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Xu M, He R, Cui G, Wei J, Li X, Xie Y, Shi P. Quantitative tracing the sources and human risk assessment of complex soil pollution in an industrial park. ENVIRONMENTAL RESEARCH 2024; 257:119185. [PMID: 38810828 DOI: 10.1016/j.envres.2024.119185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/30/2024] [Accepted: 05/18/2024] [Indexed: 05/31/2024]
Abstract
Pollution in industrial parks has long been characterized by complex pollution sources and difficulties in identifying pollutant origins. This study focuses on a typical industrial park consisting of 11 factories (F1-F11) including organic pigment, inorganic pigment, and chemical factories in Hunan Province, China, here, a total of 327 sample points were surveyed. Eight pollutants (Mn, Cd, As, Co, NH3-N, l, 1,2-Trichloroethane, chlorobenzene, and petroleum hydrocarbons) were classified as contaminants of concern (COCs). This study assessed the contributions of driving factors to the distribution of COCs in the soil. Pollutant source apportionment was conducted using positive matrix factorization (PMF) and random forest (RF). The results revealed that the main factors driving pollution are groundwater migration, non-compliant emissions, leaks during production, and interactions among pollutants. The primary pollution sources were four chemical factories and an inorganic pigment factory. Source 5 demonstrates significant correlations with TCA (29.6%), CB (30%), and As (31.6%). Two chemical factories (F7 and F10) are the most significant pollution source with a risk assessment contribution rate of more than 60%. The present study sheds some light on the contamination characteristics, source apportionment and source-health risk assessment of COCs in industrial park. By utilizing the proposed research framework, decision-makers can effectively prioritize and address identified pollution sources.
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Affiliation(s)
- Minke Xu
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Ruicheng He
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China
| | - Guannan Cui
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China
| | - Jinjin Wei
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China; School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China
| | - Xin Li
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China; Department of Environment, College of Environment and Resources, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Yunfeng Xie
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China.
| | - Peili Shi
- Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing, 100012, China.
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Zhao R, Wang G, Li F, Wang J, Zhang Y, Li D, Liu S, Li J, Song J, Wei F, Wang C. Developing Machine Learning-Based Predictive Models for Hallux Valgus Recurrence Based on Measurements From Radiographs. Foot Ankle Int 2024:10711007241256648. [PMID: 38872342 DOI: 10.1177/10711007241256648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
BACKGROUND Machine learning (ML) is increasingly used to predict the prognosis of numerous diseases. This retrospective analysis aimed to develop a prediction model using ML algorithms and to identify predictors associated with the recurrence of hallux valgus (HV) following surgery. METHODS A total of 198 symptomatic feet that underwent chevron osteotomy combined with a distal soft tissue procedure were enrolled and analyzed from 2 independent medical centers. The feet were grouped according to nonrecurrence or recurrence based on 1-year follow-up outcomes. Preoperative weightbearing radiographs and immediate postoperative nonweightbearing radiographs were obtained for each HV foot. Radiographic measurements (eg, HV angle and intermetatarsal angle) were acquired and used for ML model training. A total of 9 commonly used ML models were trained on the data obtained from one institute (108 feet), and tested on the other data set from another independent institute (90 feet) for external validation. Optimal feature sets for each model were identified based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The performance of each model was then tested on the external validation set. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were calculated to evaluate the performance of each model. RESULTS The support vector machine (SVM) model showed the highest predictive accuracy compared to other methods, with an AUC of 0.88 and an accuracy of 75.6%. Preoperative hallux valgus angle, tibial sesamoid position, postoperative intermetatarsal angle, and postoperative tibial sesamoid position were identified as the most selected features by several ML models. CONCLUSION ML classifiers such as SVM could predict the recurrence of HV (an HVA >20 degrees) at a 1-year follow-up while identifying associated predictors in a multivariate manner. This study holds the potential for foot and ankle surgeons to effectively identify individuals at higher risk of HV recurrence postsurgery.
