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Fu Y, Zhang J, Liu Q, Yang L, Wu Q, Yang X, Wang L, Ding N, Xiong J, Gao Y, Ma S, Jiang Y. Unveiling the role of ABI3 and hub senescence-related genes in macrophage senescence for atherosclerotic plaque progression. Inflamm Res 2024; 73:65-82. [PMID: 38062164 PMCID: PMC10776483 DOI: 10.1007/s00011-023-01817-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/15/2023] [Accepted: 11/06/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Atherosclerosis, characterized by abnormal arterial lipid deposition, is an age-dependent inflammatory disease and contributes to elevated morbidity and mortality. Senescent foamy macrophages are considered to be deleterious at all stages of atherosclerosis, while the underlying mechanisms remain largely unknown. In this study, we aimed to explore the senescence-related genes in macrophages diagnosis for atherosclerotic plaque progression. METHODS The atherosclerosis-related datasets were retrieved from the Gene Expression Omnibus (GEO) database, and cellular senescence-associated genes were acquired from the CellAge database. R package Limma was used to screen out the differentially expressed senescence-related genes (DE-SRGs), and then three machine learning algorithms were applied to determine the hub DE-SRGs. Next, we established a nomogram model to further confirm the clinical significance of hub DE-SRGs. Finally, we validated the expression of hub SRG ABI3 by Sc-RNA seq analysis and explored the underlying mechanism of ABI3 in THP-1-derived macrophages and mouse atherosclerotic lesions. RESULTS A total of 15 DE-SRGs were identified in macrophage-rich plaques, with five hub DE-SRGs (ABI3, CAV1, NINJ1, Nox4 and YAP1) were further screened using three machine learning algorithms. Subsequently, a nomogram predictive model confirmed the high validity of the five hub DE-SRGs for evaluating atherosclerotic plaque progression. Further, the ABI3 expression was upregulated in macrophages of advanced plaques and senescent THP-1-derived macrophages, which was consistent with the bioinformatics analysis. ABI3 knockdown abolished macrophage senescence, and the NF-κB signaling pathway contributed to ABI3-mediated macrophage senescence. CONCLUSION We identified five cellular senescence-associated genes for atherogenesis progression and unveiled that ABI3 might promote macrophage senescence via activation of the NF-κB pathway in atherogenesis progression, which proposes new preventive and therapeutic strategies of senolytic agents for atherosclerosis.
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Affiliation(s)
- Yajuan Fu
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
| | - Juan Zhang
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
- Department of Pathophysiology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China
| | - Qiujun Liu
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
- Department of Pathophysiology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China
| | - Lan Yang
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
- Department of Pathophysiology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China
| | - Qianqian Wu
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
- Department of Pathophysiology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China
| | - Xiaomin Yang
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
- Department of Pathophysiology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China
| | - Lexin Wang
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
| | - Ning Ding
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
- Department of Pathophysiology, School of Basic Medical Sciences, Ningxia Medical University, Yinchuan, China
| | - Jiantuan Xiong
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China
| | - Yujing Gao
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China.
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China.
| | - Shengchao Ma
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China.
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China.
- School of Laboratory Medicine, Ningxia Medical University, Yinchuan, China.
| | - Yideng Jiang
- National Health Commission Key Laboratory of Metabolic Cardiovascular Diseases Research, Ningxia Medical University, Yinchuan, China.
- Ningxia Key Laboratory of Vascular Injury and Repair Research, Ningxia Medical University, Yinchuan, China.
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Sharma M, Kirby M, McCormack DG, Parraga G. Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity. Acad Radiol 2023:S1076-6332(23)00658-X. [PMID: 38161089 DOI: 10.1016/j.acra.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 01/03/2024]
Abstract
RATIONALE AND OBJECTIVES Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred). MATERIALS AND METHODS In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. RESULTS Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02). CONCLUSION In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.
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Affiliation(s)
- Maksym Sharma
- Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada (M.S., G.P.); Department of Medical Biophysics, Western University, London, Canada (M.S., G.P.)
| | - Miranda Kirby
- Department of Physics, Toronto Metropolitan University, Toronto, Canada (M.K.)
| | | | - Grace Parraga
- Robarts Research Institute, Western University, 1151 Richmond St N, London, N6A 5B7, Canada (M.S., G.P.); Department of Medical Biophysics, Western University, London, Canada (M.S., G.P.); Division of Respirology, Department of Medicine (D.G.M., G.P.); School of Biomedical Engineering, Western University, London, Canada (G.P.).
