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Wyatt-Johnson SK, Afify R, Brutkiewicz RR. The immune system in neurological diseases: What innate-like T cells have to say. J Allergy Clin Immunol 2024; 153:913-923. [PMID: 38365015 PMCID: PMC10999338 DOI: 10.1016/j.jaci.2024.02.003] [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: 09/18/2023] [Revised: 01/26/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024]
Abstract
The immune system classically consists of 2 lines of defense, innate and adaptive, both of which interact with one another effectively to protect us against any pathogenic threats. Importantly, there is a diverse subset of cells known as innate-like T cells that act as a bridge between the innate and adaptive immune systems and are pivotal players in eliciting inflammatory immune responses. A growing body of evidence has demonstrated the regulatory impact of these innate-like T cells in central nervous system (CNS) diseases and that such immune cells can traffic into the brain in multiple pathological conditions, which can be typically attributed to the breakdown of the blood-brain barrier. However, until now, it has been poorly understood whether innate-like T cells have direct protective or causative properties, particularly in CNS diseases. Therefore, in this review, our attention is focused on discussing the critical roles of 3 unique subsets of unconventional T cells, namely, natural killer T cells, γδ T cells, and mucosal-associated invariant T cells, in the context of CNS diseases, disorders, and injuries and how the interplay of these immune cells modulates CNS pathology, in an attempt to gain a better understanding of their complex functions.
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Affiliation(s)
- Season K Wyatt-Johnson
- Department of Microbiology and Immunology, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Ind
| | - Reham Afify
- Department of Microbiology and Immunology, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Ind
| | - Randy R Brutkiewicz
- Department of Microbiology and Immunology, Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Ind.
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2
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Cabrera-León Y, Báez PG, Fernández-López P, Suárez-Araujo CP. Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review. J Alzheimers Dis 2024; 98:793-823. [PMID: 38489188 PMCID: PMC11091566 DOI: 10.3233/jad-231271] [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] [Accepted: 02/03/2024] [Indexed: 03/17/2024]
Abstract
Background The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too. Background Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.
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Affiliation(s)
- Ylermi Cabrera-León
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Patricio García Báez
- Departamento de Ingeniería Informática y de Sistemas, Escuela Superior de Ingeniería y Tecnología, Universidad de La Laguna, San Cristóbal de La Laguna, Canary Islands, Spain
| | - Pablo Fernández-López
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
| | - Carmen Paz Suárez-Araujo
- Instituto Universitario de Cibernética, Empresa y Sociedad, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Canary Islands, Spain
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3
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Afsar A, Chen M, Xuan Z, Zhang L. A glance through the effects of CD4 + T cells, CD8 + T cells, and cytokines on Alzheimer's disease. Comput Struct Biotechnol J 2023; 21:5662-5675. [PMID: 38053545 PMCID: PMC10694609 DOI: 10.1016/j.csbj.2023.10.058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/31/2023] [Accepted: 10/31/2023] [Indexed: 12/07/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia. Unfortunately, despite numerous studies, an effective treatment for AD has not yet been established. There is remarkable evidence indicating that the innate immune mechanism and adaptive immune response play significant roles in the pathogenesis of AD. Several studies have reported changes in CD8+ and CD4+ T cells in AD patients. This mini-review article discusses the potential contribution of CD4+ and CD8+ T cells reactivity to amyloid β (Aβ) protein in individuals with AD. Moreover, this mini-review examines the potential associations between T cells, heme oxygenase (HO), and impaired mitochondria in the context of AD. While current mathematical models of AD have not extensively addressed the inclusion of CD4+ and CD8+ T cells, there exist models that can be extended to consider AD as an autoimmune disease involving these T cell types. Additionally, the mini-review covers recent research that has investigated the utilization of machine learning models, considering the impact of CD4+ and CD8+ T cells.