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Affiliation(s)
- Rui Zhao
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Guobin Wang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fengtan Li
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jinchan Wang
- Department of Dermatology, Tianjin Medical University General Hospital, Tianjin, China
| | - Yuan Zhang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Dong Li
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Shen Liu
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Jie Li
- Graduate School, Tianjin Medical University, Tianjin, China
| | - Jiajun Song
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
| | - Fangyuan Wei
- Department of Hand and Foot Surgery, Beijing University of Chinese Medicine Third Affiliated Hospital, Beijing, China
- Engineering Research Center of Chinese Orthopaedic and Sports Rehabilitation Artificial Intelligent, Ministry of Education, Beijing, China
| | - Chenguang Wang
- Department of Orthopedic Surgery, Tianjin Medical University General Hospital, Tianjin, China
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Lin J, Liu C, Hu E. Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes. Front Immunol 2024; 15:1381765. [PMID: 38919616 PMCID: PMC11196417 DOI: 10.3389/fimmu.2024.1381765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024] Open
Abstract
Background Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. Methods The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. Results The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. Conclusion In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.
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Affiliation(s)
- Junhan Lin
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, Wenzhou Medical University, Wenzhou, China
- Laboratory of Anesthesiology of Zhejiang Province, Wenzhou Medical University, Wenzhou, China
| | - Changyuan Liu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, Wenzhou Medical University, Wenzhou, China
- Laboratory of Anesthesiology of Zhejiang Province, Wenzhou Medical University, Wenzhou, China
| | - Ende Hu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, Wenzhou Medical University, Wenzhou, China
- Laboratory of Anesthesiology of Zhejiang Province, Wenzhou Medical University, Wenzhou, China
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Yang P, Liu Z, Lu F, Sha Y, Li P, Zheng Q, Wang K, Zhou X, Zeng X, Wu Y. Machine learning models predicts risk of proliferative lupus nephritis. Front Immunol 2024; 15:1413569. [PMID: 38919623 PMCID: PMC11196753 DOI: 10.3389/fimmu.2024.1413569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024] Open
Abstract
Objective This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a reliable diagnostic alternative when renal biopsy is not feasible or safe. Methods This study retrospectively analyzed clinical and laboratory data from patients diagnosed with SLE and renal involvement who underwent renal biopsy at West China Hospital of Sichuan University between 2011 and 2021. We randomly assigned 70% of the patients to a training cohort and the remaining 30% to a test cohort. Various machine learning models were constructed on the training cohort, including generalized linear models (e.g., logistic regression, least absolute shrinkage and selection operator, ridge regression, and elastic net), support vector machines (linear and radial basis kernel functions), and decision tree models (e.g., classical decision tree, conditional inference tree, and random forest). Diagnostic performance was evaluated using ROC curves, calibration curves, and DCA for both cohorts. Furthermore, different machine learning models were compared to identify key and shared features, aiming to screen for potential PLN diagnostic markers. Results Involving 1312 LN patients, with 780 PLN/NPLN cases analyzed. They were randomly divided into a training group (547 cases) and a testing group (233 cases). we developed nine machine learning models in the training group. Seven models demonstrated excellent discriminatory abilities in the testing cohort, random forest model showed the highest discriminatory ability (AUC: 0.880, 95% confidence interval(CI): 0.835-0.926). Logistic regression had the best calibration, while random forest exhibited the greatest clinical net benefit. By comparing features across various models, we confirmed the efficacy of traditional indicators like anti-dsDNA antibodies, complement levels, serum creatinine, and urinary red and white blood cells in predicting and distinguishing PLN. Additionally, we uncovered the potential value of previously controversial or underutilized indicators such as serum chloride, neutrophil percentage, serum cystatin C, hematocrit, urinary pH, blood routine red blood cells, and immunoglobulin M in predicting PLN. Conclusion This study provides a comprehensive perspective on incorporating a broader range of biomarkers for diagnosing and predicting PLN. Additionally, it offers an ideal non-invasive diagnostic tool for SLE patients unable to undergo renal biopsy.
