie Li
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Liyun Jiang
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Wei Zhang
- Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China.
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Du J, Wang N, Yu D, He P, Gao Y, Tu Y, Li Y. Data mining-guided alleviation of hyperuricemia by Paeonia veitchii Lynch through inhibition of xanthine oxidase and regulation of renal urate transporters. Phytomedicine 2024; 124:155305. [PMID: 38176275 DOI: 10.1016/j.phymed.2023.155305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 11/30/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Hyperuricemia (HUA) is a metabolic disease characterized by a high level of uric acid (UA). The extensive historical application of traditional Chinese medicine (TCM) offers a range of herbs and prescriptions used for the treatment of HUA-related disorders. However, the core herbs in the prescriptions and their mechanisms have not been sufficiently explained. PURPOSE Our current investigation aimed to estimate the anti-HUA effect and mechanisms of Paeonia veitchii Lynch, an herb with high use frequency identified from data mining of TCM prescriptions. METHODS Prescriptions for HUA/gout treatment were statistically analyzed through a data mining approach to determine the common nature and use frequency of their composition herbs. The chemical constituents of Paeonia veitchii extract (PVE) were analyzed by UPLC-QTOF-MS/MS, while its UA-lowering effect was further evaluated in adenosine-induced liver cells and potassium oxonate (PO) and hypoxanthine (HX)-induced HUA mice. RESULTS A total of 225 prescriptions involving 246 herbs were sorted out. The properties, flavors and meridians of the appearing herbs were mainly cold, bitter and liver, respectively, while their efficacy was primarily concentrated on clearing heat and dispelling wind. Further usage frequency analysis yielded the top 20 most commonly used herbs, in which PVE presented significant inhibitory activity (IC50 = 131.33 µg/ml) against xanthine oxidase (XOD), and its constituents showed strong binding with XOD in a molecular docking study and further were experimentally validated through XOD enzymatic inhibition and surface plasmon resonance (SPR). PVE (50 to 200 μg/ml) dose-dependently decreased UA levels by inhibiting XOD expression and activity in BRL 3A liver cells. In HUA mice, oral administration of PVE exhibited a significant UA-lowering effect, which was attributed to the reduction of UA production by inhibiting XOD activity and expression, as well as the enhancement of UA excretion by regulating renal urate transporters (URAT1, GLUT9, OAT1 and ABCG2). Noticeably, all doses of PVE treatment did not cause any liver injury, and displayed a renal protective effect. CONCLUSIONS Our results first comprehensively clarified the therapeutic effect and mechanisms of PVE against HUA through suppressing UA production and promoting UA excretion with hepatic and renal protection, suggesting that PVE could be a promising UA-lowering candidate with a desirable safety profile for the treatment of HUA and prevention of gout.
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Affiliation(s)
- Jiana Du
- School of Chemical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Na Wang
- School of Chemical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Dehong Yu
- School of Chemical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Pei He
- School of Chemical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yu Gao
- School of Chemical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Yanbei Tu
- School of Pharmacy, Jiangsu University, Zhenjiang, Jiangsu 212013, China.
| | - Yanfang Li
- School of Chemical Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
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Fa H, Shuai B, Yang Z, Niu Y, Huang W. Mining the accident causes of railway dangerous goods transportation: A Logistics-DT-TFP based approach. Accid Anal Prev 2024; 195:107421. [PMID: 38061291 DOI: 10.1016/j.aap.2023.107421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/13/2023] [Accepted: 12/02/2023] [Indexed: 12/30/2023]
Abstract
Accurately and quickly mining the hidden information in railway dangerous goods transportation (RDGT) accident reports has great significance for its safety management. In this paper, a data mining method Logistics-DT-TFP is proposed for analysing the causes of RDGT accidents. Firstly, analyse the transportation process, extract the cause of the accident, and classify the severity of the accident. Then, using ordered multi-classification Logistic regression for correlation calculation, qualitatively judge and quantitatively analyse the relationship between each cause and the severity of the accident. The feature tags of the Decision Tree (DT) are screened, the C5.0 algorithm is used to obtain the accident coupling rules. Next, the FP-Growth algorithm is used to mine frequent itemsets, and TOP-K is used to improve it and output effective association rules with the degree of lift as the indicator, which avoids repeated traversal of the database, shortens the time complexity, and reduces the impact of the minimum support setting on the calculation results. The degree of lift among the causes in the coupling chain is calculated as a complement to the extraction of coupling rules. Finally, based on the analysis and mining results of case study, the management strategies for railway dangerous goods are proposed.