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Affiliation(s)
- Atefeh Afsar
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Min Chen
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Zhenyu Xuan
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
| | - Li Zhang
- Department of Biological Sciences, University of Texas at Dallas, Richardson, TX, USA
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4
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Blanco K, Salcidua S, Orellana P, Sauma-Pérez T, León T, Steinmetz LCL, Ibañez A, Duran-Aniotz C, de la Cruz R. Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease. Alzheimers Res Ther 2023; 15:176. [PMID: 37838690 PMCID: PMC10576366 DOI: 10.1186/s13195-023-01304-8] [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: 06/02/2023] [Accepted: 09/15/2023] [Indexed: 10/16/2023]
Abstract
Mild cognitive impairment (MCI) is often considered an early stage of dementia, with estimated rates of progression to dementia up to 80-90% after approximately 6 years from the initial diagnosis. Diagnosis of cognitive impairment in dementia is typically based on clinical evaluation, neuropsychological assessments, cerebrospinal fluid (CSF) biomarkers, and neuroimaging. The main goal of diagnosing MCI is to determine its cause, particularly whether it is due to Alzheimer's disease (AD). However, only a limited percentage of the population has access to etiological confirmation, which has led to the emergence of peripheral fluid biomarkers as a diagnostic tool for dementias, including MCI due to AD. Recent advances in biofluid assays have enabled the use of sophisticated statistical models and multimodal machine learning (ML) algorithms for the diagnosis of MCI based on fluid biomarkers from CSF, peripheral blood, and saliva, among others. This approach has shown promise for identifying specific causes of MCI, including AD. After a PRISMA analysis, 29 articles revealed a trend towards using multimodal algorithms that incorporate additional biomarkers such as neuroimaging, neuropsychological tests, and genetic information. Particularly, neuroimaging is commonly used in conjunction with fluid biomarkers for both cross-sectional and longitudinal studies. Our systematic review suggests that cost-effective longitudinal multimodal monitoring data, representative of diverse cultural populations and utilizing white-box ML algorithms, could be a valuable contribution to the development of diagnostic models for AD due to MCI. Clinical assessment and biomarkers, together with ML techniques, could prove pivotal in improving diagnostic tools for MCI due to AD.
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Affiliation(s)
- Kevin Blanco
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
| | - Stefanny Salcidua
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile
| | - Paulina Orellana
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tania Sauma-Pérez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Tomás León
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Memory and Neuropsychiatric Center (CMYN) Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile
| | - Lorena Cecilia López Steinmetz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Technische Universität Berlin, Berlin, Deutschland
- Instituto de Investigaciones Psicológicas (IIPsi), Universidad Nacional de Córdoba (UNC) y Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Córdoba, Argentina
| | - Agustín Ibañez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, Trinity College, Dublin, Ireland
- Global Brain Health Institute, University of California San Francisco (UCSF), San Francisco, CA, USA
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, & National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Claudia Duran-Aniotz
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibanez, Diagonal Las Torres 2640, Peñalolén, Santiago, Chile.
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
| | - Rolando de la Cruz
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile.
- Faculty of Engineering and Sciences, Universidad Adolfo Ibáñez, Diagonal Las Torres 2700, Building D, Peñalolén, Santiago, Chile.
- Data Observatory Foundation, ANID Technology Center No. DO210001, Santiago, Chile.
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Jin J, Guang M, Li S, Liu Y, Zhang L, Zhang B, Cheng M, Schmalz G, Huang X. Immune-related signature of periodontitis and Alzheimer's disease linkage. Front Genet 2023; 14:1230245. [PMID: 37849501 PMCID: PMC10577303 DOI: 10.3389/fgene.2023.1230245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/22/2023] [Indexed: 10/19/2023] Open
Abstract
Background: Periodontits (PD) and Alzheimer's disease (AD) are both associated with ageing and clinical studies increasingly evidence their association. However, specific mechanisms underlying this association remain undeciphered, and immune-related processes are purported to play a signifcant role. The accrual of publicly available transcriptomic datasets permits secondary analysis and the application of data-mining and bioinformatic tools for biological discovery. Aim: The present study aimed to leverage publicly available transcriptomic datasets and databases, and apply a series of bioinformatic analysis to identify a robust signature of immune-related signature of PD and AD linkage. Methods: We downloaded gene-expresssion data pertaining PD and AD and identified crosstalk genes. We constructed a protein-protein network analysis, applied immune cell enrichment analysis, and predicted crosstalk immune-related genes and infiltrating immune cells. Next, we applied consisent cluster analysis and performed immune cell bias analysis, followed by LASSO regression to select biomarker immune-related genes. Results: The results showed a 3 gene set comprising of DUSP14, F13A1 and SELE as a robust immune-related signature. Macrophages M2 and NKT, B-cells, CD4+ memory T-cells and CD8+ naive T-cells emerged as key immune cells linking PD with AD. Conclusion: Candidate immune-related biomarker genes and immune cells central to the assocation of PD with AD were identified, and merit investigation in experimental and clinical research.