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Affiliation(s)
- Panyu Yang
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Jintang First People’s Hospital, Chengdu, China
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Zhongyu Liu
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Fenjian Lu
- Center for Reproductive Medicine, The Third People’s Hospital of Chengdu, Chengdu, China
| | - Yulin Sha
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Penghao Li
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Qu Zheng
- Department of Laboratory Medicine, Sichuan Jinxin Xinan Women’s and Children’s Hospital , Chengdu, China
- Department of Obstetrics, Chengdu Jinjiang Hospital for Women & Children Health, Chengdu, China
| | - Kefen Wang
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Zhou
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoxi Zeng
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yongkang Wu
- Department of Laboratory Medicine, West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Jintang First People’s Hospital, Chengdu, China
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Ai M, Zhang H, Feng J, Chen H, Liu D, Li C, Yu F, Li C. Research advances in predicting the expansion of hypertensive intracerebral hemorrhage based on CT images: an overview. PeerJ 2024; 12:e17556. [PMID: 38860211 PMCID: PMC11164062 DOI: 10.7717/peerj.17556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/12/2024] Open
Abstract
Hematoma expansion (HE) is an important risk factor for death or poor prognosis in patients with hypertensive intracerebral hemorrhage (HICH). Accurately predicting the risk of HE in patients with HICH is of great clinical significance for timely intervention and improving patient prognosis. Many imaging signs reported in literatures showed the important clinical value for predicting HE. In recent years, the development of radiomics and artificial intelligence has provided new methods for HE prediction with high accuracy. Therefore, this article reviews the latest research progress in CT imaging, radiomics, and artificial intelligence of HE, in order to help identify high-risk patients for HE in clinical practice.
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Affiliation(s)
- Min Ai
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Hanghang Zhang
- Department of Breast and Thyroid Surgery, Chongqing Bishan District Maternal and Child Health Care Hospital, Chongqing, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Hongying Chen
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Di Liu
- Department of Anesthesiology, Nanan District People’s Hospital of Chongqing, Chongqing, China
| | - Chang Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
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Yu WY, Sun TH, Hsu KC, Wang CC, Chien SY, Tsai CH, Yang YW. Comparative analysis of machine learning algorithms for Alzheimer's disease classification using EEG signals and genetic information. Comput Biol Med 2024; 176:108621. [PMID: 38763067 DOI: 10.1016/j.compbiomed.2024.108621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/21/2024]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairments, and behavioral changes. The presence of abnormal beta-amyloid plaques and tau protein tangles in the brain is known to be associated with AD. However, current limitations of imaging technology hinder the direct detection of these substances. Consequently, researchers are exploring alternative approaches, such as indirect assessments involving monitoring brain signals, cognitive decline levels, and blood biomarkers. Recent studies have highlighted the potential of integrating genetic information into these approaches to enhance early detection and diagnosis, offering a more comprehensive understanding of AD pathology beyond the constraints of existing imaging methods. Our study utilized electroencephalography (EEG) signals, genotypes, and polygenic risk scores (PRSs) as features for machine learning models. We compared the performance of gradient boosting (XGB), random forest (RF), and support vector machine (SVM) to determine the optimal model. Statistical analysis revealed significant correlations between EEG signals and clinical manifestations, demonstrating the ability to distinguish the complexity of AD from other diseases by using genetic information. By integrating EEG with genetic data in an SVM model, we achieved exceptional classification performance, with an accuracy of 0.920 and an area under the curve of 0.916. This study presents a novel approach of utilizing real-time EEG data and genetic background information for multimodal machine learning. The experimental results validate the effectiveness of this concept, providing deeper insights into the actual condition of patients with AD and overcoming the limitations associated with single-oriented data.
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Affiliation(s)
- Wei-Yang Yu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Department of Medicine, China Medical University, Taichung, 40402, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan; Neuroscience Laboratory, Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; Neuroscience and Brain Disease Center, College of Medicine, China Medical University, 40402, Taichung, Taiwan
| | - Yu-Wan Yang
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan; School of Medicine, College of Medicine, China Medical University, Taichung, 40402, Taiwan.