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Affiliation(s)
- Huiyan Fa
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Bin Shuai
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National United Engineering Laboratory of Intergrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Zhenlong Yang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Yifan Niu
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China
| | - Wencheng Huang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National United Engineering Laboratory of Intergrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu Sichuan, 611756 China; National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University, Chengdu Sichuan, 611756 China.
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Takhttavous A, Saberi-Karimian M, Hafezi SG, Esmaily H, Hosseini M, Ferns GA, Amirfakhrian E, Ghamsary M, Ghayour-Mobarhan M, Alinezhad-Namaghi M. Predicting the 10-year incidence of dyslipidemia based on novel anthropometric indices, using data mining. Lipids Health Dis 2024; 23:33. [PMID: 38297277 PMCID: PMC10829243 DOI: 10.1186/s12944-024-02006-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/04/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The aim was to establish a 10-year dyslipidemia incidence model, investigating novel anthropometric indices using exploratory regression and data mining. METHODS This data mining study was conducted on people who were diagnosed with dyslipidemia in phase 2 (n = 1097) of the Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study, who were compared with healthy people in this phase (n = 679). The association of dyslipidemia with several novel anthropometric indices including Conicity Index (C-Index), Body Roundness Index (BRI), Visceral Adiposity Index (VAI), Lipid Accumulation Product (LAP), Abdominal Volume Index (AVI), Weight-Adjusted-Waist Index (WWI), A Body Shape Index (ABSI), Body Mass Index (BMI), Body Adiposity Index (BAI) and Body Surface Area (BSA) was evaluated. Logistic Regression (LR) and Decision Tree (DT) analysis were utilized to evaluate the association. The accuracy, sensitivity, and specificity of DT were assessed through the performance of a Receiver Operating Characteristic (ROC) curve using R software. RESULTS A total of 1776 subjects without dyslipidemia during phase 1 were followed up in phase 2 and enrolled into the current study. The AUC of models A and B were 0.69 and 0.63 among subjects with dyslipidemia, respectively. VAI has been identified as a significant predictor of dyslipidemias (OR: 2.81, (95% CI: 2.07, 3.81)) in all models. Moreover, the DT showed that VAI followed by BMI and LAP were the most critical variables in predicting dyslipidemia incidence. CONCLUSIONS Based on the results, model A had an acceptable performance for predicting 10 years of dyslipidemia incidence. Furthermore, the VAI, BMI, and LAP were the principal anthropometric factors for predicting dyslipidemia incidence by LR and DT models.
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Affiliation(s)
- Alireza Takhttavous
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Maryam Saberi-Karimian
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
- Endoscopic and Minimally Invasive Surgery Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Somayeh Ghiasi Hafezi
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Habibollah Esmaily
- Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Marzieh Hosseini
- School of Public Health, Department of Epidemiology and Biostatistics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK
| | - Elham Amirfakhrian
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mark Ghamsary
- School of Public Health, Department of Epidemiology and Biostatistics, Loma Linda University, Loma Linda, USA.
| | - Majid Ghayour-Mobarhan
- Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
- International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Maryam Alinezhad-Namaghi
- Transplant Research Center, Clinical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
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Yang R, Liu Q, Wang D, Zhao Z, Su Z, Fan D, Liu Q. The Toll-like Receptor-2/4 Antagonist, Sparstolonin B, and Inflammatory Diseases: A Literature Mining and Network Analysis. Cardiovasc Drugs Ther 2024:10.1007/s10557-023-07535-z. [PMID: 38270691 DOI: 10.1007/s10557-023-07535-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND Sparstolonin B (SsnB) is characterized as a new toll-like receptor (TLR)-2/4 antagonist. However, the effects of SsnB on different inflammatory diseases have not been systemically reviewed. METHODS We investigated the effects of SsnB on inflammatory diseases with data mining and network analysis of literature, including frequency description, cluster analysis, association rule mining, functional enrichment, and protein-protein interaction (PPI) mining. RESULTS A total of 27 experimental reports were included. The ARRIVE 2.0 guidelines were used to evaluate the quality of animal studies. Frequency analysis revealed 13 different diseases (cardio-cerebrovascular system diseases account for 23.53%), 12 pharmacological effects (anti-inflammatory effect accounts for 53.85%), and 67 therapeutic targets. The overview of investigation sequence of SsnB studies was depicted by Sankey diagram. Cluster analysis classified the therapeutic targets for SsnB into four main categories: (1) NF-κB; (2) IL-1β, IL-6, and TNF-α; (3) TLR2, TLR4, and MyD88; (4) the other targets. Moreover, the Apriori association discovered two main association pairs: (1) TNF-α, IL-1β, and IL-6 and (2) TLR2, TLR4, and MyD88 (support range 33.33-50%, confidence range 83.33-88.89%). Functional enrichment of the therapeutic targets for SsnB showed that the top enriched items in the biological process were mainly the response to lipopolysaccharide (LPS)/bacterial origin and regulation of cytokine production. Finally, the PPI network and hub gene selection by maximal clique centrality (MCC) algorithm indicated the top ranked proteins were TNF-α, IL-1β, IL-6, AKT1, PPAR-γ, TLR4, CCL2, and TLR2. CONCLUSION These results emphasized the importance of TLR2/TLR4-MyD88-NF-κB-IL-1β/IL-6/TNF-α pathways as therapeutic targets of SsnB in inflammatory diseases.