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Affiliation(s)
- Jieqi Jin
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Mengkai Guang
- Department of Stomatology, China-Japan Friendship Hospital, Beijing, China
| | - Simin Li
- Stomatological Hospital, Southern Medical University, Guangzhou, China
| | - Yong Liu
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liwei Zhang
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Bo Zhang
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Menglin Cheng
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Gerhard Schmalz
- Department of Cariology, Endodontology and Periodontology, Leipzig University, Leipzig, Germany
| | - Xiaofeng Huang
- Department of Stomatology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
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Qian XH, Chen SY, Liu XL, Tang HD. ABCA7-Associated Clinical Features and Molecular Mechanisms in Alzheimer's Disease. Mol Neurobiol 2023; 60:5548-5556. [PMID: 37322288 DOI: 10.1007/s12035-023-03414-8] [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: 06/29/2022] [Accepted: 05/31/2023] [Indexed: 06/17/2023]
Abstract
Alzheimer's disease (AD) is the most common type of neurodegenerative disease and its pathogenesis is still unclear. Genetic factors are thought to account for a large proportion of the overall AD phenotypes. ATP-binding cassette transporter A7 (ABCA7) is one of the most important risk gene for AD. Multiple forms of ABCA7 variants significantly increase the risk of AD, such as single-nucleotide polymorphisms, premature termination codon variants, missense variants, variable number tandem repeat, mutations, and alternative splicing. AD patients with ABCA7 variants usually exhibit typical clinical and pathological features of traditional AD with a wide age of onset range. ABCA7 variants can alter ABCA7 protein expression levels and protein structure to affect protein functions such as abnormal lipid metabolism, amyloid precursor protein (APP) processing, and immune cell function. Specifically, ABCA7 deficiency can cause neuronal apoptosis by inducing endoplasmic reticulum stress through the PERK/eIF2α pathway. Second, ABCA7 deficiency can increase Aβ production by upregulating the SREBP2/BACE1 pathway and promoting APP endocytosis. In addition, the ability of microglia to phagocytose and degrade Aβ is destroyed by ABCA7 deficiency, leading to reduced clearance of Aβ. Finally, disturbance of lipid metabolism may also be an important method by which ABCA7 variants influence the incidence rate of AD. In the future, more attention should be given to different ABCA7 variants and ABCA7 targeted therapies for AD.
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Affiliation(s)
- Xiao-Hang Qian
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Si-Yue Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiao-Li Liu
- Department of Neurology, Shanghai University of Medicine and Health Sciences Affiliated Sixth People's Hospital South Campus, Shanghai, China.
| | - Hui-Dong Tang
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Medical Center on Aging of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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7
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Almeida JS, Casanova JM, Santos-Rosa M, Tarazona R, Solana R, Rodrigues-Santos P. Natural Killer T-like Cells: Immunobiology and Role in Disease. Int J Mol Sci 2023; 24:ijms24032743. [PMID: 36769064 PMCID: PMC9917533 DOI: 10.3390/ijms24032743] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
CD56+ T cells are generally recognized as a distinct population of T cells and are categorized as NKT-like cells. Although our understanding of NKT-like cells is far from satisfactory, it has been shown that aging and a number of disease situations have impacted these cells. To construct an overview of what is currently known, we reviewed the literature on human NKT-like cells. NKT-like cells are highly differentiated T cells with "CD1d-independent" antigen recognition and MHC-unrestricted cell killing. The genesis of NKT-like cells is unclear; however, it is proposed that the acquisition of innate characteristics by T cells could represent a remodeling process leading to successful aging. Additionally, it has been shown that NKT-like cells may play a significant role in several pathological conditions, making it necessary to comprehend whether these cells might function as prognostic markers. The quantification and characterization of these cells might serve as a cutting-edge indicator of individual immune health. Additionally, exploring the mechanisms that can control their killing activity in different contexts may therefore result in innovative therapeutic alternatives in a wide range of disease settings.