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Sheng B, Zhang S, Gao Y, Xia S, Zhu Y, Yan J. Elucidating the influence of familial interactions on geriatric depression: A comprehensive nationwide multi-center investigation leveraging machine learning. Acta Psychol (Amst) 2024; 246:104274. [PMID: 38631151 DOI: 10.1016/j.actpsy.2024.104274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 04/11/2024] [Accepted: 04/12/2024] [Indexed: 04/19/2024] Open
Abstract
OBJECTIVE A plethora of studies have unequivocally established the profound significance of harmonious familial relationships on the psychological well-being of the elderly. In this study, we elucidate the intergenerational relationships, probing the association between frequent interactions or encounters with their children and the incidence of depression in old age. METHODOLOGY We employed a retrospective cross-sectional study design, sourcing our data from the 2018 wave of the China Health and Retirement Longitudinal Study (CHARLS). To identify cases of depression, we utilized the 10-item Center for Epidemiologic Studies Depression Scale (CESD). Employing a five-fold cross-validation methodology, we endeavored to fashion five distinct machine learning models. Subsequently, we crafted learning curves to facilitate the refinement of hyperparameters, assessing model classification performance through metrics such as accuracy and the Area Under the Receiver Operating Characteristic (AUROC) curve. To further elucidate the relationship between variables and geriatric depression, logistic regression was subsequently applied. RESULTS Our findings accentuated that sleep patterns emerged as the paramount determinants influencing the onset of depression in the elderly. Relationships with offspring ranked as the second most significant determinant, only surpassed by sleep habits. A negative correlation was observed between sleep patterns (Odds Ratio [OR]: 0.78, 95 % Confidence Interval [CI]: 0.75-0.81, P < 0.01), communication with offspring (OR: 0.86, 95 % CI: 0.82-0.90, P < 0.01), and the prevalence of depressive symptoms. Among the evaluated models, the k-Near Neighbor algorithm demonstrated commendable discriminative power. However, it was the Random Forest algorithm that manifested unparalleled discriminative prowess and precision, establishing itself as the most efficacious classifier. CONCLUSION Prolonging the duration of nocturnal sleep, and elevating the frequency of communication with offspring have been identified as measures conducive to mitigating the onset of geriatric depression.
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Affiliation(s)
- Boyang Sheng
- Hunan University of Chinese Medicine, Changsha 410208, Hunan Province, China
| | - Shina Zhang
- Hunan University of Chinese Medicine, Changsha 410208, Hunan Province, China
| | - Yuan Gao
- Hunan University of Chinese Medicine, Changsha 410208, Hunan Province, China
| | - Shuaishuai Xia
- Hunan University of Chinese Medicine, Changsha 410208, Hunan Province, China
| | - Yong Zhu
- Hunan University of Chinese Medicine, Changsha 410208, Hunan Province, China
| | - Junfeng Yan
- Hunan University of Chinese Medicine, Changsha 410208, Hunan Province, China.
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Kim PE, Yang H, Kim D, Sunwoo L, Kim CK, Kim BJ, Kim JT, Ryu WS, Kim HS. Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography. Stroke 2024; 55:1609-1618. [PMID: 38787932 PMCID: PMC11122774 DOI: 10.1161/strokeaha.123.045772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/27/2024] [Accepted: 04/11/2024] [Indexed: 05/26/2024]
Abstract
BACKGROUND Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. METHODS We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. RESULTS Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. CONCLUSIONS Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.
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Affiliation(s)
- Pyeong Eun Kim
- Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)
| | - Hyojung Yang
- Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)
- Department of Computer Science and Technology, University of Cambridge, United Kingdom (H.Y.)
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)
| | - Leonard Sunwoo
- Department of Radiology, Seoul National University College of Medicine, Republic of Korea (L.S.)
- Department of Radiology (L.S.), Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Chi Kyung Kim
- Department of Neurology, Korea University Guro Hospital, Seoul, Republic of Korea (C.K.K.)
| | - Beom Joon Kim
- Department of Neurology (B.J.K.), Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Joon-Tae Kim
- Department of Neurology, Chonnam National University Hospital, Gwangju, Republic of Korea (J.-T.K.)
| | - Wi-Sun Ryu
- Artificial Intelligence Research Center, JLK Inc, Seoul, Republic of Korea (P.E.K., H.Y., D.K., W.-S.R.)
| | - Ho Sung Kim
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles (H.S.K.)
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Zhang T, Liu Z, Ma Q, Hu D, Dai Y, Zhang X, Zhou Z. Identification of Dendrobium Using Laser-Induced Breakdown Spectroscopy in Combination with a Multivariate Algorithm Model. Foods 2024; 13:1676. [PMID: 38890910 PMCID: PMC11172223 DOI: 10.3390/foods13111676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Revised: 05/23/2024] [Accepted: 05/25/2024] [Indexed: 06/20/2024] Open
Abstract
Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.