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Affiliation(s)
- Rongyuan Yang
- State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Clinical School of Medicine, Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China
| | - Qingqing Liu
- State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Clinical School of Medicine, Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China
| | - Dawei Wang
- The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangdong, 510405, China
| | - Zhen Zhao
- State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Clinical School of Medicine, Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China
| | - Zhaohai Su
- Ganzhou Hospital of Guangdong Provincial People's Hospital, Ganzhou Municipal Hospital, Ganzhou, 341000, China
| | - Daping Fan
- Department of Cell Biology and Anatomy, University of South Carolina School of Medicine, Columbia, SC, 29209, USA.
| | - Qing Liu
- State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Clinical School of Medicine, Guangzhou University of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
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Taoussi O, Gameli PS, Berardinelli D, Busardò FP, Tini A, Carlier J. In silico and in vitro human metabolism of IOX2, a performance-enhancing doping agent. J Pharm Biomed Anal 2024; 238:115759. [PMID: 37866082 DOI: 10.1016/j.jpba.2023.115759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/15/2023] [Accepted: 09/30/2023] [Indexed: 10/24/2023]
Abstract
IOX2 is a potent inhibitor of prolyl hydroxylase 2, a key enzyme in the regulation of hypoxia-inducible factor (HIF) and oxygen homeostasis. As such, it can be used to enhance athletic performance and is currently banned by the World Anti-Doping Agency (WADA). Detection of metabolites is critical to demonstrate drug use in doping. However, there is currently little data on IOX2 human metabolism. Our aim was to identify relevant biomarkers of IOX2 use in humans. For this purpose, IOX2 was incubated with 10-donor-pooled human hepatocytes for 3 h, incubates were analyzed by liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS), and LC-HRMS/MS data were screened with Compound Discoverer (Thermo Scientific) for a comprehensive identification of IOX2 metabolites. Additionally, IOX2 human metabolites were predicted with GLORYx open-access software (University of Hamburg, Germany) to assist in the LC-HRMS/MS analysis and data mining. Thirteen metabolites were identified, oxidation at the quinolinyl group, O-glucuronidation, and combinations being predominant biotransformations. The results were consistent with previous animal studies and a single case of oral microdose administration. We suggest hydroxyquinolinyl-IOX2 as major biomarker of IOX2 use in biological samples, glucuronide hydrolysis being critical to increase IOX2 and hydroxyquinolinyl-IOX2 detectability in urine.
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Affiliation(s)
- Omayema Taoussi
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Via Tronto, 10/a, Ancona, AN 60126, Italy
| | - Prince Sellase Gameli
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Via Tronto, 10/a, Ancona, AN 60126, Italy
| | - Diletta Berardinelli
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Via Tronto, 10/a, Ancona, AN 60126, Italy
| | - Francesco Paolo Busardò
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Via Tronto, 10/a, Ancona, AN 60126, Italy
| | - Anastasio Tini
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Via Tronto, 10/a, Ancona, AN 60126, Italy.
