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Zhou J, Zhang W, Cao Z, Lian S, Li J, Nie J, Huang Y, Zhao K, He J, Liu C. Association of Selenium Levels with Neurodegenerative Disease: A Systemic Review and Meta-Analysis. Nutrients 2023; 15:3706. [PMID: 37686737 PMCID: PMC10490073 DOI: 10.3390/nu15173706] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 08/20/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Neurodegenerative diseases (NDs) have posed significant challenges to public health, and it is crucial to understand their mechanisms in order to develop effective therapeutic strategies. Recent studies have highlighted the potential role of selenium in ND pathogenesis, as it plays a vital role in maintaining cellular homeostasis and preventing oxidative damage. However, a comprehensive analysis of the association between selenium and NDs is still lacking. METHOD Five public databases, namely PubMed, Web of Science, EMBASE, Cochrane and Clinical Trials, were searched in our research. Random model effects were chosen, and Higgins inconsistency analyses (I2), Cochrane's Q test and Tau2 were calculated to evaluate the heterogeneity. RESULT The association of selenium in ND patients with Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD) was studied. A statistically significant relationship was only found for AD patients (SMD = -0.41, 95% CI (-0.64, -0.17), p < 0.001), especially for erythrocytes. However, no significant relationship was observed in the analysis of the other four diseases. CONCLUSION Generally, this meta-analysis indicated that AD patients are strongly associated with lower selenium concentrations compared with healthy people, which may provide a clinical reference in the future. However, more studies are urgently needed for further study and treatment of neurodegenerative diseases.
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Affiliation(s)
- Jiaxin Zhou
- International School, Jinan University, Guangzhou 510080, China;
| | - Wenfen Zhang
- School of Basic Medicine and Public Health, Jinan University, Guangzhou 510632, China;
| | - Zhiwen Cao
- Center for Data Science, New York University, New York, NY 10011, USA;
| | - Shaoyan Lian
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou 510632, China; (S.L.); (J.L.); (J.N.); (Y.H.); (K.Z.)
| | - Jieying Li
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou 510632, China; (S.L.); (J.L.); (J.N.); (Y.H.); (K.Z.)
| | - Jiaying Nie
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou 510632, China; (S.L.); (J.L.); (J.N.); (Y.H.); (K.Z.)
| | - Ying Huang
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou 510632, China; (S.L.); (J.L.); (J.N.); (Y.H.); (K.Z.)
| | - Ke Zhao
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou 510632, China; (S.L.); (J.L.); (J.N.); (Y.H.); (K.Z.)
| | - Jiang He
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Chaoqun Liu
- Department of Nutrition, School of Medicine, Jinan University, Guangzhou 510632, China; (S.L.); (J.L.); (J.N.); (Y.H.); (K.Z.)
- Disease Control and Prevention Institute, Jinan University, Guangzhou 510632, China
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Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis. Neurol Sci 2023; 44:499-517. [PMID: 36303065 DOI: 10.1007/s10072-022-06460-7] [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: 08/10/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS. METHODS We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI). RESULTS After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%). CONCLUSION Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.
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Huyut M. Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models. Ing Rech Biomed 2023; 44:100725. [PMID: 35673548 PMCID: PMC9158375 DOI: 10.1016/j.irbm.2022.05.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 04/24/2022] [Accepted: 05/29/2022] [Indexed: 02/07/2023]
Abstract
Objectives When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission. Material and methods The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set. Results In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%. Conclusion The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.
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Aslam N, Khan IU, Bashamakh A, Alghool FA, Aboulnour M, Alsuwayan NM, Alturaif RK, Brahimi S, Aljameel SS, Al Ghamdi K. Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207856. [PMID: 36298206 PMCID: PMC9609137 DOI: 10.3390/s22207856] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/29/2022] [Accepted: 10/11/2022] [Indexed: 05/17/2023]
Abstract
Multiple Sclerosis (MS) is a disease that impacts the central nervous system (CNS), which can lead to brain, spinal cord, and optic nerve problems. A total of 2.8 million are estimated to suffer from MS. Globally, a new case of MS is reported every five minutes. In this review, we discuss the proposed approaches to diagnosing MS using machine learning (ML) published between 2011 and 2022. Numerous models have been developed using different types of data, including magnetic resonance imaging (MRI) and clinical data. We identified the methods that achieved the best results in diagnosing MS. The most implemented approaches are SVM, RF, and CNN. Moreover, we discussed the challenges and opportunities in MS diagnosis to improve AI systems to enable researchers and practitioners to enhance their approaches and improve the automated diagnosis of MS. The challenges faced by automated MS diagnosis include difficulty distinguishing the disease from other diseases showing similar symptoms, protecting the confidentiality of the patients' data, achieving reliable ML models that are also easily understood by non-experts, and the difficulty of collecting a large reliable dataset. Moreover, we discussed several opportunities in the field such as the implementation of secure platforms, employing better AI solutions, developing better disease prognosis systems, combining more than one data type for better MS prediction and using OCT data for diagnosis, utilizing larger, multi-center datasets to improve the reliability of the developed models, and commercialization.