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Affiliation(s)
- Jani-Sofia Almeida
- Institute of Immunology, Faculty of Medicine, University of Coimbra (FMUC), 3004-504 Coimbra, Portugal
- Laboratory of Immunology and Oncology, Center for Neuroscience and Cell Biology (CNC), University of Coimbra, 3004-504 Coimbra, Portugal
- Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
- Center for Innovation in Biomedicine and Biotechnology (CIBB), University of Coimbra, 3004-504 Coimbra, Portugal
- Clinical Academic Centre of Coimbra (CACC), 3000-075 Coimbra, Portugal
| | - José Manuel Casanova
- Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
- Center for Innovation in Biomedicine and Biotechnology (CIBB), University of Coimbra, 3004-504 Coimbra, Portugal
- Clinical Academic Centre of Coimbra (CACC), 3000-075 Coimbra, Portugal
- University Clinic of Orthopedics, Orthopedics Service, Tumor Unit of the Locomotor Apparatus (UTAL), Coimbra Hospital and Universitary Center (CHUC), 3000-075 Coimbra, Portugal
| | - Manuel Santos-Rosa
- Institute of Immunology, Faculty of Medicine, University of Coimbra (FMUC), 3004-504 Coimbra, Portugal
- Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
- Center for Innovation in Biomedicine and Biotechnology (CIBB), University of Coimbra, 3004-504 Coimbra, Portugal
- Clinical Academic Centre of Coimbra (CACC), 3000-075 Coimbra, Portugal
| | - Raquel Tarazona
- Immunology Unit, Department of Physiology, University of Extremadura, 10003 Cáceres, Spain
| | - Rafael Solana
- Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofía University Hospital, 14004 Córdoba, Spain
- Immunology Unit, Department of Cell Biology, Physiology and Immunology, University of Córdoba, 14071 Córdoba, Spain
| | - Paulo Rodrigues-Santos
- Institute of Immunology, Faculty of Medicine, University of Coimbra (FMUC), 3004-504 Coimbra, Portugal
- Laboratory of Immunology and Oncology, Center for Neuroscience and Cell Biology (CNC), University of Coimbra, 3004-504 Coimbra, Portugal
- Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
- Coimbra Institute for Clinical and Biomedical Research (iCBR), Faculty of Medicine, University of Coimbra, 3000-548 Coimbra, Portugal
- Center for Innovation in Biomedicine and Biotechnology (CIBB), University of Coimbra, 3004-504 Coimbra, Portugal
- Clinical Academic Centre of Coimbra (CACC), 3000-075 Coimbra, Portugal
- Correspondence:
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Sh Y, Dong J, Chen Z, Yuan M, Lyu L, Zhang X. Active regression model for clinical grading of COVID-19. Front Immunol 2023; 14:1141996. [PMID: 37026015 PMCID: PMC10071017 DOI: 10.3389/fimmu.2023.1141996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/13/2023] [Indexed: 04/08/2023] Open
Abstract
Background In the therapeutic process of COVID-19, the majority of indicators that physicians have for assisting treatment have come from clinical tests represented by proteins, metabolites, and immune levels in patients' blood. Therefore, this study constructs an individualized treatment model based on deep learning methods, aiming to realize timely intervention based on clinical test indicator data of COVID-19 patients and provide an important theoretical basis for optimizing medical resource allocation. Methods This study collected clinical data from a total of 1,799 individuals, including 560 controls for non-respiratory infectious diseases (Negative), 681 controls for other respiratory virus infections (Other), and 558 coronavirus infections (Positive) for COVID-19. We first used the Student T-test to screen for statistically significant differences (Pvalue<0.05); we then used the Adaptive-Lasso method stepwise regression to screen the characteristic variables and filter the features with low importance; we then used analysis of covariance to calculate the correlation between variables and filter the highly correlated features; and finally, we analyzed the feature contribution and screened the best combination of features. Results Feature engineering reduced the feature set to 13 feature combinations. The correlation coefficient between the projected results of the artificial intelligence-based individualized diagnostic model and the fitted curve of the actual values in the test group was 0.9449 which could be applied to the clinical prognosis of COVID-19. In addition, the depletion of platelets in patients with COVID-19 is an important factor affecting their severe deterioration. With the progression of COVID-19, there is a slight decrease in the total number of platelets in the patient's body, particularly as the volume of larger platelets sharply decreases. The importance of plateletCV (count*mean platelet volume) in evaluating the severity of COVID-19 patients is higher than the count of platelets and mean platelet volume. Conclusion In general, we found that for patients with COVID-19, the increase in mean platelet volume was a predictor for SARS-Cov-2. The rapid decrease of platelet volume and the decrease of total platelet volume are dangerous signals for the aggravation of SARS-Cov-2 infection. The analysis and modeling results of this study provide a new perspective for individualized accurate diagnosis and treatment of clinical COVID-19 patients.