| | - Jeremy Carlier
- Department of Biomedical Sciences and Public Health, Marche Polytechnic University, Via Tronto, 10/a, Ancona, AN 60126, Italy
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Zhao Y, Kong X, Zheng W, Ahmad S. Emotion generation method in online physical education teaching based on data mining of teacher-student interactions. PeerJ Comput Sci 2024; 10:e1814. [PMID: 38259880 PMCID: PMC10803077 DOI: 10.7717/peerj-cs.1814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 12/19/2023] [Indexed: 01/24/2024]
Abstract
Different from conventional educational paradigms, online education lacks the direct interplay between instructors and learners, particularly in the sphere of virtual physical education. Regrettably, extant research seldom directs its focus toward the intricacies of emotional arousal within the teacher-student course dynamic. The formulation of an emotion generation model exhibits constraints necessitating refinement tailored to distinct educational cohorts, disciplines, and instructional contexts. This study proffers an emotion generation model rooted in data mining of teacher-student course interactions to refine emotional discourse and enhance learning outcomes in the realm of online physical education. This model includes techniques for data preprocessing and augmentation, a multimodal dialogue text emotion recognition model, and a topic-expanding emotional dialogue generation model based on joint decoding. The encoder assimilates the input sentence into a fixed-length vector, culminating in the final state, wherein the vector produced by the context recurrent neural network is conjoined with the preceding word's vector and employed as the decoder's input. Leveraging the long-short-term memory neural network facilitates the modeling of emotional fluctuations across multiple rounds of dialogue, thus fulfilling the mandate of emotion prediction. The evaluation of the model against the DailyDialog dataset demonstrates its superiority over the conventional end-to-end model in terms of loss and confusion values. Achieving an accuracy rate of 84.4%, the model substantiates that embedding emotional cues within dialogues augments response generation. The proposed emotion generation model augments emotional discourse and learning efficacy within online physical education, offering fresh avenues for refining and advancing emotion generation models.
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Affiliation(s)
| | | | - Wei Zheng
- Langfang 16th Middle School, LangFang, China
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Poppe R, van der Zee S, Taylor PJ, Anderson RJ, Veltkamp RC. Mining Bodily Cues to Deception. J Nonverbal Behav 2024; 48:137-159. [PMID: 38566623 PMCID: PMC10982095 DOI: 10.1007/s10919-023-00450-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/04/2023] [Indexed: 04/04/2024]
Abstract
A significant body of research has investigated potential correlates of deception and bodily behavior. The vast majority of these studies consider discrete, subjectively coded bodily movements such as specific hand or head gestures. Such studies fail to consider quantitative aspects of body movement such as the precise movement direction, magnitude and timing. In this paper, we employ an innovative data mining approach to systematically study bodily correlates of deception. We re-analyze motion capture data from a previously published deception study, and experiment with different data coding options. We report how deception detection rates are affected by variables such as body part, the coding of the pose and movement, the length of the observation, and the amount of measurement noise. Our results demonstrate the feasibility of a data mining approach, with detection rates above 65%, significantly outperforming human judgement (52.80%). Owing to the systematic analysis, our analyses allow for an understanding of the importance of various coding factor. Moreover, we can reconcile seemingly discrepant findings in previous research. Our approach highlights the merits of data-driven research to support the validation and development of deception theory.
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Affiliation(s)
- Ronald Poppe
- Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
| | - Sophie van der Zee
- Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Erasmus School of Law, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Paul J. Taylor
- Psychology, Lancaster University, Lancaster, UK
- Psychology, University of Twente, Enschede, The Netherlands
| | - Ross J. Anderson
- Computer Laboratory, University of Cambridge, Cambridge, UK
- Security Engineering, School of Informatics Institute for Computing Systems Architecture, University of Edinburgh, Edinburgh, UK
| | - Remco C. Veltkamp
- Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
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Sánchez M, Urquiza L. Improving fraud detection with semi-supervised topic modeling and keyword integration. PeerJ Comput Sci 2024; 10:e1733. [PMID: 38259882 PMCID: PMC10803081 DOI: 10.7717/peerj-cs.1733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/13/2023] [Indexed: 01/24/2024]
Abstract
Fraud detection through auditors' manual review of accounting and financial records has traditionally relied on human experience and intuition. However, replicating this task using technological tools has represented a challenge for information security researchers. Natural language processing techniques, such as topic modeling, have been explored to extract information and categorize large sets of documents. Topic modeling, such as latent Dirichlet allocation (LDA) or non-negative matrix factorization (NMF), has recently gained popularity for discovering thematic structures in text collections. However, unsupervised topic modeling may not always produce the best results for specific tasks, such as fraud detection. Therefore, in the present work, we propose to use semi-supervised topic modeling, which allows the incorporation of specific knowledge of the study domain through the use of keywords to learn latent topics related to fraud. By leveraging relevant keywords, our proposed approach aims to identify patterns related to the vertices of the fraud triangle theory, providing more consistent and interpretable results for fraud detection. The model's performance was evaluated by training with several datasets and testing it with another one that did not intervene in its training. The results showed efficient performance averages with a 7% increase in performance compared to a previous job. Overall, the study emphasizes the importance of deepening the analysis of fraud behaviors and proposing strategies to identify them proactively.