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Affiliation(s)
- Tingsong Zhang
- College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China (Z.L.); (Y.D.)
| | - Ziyuan Liu
- College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China (Z.L.); (Y.D.)
| | - Qing Ma
- College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China (Z.L.); (Y.D.)
| | - Dong Hu
- College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China (Z.L.); (Y.D.)
| | - Yujia Dai
- College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China (Z.L.); (Y.D.)
| | - Xinfeng Zhang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, China
| | - Zhu Zhou
- College of Opto-Electro-Mechanical Engineering, Zhejiang A&F University, Hangzhou 311300, China (Z.L.); (Y.D.)
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Zhu Y, Zhang Y, Yang M, Tang N, Liu L, Wu J, Yang Y. Machine Learning-Based Predictive Modeling of Diabetic Nephropathy in Type 2 Diabetes Using Integrated Biomarkers: A Single-Center Retrospective Study. Diabetes Metab Syndr Obes 2024; 17:1987-1997. [PMID: 38746045 PMCID: PMC11093114 DOI: 10.2147/dmso.s458263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 04/16/2024] [Indexed: 05/16/2024] Open
Abstract
Purpose Diabetic nephropathy (DN), a major complication of diabetes mellitus, significantly impacts global health. Identifying individuals at risk of developing DN is crucial for early intervention and improving patient outcomes. This study aims to develop and validate a machine learning-based predictive model using integrated biomarkers. Methods A cross-sectional analysis was conducted on a baseline dataset involving 2184 participants without DN, categorized based on their development of DN over a follow-up period of 36 months: DN (n=1270) and Non-DN (n=914). Various demographic and clinical parameters were analyzed. The findings were validated using an independent dataset comprising 468 participants, with 273 developing DN and 195 remaining as Non-DN over the follow-up period. Machine learning algorithms, alongside traditional descriptive statistics and logistic regression were used for statistical analyses. Results Elevated levels of serum creatinine, urea, and reduced eGFR, alongside an increased prevalence of retinopathy and peripheral neuropathy, were prominently observed in those who developed DN. Validation on the independent dataset further confirmed the model's robustness and consistency. The SVM model demonstrated superior performance in the training set (AUC=0.79, F1-score=0.74) and testing set (AUC=0.83, F1-score=0.82), outperforming other models. Significant predictors of DN included serum creatinine, eGFR, presence of diabetic retinopathy, and peripheral neuropathy. Conclusion Integrating machine learning algorithms with clinical and biomarker data at baseline offers a promising avenue for identifying individuals at risk of developing diabetic nephropathy in type 2 diabetes patients over a 36-month period.
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Affiliation(s)
- Ying Zhu
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
| | - Yiyi Zhang
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
| | - Miao Yang
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
| | - Nie Tang
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
| | - Limei Liu
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
| | - Jichuan Wu
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
| | - Yan Yang
- Department of Endocrinology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China
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11
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Sun HW, Zhang X, Shen CC. The shared circulating diagnostic biomarkers and molecular mechanisms of systemic lupus erythematosus and inflammatory bowel disease. Front Immunol 2024; 15:1354348. [PMID: 38774864 PMCID: PMC11106441 DOI: 10.3389/fimmu.2024.1354348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/22/2024] [Indexed: 05/24/2024] Open
Abstract
Background Systemic lupus erythematosus (SLE) is a multi-organ chronic autoimmune disease. Inflammatory bowel disease (IBD) is a common chronic inflammatory disease of the gastrointestinal tract. Previous studies have shown that SLE and IBD share common pathogenic pathways and genetic susceptibility, but the specific pathogenic mechanisms remain unclear. Methods The datasets of SLE and IBD were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the Limma package. Weighted gene coexpression network analysis (WGCNA) was used to determine co-expression modules related to SLE and IBD. Pathway enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for co-driver genes. Using the Least AbsoluteShrinkage and Selection Operator (Lasso) regressionand Support Vector Machine-Recursive Feature Elimination (SVM-RFE), common diagnostic markers for both diseases were further evaluated. Then, we utilizedthe CIBERSORT method to assess the abundance of immune cell infiltration. Finally,we used the single-cell analysis to obtain the location of common diagnostic markers. Results 71 common driver genes were identified in the SLE and IBD cohorts based on the DEGs and module genes. KEGG and GO enrichment results showed that these genes were closely associated with positive regulation of programmed cell death and inflammatory responses. By using LASSO regression and SVM, five hub genes (KLRF1, GZMK, KLRB1, CD40LG, and IL-7R) were ultimately determined as common diagnostic markers for SLE and IBD. ROC curve analysis also showed good diagnostic performance. The outcomes of immune cell infiltration demonstrated that SLE and IBD shared almost identical immune infiltration patterns. Furthermore, the majority of the hub genes were commonly expressed in NK cells by single-cell analysis. Conclusion This study demonstrates that SLE and IBD share common diagnostic markers and pathogenic pathways. In addition, SLE and IBD show similar immune cellinfiltration microenvironments which provides newperspectives for future treatment.