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Zhang WY, Chen ZH, An XX, Li H, Zhang HL, Wu SJ, Guo YQ, Zhang K, Zeng CL, Fang XM. Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric sepsis by integrating bioinformatics and machine learning. World J Pediatr 2023; 19:1094-1103. [PMID: 37115484 PMCID: PMC10533616 DOI: 10.1007/s12519-023-00717-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/10/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND Pediatric sepsis is a complicated condition characterized by life-threatening organ failure resulting from a dysregulated host response to infection in children. It is associated with high rates of morbidity and mortality, and rapid detection and administration of antimicrobials have been emphasized. The objective of this study was to evaluate the diagnostic biomarkers of pediatric sepsis and the function of immune cell infiltration in the development of this illness. METHODS Three gene expression datasets were available from the Gene Expression Omnibus collection. First, the differentially expressed genes (DEGs) were found with the use of the R program, and then gene set enrichment analysis was carried out. Subsequently, the DEGs were combined with the major module genes chosen using the weighted gene co-expression network. The hub genes were identified by the use of three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. The receiver operating characteristic curve and nomogram model were used to verify the discrimination and efficacy of the hub genes. In addition, the inflammatory and immune status of pediatric sepsis was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The relationship between the diagnostic markers and infiltrating immune cells was further studied. RESULTS Overall, after overlapping key module genes and DEGs, we detected 402 overlapping genes. As pediatric sepsis diagnostic indicators, CYSTM1 (AUC = 0.988), MMP8 (AUC = 0.973), and CD177 (AUC = 0.986) were investigated and demonstrated statistically significant differences (P < 0.05) and diagnostic efficacy in the validation set. As indicated by the immune cell infiltration analysis, multiple immune cells may be involved in the development of pediatric sepsis. Additionally, all diagnostic characteristics may correlate with immune cells to varying degrees. CONCLUSIONS The candidate hub genes (CD177, CYSTM1, and MMP8) were identified, and the nomogram was constructed for pediatric sepsis diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for pediatric sepsis patients.
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Affiliation(s)
- Wen-Yuan Zhang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Zhong-Hua Chen
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
- Department of Anesthesiology, Shaoxing People's Hospital (Shaoxing Hospital, Zhejiang University School of Medicine), Shaoxing, 312000, China
| | | | - Hui Li
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Hua-Lin Zhang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Shui-Jing Wu
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Yu-Qian Guo
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Kai Zhang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China
| | - Cong-Li Zeng
- Department of Anesthesiology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Xiang-Ming Fang
- Department of Anesthesiology and Intensive Care, School of Medicine, The First Affiliated Hospital, Zhejiang University, QingChun Road 79, Hangzhou, 310003, China.
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Pu T, Ding J, Zhang F, Wang K, Cao N, Hensen EJM, Xie P. Dual Atom Catalysts for Energy and Environmental Applications. Angew Chem Int Ed Engl 2023; 62:e202305964. [PMID: 37277990 DOI: 10.1002/anie.202305964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/07/2023]
Abstract
The pursuit of high metal utilization in heterogeneous catalysis has triggered the burgeoning interest of various atomically dispersed catalysts. Our aim in this review is to assess key recent findings in the synthesis, characterization, structure-property relationship and computational studies of dual-atom catalysts (DACs), which cover the full spectrum of applications in thermocatalysis, electrocatalysis and photocatalysis. In particular, combination of qualitative and quantitative characterization with cooperation with DFT insights, synergies and superiorities of DACs compare to counterparts, high-throughput catalyst exploration and screening with machine-learning algorithms are highlighted. Undoubtably, it would be wise to expect more fascinating developments in the field of DACs as tunable catalysts.