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Affiliation(s)
- Marco Sánchez
- Departamento de Informática y Ciencias de la Computación, Escuela Politécnica Nacional, Quito, Pichincha, Ecuador
| | - Luis Urquiza
- Departamento de Electrónica, Telecomunicaciones y Redes de Información, Escuela Politécnica Nacional, Quito, Pichincha, Ecuador
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Yan M, Kang W, Liu X, Yang B, Sun N, Yang Y, Wang W. Prognostic value of plasma microRNAs for non-small cell lung cancer based on data mining models. BMC Cancer 2024; 24:52. [PMID: 38200421 PMCID: PMC10777550 DOI: 10.1186/s12885-024-11830-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/03/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND As biomarkers, microRNAs (miRNAs) are closely associated with the occurrence, progression, and prognosis of non-small cell lung cancer (NSCLC). However, the prognostic predictive value of miRNAs in NSCLC has rarely been explored. In this study, the value in prognosis prediction of NSCLC was mined based on data mining models using clinical data and plasma miRNAs biomarkers. METHODS A total of 69 patients were included in this prospective cohort study. After informed consent, they filled out questionnaires and had their peripheral blood collected. The expressions of plasma miRNAs were examined by quantitative polymerase chain reaction (qPCR). The Whitney U test was used to analyze non-normally distributed data. Kaplan-Meier was used to plot the survival curve, the log-rank test was used to compare with the overall survival curve, and the Cox proportional hazards model was used to screen the factors related to the prognosis of lung cancer. Data mining techniques were utilized to predict the prognostic status of patients. RESULTS We identified that smoking (HR = 2.406, 95% CI = 1.256-4.611), clinical stage III + IV (HR = 5.389, 95% CI = 2.290-12.684), the high expression group of miR-20a (HR = 4.420, 95% CI = 1.760-11.100), the high expression group of miR-197 (HR = 3.828, 95% CI = 1.778-8.245), the low expression group of miR-145 ( HR = 0.286, 95% CI = 0.116-0.709), and the low expression group of miR-30a (HR = 0.307, 95% CI = 0.133-0.706) was associated with worse prognosis. Among the five data mining models, the decision trees (DT) C5.0 model performs the best, with accuracy and Area Under Curve (AUC) of 93.75% and 0.929 (0.685, 0.997), respectively. CONCLUSION The results showed that the high expression level of miR-20a and miR-197, the low expression level of miR-145 and miR-30a were strongly associated with poorer prognosis in NSCLC patients, and the DT C5.0 model may serve as a novel, accurate, method for predicting prognosis of NSCLC.
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Affiliation(s)
- Mengqing Yan
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China
- The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou University, Zhengzhou, China
| | - Wenjun Kang
- Zhuji People's Hospital of Zhejiang Province, Shaoxing, China
| | - Xiaohua Liu
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China
- The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou University, Zhengzhou, China
| | - Bin Yang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China
- The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou University, Zhengzhou, China
| | - Na Sun
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, China.
| | - Wei Wang
- Department of Occupational and Environmental Health, College of Public Health, Zhengzhou University, Zhengzhou, China.
- The Key Laboratory of Nanomedicine and Health Inspection of Zhengzhou, Zhengzhou University, Zhengzhou, China.