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Affiliation(s)
- Hao-Wen Sun
- Department of Gastroenterology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Xin Zhang
- Department of Dermatology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
| | - Cong-Cong Shen
- Department of Dermatology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China
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12
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Xiao H, Tian Y, Gao H, Cui X, Dong S, Xue Q, Yao D. Analysis of the fatigue status of medical security personnel during the closed-loop period using multiple machine learning methods: a case study of the Beijing 2022 Olympic Winter Games. Sci Rep 2024; 14:8987. [PMID: 38637575 PMCID: PMC11026406 DOI: 10.1038/s41598-024-59397-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/10/2024] [Indexed: 04/20/2024] Open
Abstract
Using machine learning methods to analyze the fatigue status of medical security personnel and the factors influencing fatigue (such as BMI, gender, and wearing protective clothing working hours), with the goal of identifying the key factors contributing to fatigue. By validating the predicted outcomes, actionable and practical recommendations can be offered to enhance fatigue status, such as reducing wearing protective clothing working hours. A questionnaire was designed to assess the fatigue status of medical security personnel during the closed-loop period, aiming to capture information on fatigue experienced during work and disease recovery. The collected data was then preprocessed and used to determine the structural parameters for each machine learning algorithm. To evaluate the prediction performance of different models, the mean relative error (MRE) and goodness of fit (R2) between the true and predicted values were calculated. Furthermore, the importance rankings of various parameters in relation to fatigue status were determined using the RF feature importance analysis method. The fatigue status of medical security personnel during the closed-loop period was analyzed using multiple machine learning methods. The prediction performance of these methods was ranked from highest to lowest as follows: Gradient Boosting Regression (GBM) > Random Forest (RF) > Adaptive Boosting (AdaBoost) > K-Nearest Neighbors (KNN) > Support Vector Regression (SVR). Among these algorithms, four out of the five achieved good prediction results, with the GBM method performing the best. The five most critical parameters influencing fatigue status were identified as working hours in protective clothing, a customized symptom and disease score (CSDS), physical exercise, body mass index (BMI), and age, all of which had importance scores exceeding 0.06. Notably, working hours in protective clothing obtained the highest importance score of 0.54, making it the most critical factor impacting fatigue status. Fatigue is a prevalent and pressing issue among medical security personnel operating in closed-loop environments. In our investigation, we observed that the GBM method exhibited superior predictive performance in determining the fatigue status of medical security personnel during the closed-loop period, surpassing other machine learning techniques. Notably, our analysis identified several critical factors influencing the fatigue status of medical security personnel, including the duration of working hours in protective clothing, CSDS, and engagement in physical exercise. These findings shed light on the multifaceted nature of fatigue among healthcare workers and emphasize the importance of considering various contributing factors. To effectively alleviate fatigue, prudent management of working hours for security personnel, along with minimizing the duration of wearing protective clothing, proves to be promising strategies. Furthermore, promoting regular physical exercise among medical security personnel can significantly impact fatigue reduction. Additionally, the exploration of medication interventions and the adoption of innovative protective clothing options present potential avenues for mitigating fatigue. The insights derived from this study offer valuable guidance to management personnel involved in organizing large-scale events, enabling them to make informed decisions and implement targeted interventions to address fatigue among medical security personnel. In our upcoming research, we will further expand the fatigue dataset while considering higher precisionprediction algorithms, such as XGBoost model, ensemble model, etc., and explore their potential contributions to our research.