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Affiliation(s)
- Tiancheng Pu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310058, China
| | - Jiaqi Ding
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310058, China
- Laboratory of Inorganic Materials and Catalysis, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Fanxing Zhang
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310058, China
| | - Ke Wang
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310058, China
| | - Ning Cao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310058, China
| | - Emiel J M Hensen
- Laboratory of Inorganic Materials and Catalysis, Department of Chemical Engineering and Chemistry, Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands
| | - Pengfei Xie
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310058, China
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Ying X, Li B. Machine-learning Modeling for Personalized Immunotherapy- An Evaluation Module. Biomed J Sci Tech Res 2022; 47:38211-38216. [PMID: 37817882 PMCID: PMC10563037 DOI: 10.26717/bjstr.2022.47.007462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/12/2023]
Abstract
Immune-cell therapy and targeting therapy are in rapid development to treat tumor diseases. However, current immune-cell therapy and targeting immunotherapy often face three challenges (three Ss): safety challenges such as cytokine releasing syndrome (C.R.S.); specificity targeting problems such as low efficacy caused by off-targeting tumor cells; unsatisfying payment are confounded to clinical patients and physicians. We have been studying immunotherapy for more than thirty years, and recently, personalized immunotherapy to treat tumor disease has been proposed. After we discovered quiescent genes from immune cells within the tumor microenvironment, we set up single-cell genomics analysis, studying heterogeneous immune responses from multiple tumor antigens (neo-antigen); here, we further introduce a new generation of immunotherapy module by using a machine-learning model to assess optimal immunotherapy. The machine-learning model combined with single-cell genomic analysis can predict optimal immune-cell (such as T-cells) and other optimal targeting drugs such as PD1 and CTLA4 inhibitors for the patient to use.
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Affiliation(s)
- Xiaonan Ying
- University of Nebraska Medical Center, Omaha, NE 68131, USA
| | - Biaoru Li
- Georgia Cancer Center and Department of Pediatrics, Medical College at GA, Augusta, GA 30912, USA
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Gonzalez-Martinez A, Pagán J, Sanz-García A, García-Azorín D, Rodriguez Vico JS, Jaimes A, Gómez García A, Díaz de Terán J, González-García N, Quintas S, Belascoaín R, Casas Limón J, Latorre G, Calle de Miguel C, Sierra Á, Guerrero-Peral ÁL, Trevino-Peinado C, Gago-Veiga AB. Machine-learning based approach to predict anti-CGRP response in patients with migraine: multicenter Spanish study. Eur J Neurol 2022; 29:3102-3111. [PMID: 35726393 DOI: 10.1111/ene.15458] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 06/13/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND To date, several variables have been associated with anti-CGRP receptor or ligand-antibody response with disparate results. Our objective is to determine whether machine learning (ML)-based models can predict 6, 9 and 12 months response to anti-CGRP receptor or ligand therapies among migraine patients. METHODS We performed a multicenter analysis of a prospectively collected data cohort of patients with migraine receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rate defined in the 30% to 50% range -or at least 30%-, in the 50% to 75% range -or at least 50%-, and response rate over 75% reduction in the number of headache days per month at 6, 9 and 12 months. A sequential forward feature selector was used for variable selection and ML-based predictive models response to anti-CGRP therapies at 6, 9 and 12 months, with models' accuracy not less than 70%, were generated. RESULTS A total of 712 patients were included, 93% women, aged 48 years (SD=11.7). Eighty-three percent had chronic migraine. ML models using headache days/month, migraine days/month and HIT-6 variables yielded predictions with a F1 score range of 0.70-0.97 and AUC (area under the receiver operating curve) score range of 0.87-0.98. SHAP (SHapley Additive exPlanations) summary plots and dependence plots were generated to evaluate the relevance of the factors associated with the prediction of the above-mentioned response rates. CONCLUSIONS According to our study, ML models can predict anti-CGRP response at 6, 9 and 12 months. This study provides a predictive tool to be used in a real-world setting.