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Zheng X, Pan F, Naumovski N, Wei Y, Wu L, Peng W, Wang K. Precise prediction of metabolites patterns using machine learning approaches in distinguishing honey and sugar diets fed to mice. Food Chem 2024; 430:136915. [PMID: 37515908 DOI: 10.1016/j.foodchem.2023.136915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/31/2023]
Abstract
As a natural sweetener produced by honey bees, honey was recognized as being healthier for consumption than table sugar. Our previous study also indicated thatmetaboliteprofiles in mice fed honey and mixedsugardiets aredifferent. However, it is still noteworthy about the batch-to-batch consistency of the metabolic differences between two diet types. Here, the machine learning (ML) algorithms were applied to complement and calibrate HPLC-QTOF/MS-based untargeted metabolomics data. Data were generated from three batches of mice that had the same treatment, which can further mine the metabolite biomarkers. Random Forest and Extra-Trees models could better discriminate between honey and mixed sugar dietary patterns under five-fold cross-validation. Finally, SHapley Additive exPlanations tool identified phosphatidylethanolamine and phosphatidylcholine as reliable metabolic biomarkers to discriminate the honey diet from the mixed sugar diet. This study provides us new ideas for metabolomic analysis of larger data sets.
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Affiliation(s)
- Xing Zheng
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Fei Pan
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Nenad Naumovski
- University of Canberra Health Research Institute (UCHRI), University of Canberra, Locked Bag 1, Bruce, Canberra, ACT 2601, Australia
| | - Yue Wei
- College of Science & Technology, Hebei Agricultural University, Huanghua, Hebei 061100, China
| | - Liming Wu
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
| | - Wenjun Peng
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China.
| | - Kai Wang
- State Key Laboratory of Resource Insects, Institute of Apiculture Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China.
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Valerio F, Twort VG, Duplouy A. Screening Host Genomic Data for Wolbachia Infections. Methods Mol Biol 2024; 2739:251-274. [PMID: 38006557 DOI: 10.1007/978-1-0716-3553-7_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Less than a decade ago, the production of Wolbachia genomic assemblies was tedious, time-consuming, and expensive. The production of Wolbachia genomic DNA free of contamination from host DNA, as required for Wolbachia-targeted sequencing, was then only possible after the amplification and extraction of a large amount of clonal Wolbachia DNA. However, as an endosymbiotic bacterium, Wolbachia does not grow outside the host cell environment, and large-scale recovery of the bacteria required mass rearing of their host, preferably clones of a single individual to avoid strain genetic diversity, or amplification of cell cultures infected with a single Wolbachia strain. Bacterial DNA could be separated from host DNA based on genomic size. Nowadays, the production of full Wolbachia genomes does not require the physical isolation of the bacterial strains from their respective hosts, and the bacterium is often sequenced as a by-catch of host genomic projects. Here, we provide a step-by-step protocol to (1) identify whether host genome projects contain reads from associated Wolbachia and (2) isolate/retrieve the Wolbachia reads from the rest of the sequenced material. We hope this simple protocol will support many projects aiming at studying diverse Wolbachia genome assemblies.
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Affiliation(s)
- Federica Valerio
- Insect Symbiosis Ecology and Evolution, Organismal and Evolutionary Biology Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland
- Research Centre for Ecological Changes, University of Helsinki, Helsinki, Finland
| | - Victoria G Twort
- The Finnish Museum of Natural History, Luomus, University of Helsinki, Helsinki, Finland
| | - Anne Duplouy
- Insect Symbiosis Ecology and Evolution, Organismal and Evolutionary Biology Research Program, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
- Research Centre for Ecological Changes, University of Helsinki, Helsinki, Finland.
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46
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Ahmadi M, Ayyoubzadeh SM, Ghorbani-Bidkorpeh F. Toxicity prediction of nanoparticles using machine learning approaches. Toxicology 2024; 501:153697. [PMID: 38056590 DOI: 10.1016/j.tox.2023.153697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/21/2023] [Accepted: 12/01/2023] [Indexed: 12/08/2023]
Abstract
Nanoparticle toxicity analysis is critical for evaluating the safety of nanomaterials due to their potential harm to the biological system. However, traditional experimental methods for evaluating nanoparticle toxicity are expensive and time-consuming. As an alternative approach, machine learning offers a solution for predicting cellular responses to nanoparticles. This study focuses on developing ML models for nanoparticle toxicity prediction. The training dataset used for building these models includes the physicochemical properties of nanoparticles, exposure conditions, and cellular responses of different cell lines. The impact of each parameter on cell death was assessed using the Gini index. Five classifiers, namely Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, and Artificial Neural Network, were employed to predict toxicity. The models' performance was compared based on accuracy, sensitivity, specificity, area under the curve, F measure, K-fold validation, and classification error. The Gini index indicated that cell line, exposure dose, and tissue are the most influential factors in cell death. Among the models tested, Random Forest exhibited the highest performance in the given dataset. Other models demonstrated lower performance compared to Random Forest. Researchers can utilize the Random Forest model to predict nanoparticle toxicity, resulting in cost and time savings for toxicity analysis.