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Affiliation(s)
- Hao Xiao
- Department of Emergency, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Yingping Tian
- Department of Emergency, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Hengbo Gao
- Department of Emergency, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Xiaolei Cui
- Department of Emergency, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Shimin Dong
- Department of Emergency, The Third Hospital of Hebei Medical University, Shijiazhuang, 050000, China
| | - Qianlong Xue
- Department of Emergency, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, China
| | - Dongqi Yao
- Department of Emergency, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
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13
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Lai QC, Zheng J, Mou J, Cui CY, Wu QC, M Musa Rizvi S, Zhang Y, Li TM, Ren YB, Liu Q, Li Q, Zhang C. Identification of hub genes in calcific aortic valve disease. Comput Biol Med 2024; 172:108214. [PMID: 38508057 DOI: 10.1016/j.compbiomed.2024.108214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/26/2024] [Accepted: 02/25/2024] [Indexed: 03/22/2024]
Abstract
Calcific aortic valve disease (CAVD) is a heart valve disorder characterized primarily by calcification of the aortic valve, resulting in stiffness and dysfunction of the valve. CAVD is prevalent among aging populations and is linked to factors such as hypertension, dyslipidemia, tobacco use, and genetic predisposition, and can result in becoming a growing economic and health burden. Once aortic valve calcification occurs, it will inevitably progress to aortic stenosis. At present, there are no medications available that have demonstrated effectiveness in managing or delaying the progression of the disease. In this study, we mined four publicly available microarray datasets (GSE12644 GSE51472, GSE77287, GSE233819) associated with CAVD from the GEO database with the aim of identifying hub genes associated with the occurrence of CAVD and searching for possible biological targets for the early prevention and diagnosis of CAVD. This study provides preliminary evidence for therapeutic and preventive targets for CAVD and may provide a solid foundation for subsequent biological studies.
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Affiliation(s)
- Qian-Cheng Lai
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Sichuan Provincial People's Hospital, Chengdu, 610000, Sichuan, China
| | - Jie Zheng
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Jian Mou
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China; Department of Pain, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Chun-Yan Cui
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China; Department of Pain, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Qing-Chen Wu
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Syed M Musa Rizvi
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying Zhang
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Tian-Mei Li
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Ying-Bo Ren
- Department of Anesthesiology, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China
| | - Qing Liu
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China; Hejiang Traditional Chinese Medicine Hospital, Luzhou, 646000, Sichuan, China.
| | - Qun Li
- Department of Pain, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China.
| | - Cheng Zhang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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14
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Martínez-Blanco P, Suárez M, Gil-Rojas S, Torres AM, Martínez-García N, Blasco P, Torralba M, Mateo J. Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence. Diagnostics (Basel) 2024; 14:406. [PMID: 38396445 PMCID: PMC10888215 DOI: 10.3390/diagnostics14040406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 01/24/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. METHODS In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. RESULTS Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). CONCLUSIONS The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.
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Affiliation(s)
| | - Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Sergio Gil-Rojas
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | | | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Miguel Torralba
- Internal Medicine Unit, Guadalajara University Hospital, 19002 Guadalajara, Spain (M.T.)
- Faculty of Medicine, Universidad de Alcalá de Henares, 28801 Alcalá de Henares, Spain
- Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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15
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Geubbelmans M, Rousseau AJ, Burzykowski T, Valkenborg D. Artificial neural networks and deep learning. Am J Orthod Dentofacial Orthop 2024; 165:248-251. [PMID: 38302219 DOI: 10.1016/j.ajodo.2023.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 02/03/2024]
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16
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Ke TM, Lophatananon A, Muir KR. An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank. Biomedicines 2023; 11:3206. [PMID: 38137427 PMCID: PMC10740416 DOI: 10.3390/biomedicines11123206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/20/2023] [Accepted: 11/26/2023] [Indexed: 12/24/2023] Open
Abstract
Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.
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Affiliation(s)
| | | | - Kenneth R. Muir
- Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PT, UK; (T.-M.K.); (A.L.)
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