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Affiliation(s)
- Alicia Gonzalez-Martinez
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - Josué Pagán
- Universidad Politécnica de Madrid and Center for Computational Simulation of Universidad Politécnica de Madrid, Madrid, Spain
| | - Ancor Sanz-García
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - David García-Azorín
- Headache Unit, Neurology Department, Department of Medicine, University of Valladolid, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | | | - Alex Jaimes
- Headache Unit, Neurology Department, Fundación Jiménez Díaz, Madrid, Spain
| | | | - Javier Díaz de Terán
- Headache Unit, Neurology Department, Hospital Universitario La Paz, Madrid, Spain
| | - Nuria González-García
- Headache Unit, Neurology Department, Hospital Universitario Clínico San Carlos, Madrid, Spain
| | - Sonia Quintas
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - Rocio Belascoaín
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
| | - Javier Casas Limón
- Headache Unit Neurology Department, Hospital Universitario Fundación de Alcorcón, Alcorcón, Spain
| | - Germán Latorre
- Headache Unit, Neurology Department, Hospital Universitario de Fuenlabrada, Madrid, Spain
| | - Carlos Calle de Miguel
- Headache Unit, Neurology Department, Hospital Universitario de Fuenlabrada, Madrid, Spain
| | - Álvaro Sierra
- Headache Unit, Neurology Department, Department of Medicine, University of Valladolid, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | - Ángel Luis Guerrero-Peral
- Headache Unit, Neurology Department, Department of Medicine, University of Valladolid, Hospital Clínico Universitario de Valladolid, Valladolid, Spain
| | | | - Ana Beatriz Gago-Veiga
- Headache Unit, Neurology Department, Hospital Universitario de la Princesa & Instituto de Investigación Sanitaria La Princesa, Madrid, Spain
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Liñares-Blanco J, Fernandez-Lozano C, Seoane JA, Lopez-Campos G. Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data. Stud Health Technol Inform 2021; 281:382-6. [PMID: 34042770 DOI: 10.3233/SHTI210185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in the world, a classification model has been developed based on Machine Learning (ML) capable of predicting the country of origin (United Kingdom vs United States) according to metagenomic data. The data were used for the training of a glmnet algorithm and a Random Forest algorithm. Both algorithms obtained similar results (0.698 and 0.672 in AUC, respectively). Furthermore, thanks to the application of a multivariate feature selection algorithm, eleven metagenomic genres highly correlated with the country of origin were obtained. An in-depth study of the variables used in each model is shown in the present work.
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Fatoum HA, Kuzmeskas K, Halamka JD, Hashmi SK. Harnessing the Power of Blockchains and Machine Learning to End the COVID-19 Pandemic. Blockchain Healthc Today 2020; 3. [PMID: 36777052 DOI: 10.30953/bhty.v3.153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The last catastrophic pandemic the world has seen was the 1918 H1N1 influenza pandemic, considering that it happened 102 years ago, one can perhaps leverage the technologic advancements over the past century to combat the current pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (COVID-19), which has already infected more than 16 million people globally and shut down the majority of the daily activities of life. The danger and challenge of infectious diseases such as COVID-19 lies in their highly contagious nature, spreading like wildfire with the potential to infect the majority of the world's population unless drastic measures are undertaken. In some countries, the social distancing and quarantine measures are either insufficient or ineffective as the number of cases is still on the rise. A major issue causing this rise is that most COVID-19 carriers appear asymptomatic. Identifying infected individuals as well as healthy/immune ones is the most crucial step in halting the disease spread. This is where the role of mass screening comes in.
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Stamate D, Kim M, Proitsi P, Westwood S, Baird A, Nevado-Holgado A, Hye A, Bos I, Vos SJB, Vandenberghe R, Teunissen CE, Kate MT, Scheltens P, Gabel S, Meersmans K, Blin O, Richardson J, De Roeck E, Engelborghs S, Sleegers K, Bordet R, Ramit L, Kettunen P, Tsolaki M, Verhey F, Alcolea D, Lléo A, Peyratout G, Tainta M, Johannsen P, Freund-Levi Y, Frölich L, Dobricic V, Frisoni GB, Molinuevo JL, Wallin A, Popp J, Martinez-Lage P, Bertram L, Blennow K, Zetterberg H, Streffer J, Visser PJ, Lovestone S, Legido-Quigley C. A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort. Alzheimers Dement (N Y) 2019; 5:933-938. [PMID: 31890857 PMCID: PMC6928349 DOI: 10.1016/j.trci.2019.11.001] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Introduction Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. Methods This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). Results On the test data, DL produced the AUC of 0.85 (0.80–0.89), XGBoost produced 0.88 (0.86–0.89) and RF produced 0.85 (0.83–0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. Discussion This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.