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Affiliation(s)
- Mahnaz Ahmadi
- Medical Nanotechnology and Tissue Engineering Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Seyed Mohammad Ayyoubzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran; Health Information Management Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Fatemeh Ghorbani-Bidkorpeh
- Department of Pharmaceutics and Pharmaceutical Nanotechnology, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Hong M, Zhao YD, Zhong TL, Lu M, Sun WH, Chen TY, Hong N, Zhu Y, Yu DH. Out-of-set association analysis of lung cancer drugs and symptoms based on clinical case data mining. Technol Health Care 2024; 32:849-859. [PMID: 37545275 DOI: 10.3233/thc-230269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
BACKGROUND There are 1.8 million lung cancer deaths worldwide, accounting for 18% of global cancer deaths, including 710,000 in China, accounting for 23.8% of all cancer deaths in China. OBJECTIVE To explore the out-of-set association rules of lung cancer symptoms and drugs through text mining of traditional Chinese medicine (TCM) treatment of lung cancer, and form medical case analysis to analyze the experience of TCM syndrome differentiation in its treatment. METHODS The medical records of all patients diagnosed with lung cancer in Nanjing Chest Hospital from January to December 2018 were collected, and the out-of-set association analysis was performed using the MedCase v5.2 TCM clinical scientific research auxiliary platform based on the frequent pattern growth enhanced association analysis algorithm. RESULTS In terms of TCM treatment of lung cancer, the clinical symptoms with high correlation included cough, expectoration, chest distress, and white phlegm; and the drugs with high correlation included Pinellia ternata, licorice root, white Atractylodes rhizome, and Radix Ophiopogonis; with the prescriptions based on Erchen and Maimendong decoctions. CONCLUSION This analytical study of the medical cases of TCM treatment for lung cancer was performed using data mining techniques, and the out-of-set association rules between clinical symptoms and drugs were analyzed, including the understanding of lung cancer in TCM. Moreover, the essence of experience in drug use was gathered, providing significant scientific guidance for the clinical treatment of lung cancer.
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Affiliation(s)
- Mei Hong
- Department of Radiation Oncology, Nanjing Chest Hospital, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Department of Radiation Oncology, Nanjing Chest Hospital, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Yi-Dong Zhao
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
- Department of Radiation Oncology, Nanjing Chest Hospital, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Tao-Li Zhong
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Ming Lu
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
- Nanjing Medical Data Mining Center, Nanjing, Jiangsu, China
| | - Wen-Hao Sun
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Tian-Yuan Chen
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Nan Hong
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Yao Zhu
- Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
| | - Da-Hai Yu
- Department of Radiotherapy, Jiangsu Province Hospital of Traditional Chinese Medicine, Affiliated Hospital of Nanjing University of Traditional Chinese Medicine, Nanjing, Jiangsu, China
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Tran Van Canh L, Aubourg S. Bioinformatics Methods for Prediction of Gene Families Encoding Extracellular Peptides. Methods Mol Biol 2024; 2731:3-21. [PMID: 38019422 DOI: 10.1007/978-1-0716-3511-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Genes encoding small secreted peptides are widely distributed among plant genomes but their detection and annotation remains challenging. The bioinformatics protocol described here aims to identify as exhaustively as possible secreted peptide precursors belonging to a family of interest. First, homology searches are performed at the protein and genome levels. Next, multiple sequence alignments and predictions of a secretion signal are used to define a set of homologous proteins sharing features of secreted peptide precursors. These protein sequences are then used as input of motif detection and profile-based tools to build representative matrices and profiles that are used iteratively as guides to scan again the proteome and genome until family completion.