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Affiliation(s)
- Yuan Sh
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, Fujian, China
- The Chinese Academy of Sciences (CAS) Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, The Chinese Academy of Sciences (CAS) Key Laboratory of Standardization and Measurement for Nanotechnology, The Chinese Academy of Sciences (CAS) Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
| | - Jierong Dong
- The Chinese Academy of Sciences (CAS) Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, The Chinese Academy of Sciences (CAS) Key Laboratory of Standardization and Measurement for Nanotechnology, The Chinese Academy of Sciences (CAS) Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
| | - Zhongqing Chen
- The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Meiqing Yuan
- Key Laboratory of Forensic Genetics, Institute of Forensic Sciences, Ministry of Public Security, Beijing, China
- *Correspondence: Xiuli Zhang, ; Lingna Lyu, ; Meiqing Yuan,
| | - Lingna Lyu
- Department of Gastroenterology and Hepatology, Beijing You’an Hospital, Capital Medical University, Beijing, China
- *Correspondence: Xiuli Zhang, ; Lingna Lyu, ; Meiqing Yuan,
| | - Xiuli Zhang
- The Chinese Academy of Sciences (CAS) Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, The Chinese Academy of Sciences (CAS) Key Laboratory of Standardization and Measurement for Nanotechnology, The Chinese Academy of Sciences (CAS) Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
- *Correspondence: Xiuli Zhang, ; Lingna Lyu, ; Meiqing Yuan,
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9
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Jing R, Yin XL, Xie XL, Lian HQ, Li J, Zhang G, Yang WH, Sun TS, Xu YC. Morphologic identification of clinically encountered moulds using a residual neural network. Front Microbiol 2022; 13:1021236. [PMID: 36312928 PMCID: PMC9614265 DOI: 10.3389/fmicb.2022.1021236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 09/30/2022] [Indexed: 11/04/2022] Open
Abstract
The use of morphology to diagnose invasive mould infections in China still faces substantial challenges, which often leads to delayed diagnosis or misdiagnosis. We developed a model called XMVision Fungus AI to identify mould infections by training, testing, and evaluating a ResNet-50 model. Our research achieved the rapid identification of nine common clinical moulds: Aspergillus fumigatus complex, Aspergillus flavus complex, Aspergillus niger complex, Aspergillus terreus complex, Aspergillus nidulans, Aspergillus sydowii/Aspergillus versicolor, Syncephalastrum racemosum, Fusarium spp., and Penicillium spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively improve the identification performance. The overall identification accuracy of XMVision Fungus AI was up to 93.00% (279/300), which was higher than that of human readers. XMVision Fungus AI shows intrinsic advantages in the identification of clinical moulds and can be applied to improve human identification efficiency through training. Moreover, it has great potential for clinical application because of its convenient operation and lower cost. This system will be suitable for primary hospitals in China and developing countries.
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Affiliation(s)
- Ran Jing
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Graduate School, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Xiang-Long Yin
- Beijing Hao Chen Xing Yue Technology Co., Ltd., Beijing, China
| | - Xiu-Li Xie
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - He-Qing Lian
- Beijing Xiaoying Technology Co., Ltd., Beijing, China
| | - Jin Li
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Ge Zhang
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Wen-Hang Yang
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China
| | - Tian-Shu Sun
- Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China,Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China,*Correspondence: Tian-Shu Sun,
| | - Ying-Chun Xu
- Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Graduate School, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China,Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China,Ying-Chun Xu,
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Alfonso Perez G, Caballero Villarraso J. Neural Network Aided Detection of Huntington Disease. J Clin Med 2022; 11:jcm11082110. [PMID: 35456203 PMCID: PMC9032851 DOI: 10.3390/jcm11082110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 02/06/2023] Open
Abstract
Huntington Disease (HD) is a degenerative neurological disease that causes a significant impact on the quality of life of the patient and eventually death. In this paper we present an approach to create a biomarker using as an input DNA CpG methylation data to identify HD patients. DNA CpG methylation is a well-known epigenetic marker for disease state. Technological advances have made it possible to quickly analyze hundreds of thousands of CpGs. This large amount of information might introduce noise as potentially not all DNA CpG methylation levels will be related to the presence of the illness. In this paper, we were able to reduce the number of CpGs considered from hundreds of thousands to 237 using a non-linear approach. It will be shown that using only these 237 CpGs and non-linear techniques such as artificial neural networks makes it possible to accurately differentiate between control and HD patients. An underlying assumption in this paper is that there are no indications suggesting that the process is linear and therefore non-linear techniques, such as artificial neural networks, are a valid tool to analyze this complex disease. The proposed approach is able to accurately distinguish between control and HD patients using DNA CpG methylation data as an input and non-linear forecasting techniques. It should be noted that the dataset analyzed is relatively small. However, the results seem relatively consistent and the analysis can be repeated with larger data-sets as they become available.
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Affiliation(s)
- Gerardo Alfonso Perez
- Department of Biochemistry and Molecular Biology, University of Cordoba, 14071 Cordoba, Spain;
- Correspondence:
| | - Javier Caballero Villarraso
- Department of Biochemistry and Molecular Biology, University of Cordoba, 14071 Cordoba, Spain;
- Biochemical Laboratory, Reina Sofia University Hospital, 14004 Cordoba, Spain
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