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Affiliation(s)
- Daniel Stamate
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK.,Data Science & Soft Computing Lab, London, UK.,Computing Department, Goldsmiths College, University of London, London, UK
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - Sarah Westwood
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Alison Baird
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Abdul Hye
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.,Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Rik Vandenberghe
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Charlotte E Teunissen
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Mara Ten Kate
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
| | - Philip Scheltens
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Silvy Gabel
- Department of Clinical Chemistry, Neurochemistry Laboratory, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands.,University Hospital Leuven, Leuven, Belgium.,Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Belgium
| | - Karen Meersmans
- University Hospital Leuven, Leuven, Belgium.,Department of Neurosciences, Laboratory for Cognitive Neurology, KU Leuven, Belgium
| | - Olivier Blin
- AIX Marseille University, INS, Ap-hm, Marseille, France
| | - Jill Richardson
- Neurosciences Therapeutic Area, GlaxoSmithKline R&D, Stevenage, UK
| | - Ellen De Roeck
- Faculty of Psychology & Educational Sciences Vrije Universiteit Brussel (VUB), Brussels, Belgium.,Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium.,Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), University of Antwerp, Antwerp, Belgium.,Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology, UZ Brussel and Center for Neurosciences, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Kristel Sleegers
- Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Belgium
| | - Régis Bordet
- University of Lille, Inserm, CHU Lille, Lille, France
| | - Lorena Ramit
- Alzheimer's Disease & Other Cognitive Disorders Unit, Hospital Clínic-IDIBAPS, Barcelona, Spain
| | - Petronella Kettunen
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Magda Tsolaki
- 1st Department of Neurology, AHEPA University Hospital, Makedonia, Thessaloniki, Greece
| | - Frans Verhey
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
| | - Daniel Alcolea
- Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Alberto Lléo
- Memory Unit, Neurology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | - Mikel Tainta
- Center for Research and Advanced Therapies, Fundacion CITA-alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Peter Johannsen
- Danish Dementia Research Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Yvonne Freund-Levi
- Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK.,Department of Neurobiology, Caring Sciences and Society (NVS), Division of Clinical Geriatrics, Karolinska Institute, and Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden
| | - Lutz Frölich
- Department of Geriatric Psychiatry, Zentralinstitut für Seelische Gesundheit, University of Heidelberg, Mannheim, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Giovanni B Frisoni
- University of Geneva, Geneva, Switzerland.,IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - José L Molinuevo
- University of Lille, Inserm, CHU Lille, Lille, France.,Barcelona Beta Brain Research Center, Unversitat Pompeu Fabra, Barcelona, Spain
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Julius Popp
- University Hospital of Lausanne, Lausanne, Switzerland.,Department of Mental Health and Psychiatry, Geriatric Psychiatry, Geneva University Hospitals, Geneva, Switzerland
| | - Pablo Martinez-Lage
- Center for Research and Advanced Therapies, Fundacion CITA-alzheimer Fundazioa, Donostia/San Sebastian, Spain
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany.,Department of Psychology, University of Oslo, Oslo, Norway
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,UK Dementia Research Institute at UCL, London, UK.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
| | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Pieter J Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.,Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Simon Lovestone
- Department of Psychiatry, University of Oxford, Oxford, UK.,Janssen-Cilag UK Ltd, Oxford, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center Copenhagen, Gentofte, Denmark.,Institute of Pharmaceutical Science, King's College London, London, UK
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10
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Tolpadi AA, Stone ML, Carass A, Prince JL, Gomez AD. Inverse Biomechanical Modeling of the Tongue via Machine Learning and Synthetic Training Data. Proc SPIE Int Soc Opt Eng 2018; 10576. [PMID: 29997406 DOI: 10.1117/12.2296927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The tongue's deformation during speech can be measured using tagged magnetic resonance imaging, but there is no current method to directly measure the pattern of muscles that activate to produce a given motion. In this paper, the activation pattern of the tongue's muscles is estimated by solving an inverse problem using a random forest. Examples describing different activation patterns and the resulting deformations are generated using a finite-element model of the tongue. These examples form training data for a random forest comprising 30 decision trees to estimate contractions in 262 contractile elements. The method was evaluated on data from tagged magnetic resonance data from actual speech and on simulated data mimicking flaps that might have resulted from glossectomy surgery. The estimation accuracy was modest (5.6% error), but it surpassed a semi-manual approach (8.1% error). The results suggest that a machine learning approach to contraction pattern estimation in the tongue is feasible, even in the presence of flaps.