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Affiliation(s)
- Nida Aslam
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
- Correspondence:
| | - Irfan Ullah Khan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Asma Bashamakh
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Fatima A. Alghool
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Menna Aboulnour
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Noorah M. Alsuwayan
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Rawa’a K. Alturaif
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Samiha Brahimi
- Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Sumayh S. Aljameel
- Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
| | - Kholoud Al Ghamdi
- Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
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Torkey H, Belal NA. An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach. Diagnostics (Basel) 2022; 12:diagnostics12071771. [PMID: 35885672 PMCID: PMC9316893 DOI: 10.3390/diagnostics12071771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/25/2022] [Accepted: 07/18/2022] [Indexed: 11/30/2022] Open
Abstract
Multiple Sclerosis (MS) is a disease attacking the central nervous system. According to MS Atlas’s most recent statistics, there are more than 2.8 million people worldwide diagnosed with MS. Recently, studies started to explore machine learning techniques to predict MS using various data. The objective of this paper is to develop an ensemble approach for diagnosis of MS using gene expression profiles, while handling the class imbalance problem associated with the data. A hierarchical ensemble approach employing voting and boosting techniques is proposed. This approach adopts a heterogeneous voting approach using two base learners, random forest and support vector machine. Experiments show that our approach outperforms state-of-the-art methods, with the highest recorded accuracy being 92.81% and 93.5% with BoostFS and DEGs for feature selection, respectively. Conclusively, the proposed approach is able to efficiently diagnose MS using the gene expression profiles that are more relevant to the disease. The approach is not merely an ensemble classifier outperforming previous work; it also identifies differentially expressed genes between normal samples and patients with multiple sclerosis using a genome-wide expression microarray. The results obtained show that the proposed approach is an efficient diagnostic tool for MS.
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Affiliation(s)
- Hanaa Torkey
- Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt;
| | - Nahla A. Belal
- College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Smart Village 12577, Egypt
- Correspondence:
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Pinto MF, Oliveira H, Batista S, Cruz L, Pinto M, Correia I, Martins P, Teixeira C. Prediction of disease progression and outcomes in multiple sclerosis with machine learning. Sci Rep 2020; 10:21038. [PMID: 33273676 PMCID: PMC7713436 DOI: 10.1038/s41598-020-78212-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/01/2020] [Indexed: 12/03/2022] Open
Abstract
Multiple Sclerosis is a chronic inflammatory disease, affecting the Central Nervous System and leading to irreversible neurological damage, such as long term functional impairment and disability. It has no cure and the symptoms vary widely, depending on the affected regions, amount of damage, and the ability to activate compensatory mechanisms, which constitutes a challenge to evaluate and predict its course. Additionally, relapsing-remitting patients can evolve its course into a secondary progressive, characterized by a slow progression of disability independent of relapses. With clinical information from Multiple Sclerosis patients, we developed a machine learning exploration framework concerning this disease evolution, more specifically to obtain three predictions: one on conversion to secondary progressive course and two on disease severity with rapid accumulation of disability, concerning the 6th and 10th years of progression. For the first case, the best results were obtained within two years: AUC=[Formula: see text], sensitivity=[Formula: see text] and specificity=[Formula: see text]; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC=[Formula: see text], sensitivity=[Formula: see text], and specificity=[Formula: see text]. The Expanded Disability Status Scale value, the majority of functional systems, affected functions during relapses, and age at onset were described as the most predictive features. These results demonstrate the possibility of predicting Multiple Sclerosis progression by using machine learning, which may help to understand this disease's dynamics and thus, advise physicians on medication intake.
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Affiliation(s)
- Mauro F Pinto
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal.
| | - Hugo Oliveira
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - Sónia Batista
- Neurology Department of Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Luís Cruz
- Functional Unit of Neuroradiology, Medical Imaging Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Mafalda Pinto
- Functional Unit of Neuroradiology, Medical Imaging Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Inês Correia
- Neurology Department of Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Pedro Martins
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
| | - César Teixeira
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal
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