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Affiliation(s)
- Loup Tran Van Canh
- Institut Agro, INRAE, IRHS, SFR QUASAV, Université d'Angers, Angers, France
| | - Sébastien Aubourg
- Institut Agro, INRAE, IRHS, SFR QUASAV, Université d'Angers, Angers, France
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Muñoz-Osorio GA, Tırınk C, Tyasi TL, Ramirez-Bautista MA, Cruz-Tamayo AA, Dzib-Cauich DA, Garcia-Herrera RA, Chay-Canul AJ. Using fat thickness and longissimus thoracis traits real-time ultrasound measurements in Black Belly ewe lambs to predict carcass tissue composition through multiresponse multivariate adaptive regression splines algorithm. Meat Sci 2024; 207:109369. [PMID: 37857028 DOI: 10.1016/j.meatsci.2023.109369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 10/02/2023] [Accepted: 10/13/2023] [Indexed: 10/21/2023]
Abstract
The main idea of the current study was to estimate carcass tissue composition using fat thickness and longissimus thoracis (LT) traits real-time ultrasound measurements (USM) in Black Belly ewe lambs through multiresponse multivariate adaptive regression splines (MARS) algorithms. Twenty-four hours before slaughter, subcutaneous (SFT) and kidney-fat thickness (KFT), LT depth (LTD), width (LTA, cm) and area (LTMA) were measured in 60 lambs (BW of 26.40 ± 7.01 kg). Information on carcass and non-carcass components was recorded after slaughter. The total carcass muscle (TCM), total carcass bone (TCB), and total carcass fat (TCF) had a low to high correlation (P < 0.01) with BW, cold carcass weight (CCW), and LTD, SFT, KFT, and LDMA. The CCW (%65.58) and SFT (%16.70) were the most effective variables, whilst LTD (%9.57) and LTMA (%8.15) were the lowest variables for determining TCB, TCM, and TCF. The multiresponse MARS algorithm provides an accurate and efficient means of estimating TCF, TCB, and TCM.
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Affiliation(s)
- Germani Adrián Muñoz-Osorio
- División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr, Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, Mexico
| | - Cem Tırınk
- Igdir University, Faculty of Agriculture, Department of Animal Science, Igdir TR76000, Türkiye
| | - Thobela Louis Tyasi
- Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X1106, Sovenga 0727, South Africa
| | | | - Alvar Alonzo Cruz-Tamayo
- Facultad de Ciencias Agropecuarias, Universidad Autónoma de Campeche, Escárcega, Campeche, Mexico
| | - Dany Alejandro Dzib-Cauich
- Tecnológico Nacional de México, Instituto Tecnológico Superior de Calkiní, Av. Ah-Canul, Calkiní C.P. 24900, Campeche, Mexico
| | - Ricardo A Garcia-Herrera
- División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr, Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, Mexico
| | - Alfonso J Chay-Canul
- División Académica de Ciencias Agropecuarias, Universidad Juárez Autónoma de Tabasco, Carr, Villahermosa-Teapa, km 25, Villahermosa CP 86280, Tabasco, Mexico.
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Piadeh F, Offie I, Behzadian K, Bywater A, Campos LC. Real-time operation of municipal anaerobic digestion using an ensemble data mining framework. Bioresour Technol 2024; 392:130017. [PMID: 37967795 DOI: 10.1016/j.biortech.2023.130017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/05/2023] [Accepted: 11/10/2023] [Indexed: 11/17/2023]
Abstract
This study presents a novel approach for real-time operation of anaerobic digestion using an ensemble decision-making framework composed of weak learner data mining models. The framework utilises simple but practical features such as waste composition, added water and feeding volume to predict biogas yield and to generate an optimised weekly operation pattern to maximise biogas production and minimise operational costs. The effectiveness of this framework is validated through a real-world case study conducted in the UK. Comparative analysis with benchmark models demonstrates a significant improvement in prediction accuracy, increasing from the range of 50-80% with benchmark models to 91% with the proposed framework. The results also show the efficacy of the weekly operation pattern, which leads to a substantial 78% increase in biogas generation during the testing period. Moreover, the pattern contributes to a reduction of 71% in total days required for feeding and 30% in total days required for pre-feeding.
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Affiliation(s)
- Farzad Piadeh
- School of Computing and Engineering, University of West London, London W5 5RF, United Kingdom; School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom
| | - Ikechukwu Offie
- School of Computing and Engineering, University of West London, London W5 5RF, United Kingdom
| | - Kourosh Behzadian
- School of Computing and Engineering, University of West London, London W5 5RF, United Kingdom; Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E6BT, United Kingdom.
| | - Angela Bywater
- Water and Environmental Engineering Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, SO17 1BJ, UK
| | - Luiza C Campos
- Centre for Urban Sustainability and Resilience, Department of Civil, Environmental and Geomatic Engineering, University College London, London WC1E6BT, United Kingdom
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