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Affiliation(s)
- Aniket A Tolpadi
- Department of Bioengineering, Rice University, Houston, TX, US 77005
| | - Maureen L Stone
- Department of Neural and Pain Sciences, Dept of Orthodontics, University of Maryland Dental School, Baltimore, MD, US 21201
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
| | - Arnold D Gomez
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, US 21218
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11
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Abstract
Drug-induced abnormal heart rhythm known as Torsades de Pointes (TdP) is a potential lethal ventricular tachycardia found in many patients. Even newly released anti-arrhythmic drugs, like ivabradine with HCN channel as a primary target, block the hERG potassium current in overlapping concentration interval. Promiscuous drug block to hERG channel may potentially lead to perturbation of the action potential duration (APD) and TdP, especially when with combined with polypharmacy and/or electrolyte disturbances. The example of novel anti-arrhythmic ivabradine illustrates clinically important and ongoing deficit in drug design and warrants for better screening methods. There is an urgent need to develop new approaches for rapid and accurate assessment of how drugs with complex interactions and multiple subcellular targets can predispose or protect from drug-induced TdP. One of the unexpected outcomes of compulsory hERG screening implemented in USA and European Union resulted in large datasets of IC50 values for various molecules entering the market. The abundant data allows now to construct predictive machine-learning (ML) models. Novel ML algorithms and techniques promise better accuracy in determining IC50 values of hERG blockade that is comparable or surpassing that of the earlier QSAR or molecular modeling technique. To test the performance of modern ML techniques, we have developed a computational platform integrating various workflows for quantitative structure activity relationship (QSAR) models using data from the ChEMBL database. To establish predictive powers of ML-based algorithms we computed IC50 values for large dataset of molecules and compared it to automated patch clamp system for a large dataset of hERG blocking and non-blocking drugs, an industry gold standard in studies of cardiotoxicity. The optimal protocol with high sensitivity and predictive power is based on the novel eXtreme gradient boosting (XGBoost) algorithm. The ML-platform with XGBoost displays excellent performance with a coefficient of determination of up to R2 ~0.8 for pIC50 values in evaluation datasets, surpassing other metrics and approaches available in literature. Ultimately, the ML-based platform developed in our work is a scalable framework with automation potential to interact with other developing technologies in cardiotoxicity field, including high-throughput electrophysiology measurements delivering large datasets of profiled drugs, rapid synthesis and drug development via progress in synthetic biology.
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Affiliation(s)
- Soren Wacker
- Centre for Molecular Simulation, Department of Biological Sciences, University of Calgary, 2500 University Drive, Calgary, AB, Canada, T2N 1N4.,Achlys Inc. and Li Ka Shing Institute of Applied Virology, 6-020 Katz Group Centre for Health Research, University of Alberta, Edmonton, AB T6G 2E1
| | - Sergei Yu Noskov
- Centre for Molecular Simulation, Department of Biological Sciences, University of Calgary, 2500 University Drive, Calgary, AB, Canada, T2N 1N4
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12
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Abstract
The PepArML meta-search peptide identification platform for tandem mass spectra provides a unified search interface to seven search engines; a robust cluster, grid, and cloud computing scheduler for large-scale searches; and an unsupervised, model-free, machine-learning-based result combiner, which selects the best peptide identification for each spectrum, estimates false-discovery rates, and outputs pepXML format identifications. The meta-search platform supports Mascot; Tandem with native, k-score and s-score scoring; OMSSA; MyriMatch; and InsPecT with MS-GF spectral probability scores—reformatting spectral data and constructing search configurations for each search engine on the fly. The combiner selects the best peptide identification for each spectrum based on search engine results and features that model enzymatic digestion, retention time, precursor isotope clusters, mass accuracy, and proteotypic peptide properties, requiring no prior knowledge of feature utility or weighting. The PepArML meta-search peptide identification platform often identifies two to three times more spectra than individual search engines at 10% FDR.
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Affiliation(s)
- Nathan J Edwards
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, Washington, D.C., USA
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13
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Heumann BW, Walsh SJ, Verdery AM, McDaniel PM, Rindfuss RR. Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand. Ann Assoc Am Geogr 2013; 103:10.1080/00045608.2012.702479. [PMID: 24187378 PMCID: PMC3811970 DOI: 10.1080/00045608.2012.702479] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District - cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops.
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Affiliation(s)
- Benjamin W Heumann
- Department of Geography, University of North Carolina at Chapel Hill ; Carolina Population Center, University of North Carolina at Chapel Hill
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