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Caldas BT, Ribeiro FCV, Pereira JS, Souza WC, Lopes AJ, de Melo PL. Oscillometry of the respiratory system in Parkinson's disease: physiological changes and diagnostic use. BMC Pulm Med 2023; 23:406. [PMID: 37884922 PMCID: PMC10605979 DOI: 10.1186/s12890-023-02716-w] [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: 03/27/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Lung function analysis in Parkinson's disease (PD) is often difficult due to the demand for adequate forced expiratory maneuvers. Respiratory oscillometry exams require onlyquiet tidal breathing and provide a detailed analysis of respiratory mechanics. We hypothesized that oscillometry would simplify the diagnosis of respiratory abnormalitiesin PD and improve our knowledge about the pathophysiological changes in these patients. MATERIALS AND METHODS This observational study includes 20 controls and 47 individuals with PD divided into three groups (Hoehn and Yahr Scale 1-1.5; H&Y scale 2-3 and PD smokers).The diagnostic accuracy was evaluated by investigating the area under the receiver operating characteristic curve (AUC). RESULTS Initial stages are related to increased peripheral resistance (Rp; p = 0.001). In more advanced stages, a restrictive pattern is added, reflected by reductions in dynamic compliance (p < 0.05) and increase in resonance frequency (Fr; p < 0.001). Smoking PD patients presented increased Rp (p < 0.001) and Fr (p < 0.01). PD does not introduce changes in the central airways. Oscillometric changes were correlated with respiratory muscle weakness (R = 0.37, p = 0.02). Rp showed adequate accuracy in the detection of early respiratory abnormalities (AUC = 0.858), while in more advanced stages, Fr showed high diagnostic accuracy (AUC = 0.948). The best parameter to identify changes in smoking patients was Rp (AUC = 0.896). CONCLUSION The initial stages of PD are related to a reduction in ventilation homogeneity associated with changes in peripheral airways. More advanced stages also include a restrictive ventilatory pattern. These changes were correlated with respiratory muscle weakness and were observed in mild and moderate stages of PD in smokers and non-smokers. Oscillometry may adequately identify respiratory changes in the early stages of PD and obtain high diagnostic accuracy in more advanced stages of the disease.
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Affiliation(s)
- Bruno Tavares Caldas
- Department of Physiological Sciences, Biomedical Instrumentation Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - João Santos Pereira
- Department of Neurology, Pedro Ernesto University Hospital, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Wilma Costa Souza
- Carioca Parkinson Association, Municipal Rehabilitation Center, Rio de Janeiro, Brazil
| | - Agnaldo José Lopes
- Department of Pulmonology, Respiratory Function Testing Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Department of Physiological Sciences, Biomedical Instrumentation Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
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de Lima AD, Lopes AJ, do Amaral JLM, de Melo PL. Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis. BMC Med Inform Decis Mak 2022; 22:274. [PMID: 36266674 PMCID: PMC9583465 DOI: 10.1186/s12911-022-02021-2] [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: 06/25/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmonary mechanics. In addition to accurate results, there is a particular interest in their interpretability and explainability, so we used Genetic Programming since the classification is made with intelligible expressions and we also evaluate the feature importance in different experiments to find the more discriminative features. METHODOLOGY/PRINCIPAL FINDINGS We used genetic programming in its traditional tree form and a grammar-based form. To check if interpretable results are competitive, we compared their performance to K-Nearest Neighbors, Support Vector Machine, AdaBoost, Random Forest, LightGBM, XGBoost, Decision Trees and Logistic Regressor. We also performed experiments with fuzzy features and tested a feature selection technique to bring even more interpretability. The data used to feed the classifiers come from the FOT exams in 72 individuals, of which 25 were healthy, and 47 were diagnosed with sarcoidosis. Among the latter, 24 showed normal conditions by spirometry, and 23 showed respiratory changes. The results achieved high accuracy (AUC > 0.90) in two analyses performed (controls vs. individuals with sarcoidosis and normal spirometry and controls vs. individuals with sarcoidosis and altered spirometry). Genetic Programming and Grammatical Evolution were particularly beneficial because they provide intelligible expressions to make the classification. The observation of which features were selected most frequently also brought explainability to the study of sarcoidosis. CONCLUSIONS The proposed system may provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis. Clinicians may reference the prediction results and make better decisions, improving the productivity of pulmonary function services by AI-assisted workflow.
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Affiliation(s)
- Allan Danilo de Lima
- Electronic Engineering Post-Graduation Program, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Agnaldo J Lopes
- Pulmonary Function Laboratory, Faculty of Medical Sciences, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jorge Luis Machado do Amaral
- Department of Electronics and Telecommunications Engineering, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), Rio de Janeiro State University, Rio de Janeiro, Brazil.
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Polak AG. Algebraic approximation of the distributed model for the pressure drop in the respiratory airways. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3632. [PMID: 35648086 DOI: 10.1002/cnm.3632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/06/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
The complexity of the human respiratory system causes that one of the main methods of analyzing the dynamic pulmonary phenomena and interpreting experimental results are simulations of its computational models. Among the most compound elements of these models, apart from the bronchial tree structure, is the phenomenon of flow limitation in flexible bronchi, which causes them to collapse with increasing flow, thus their properties, such as resistance, compliance and inertance, are highly nonlinear and time-varying. Commonly, this phenomenon is ignored, or a distributed model for the airway pressure drop is applied, simulated with a modified numerical solver of this differential equation (ODE). The disadvantages of this solution are the problems with taking into account the inherent singularity of the model and the long computation time due to iterative nature of the ODE procedure. The aim of the work was to derive an algebraic approximation of this distributed model, suitable for implementation in continuous dynamic models, to validate it by comparing the results of simulations with the respiratory system model including approximate and original (ODE solver) numerical procedures, as well as to evaluate possible acceleration of calculations. All simulations, including spontaneous breathing, mechanical ventilation with the optimal ventilatory waveform and forced expiration, proved that algebraic approximation yielded results negligibly differing from the ODE solution, and shortened the computation time by an order. The proposed approach is an attractive alternative in the case of computer implementations of pulmonary models, where simulations of flows and pressures in the complex respiratory system are of primary importance.
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Affiliation(s)
- Adam G Polak
- Department of Electronic and Photonic Metrology, Faculty of Electronics, Photonics and Microsystems, Wrocław University of Science and Technology, Wrocław, Poland
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Zhao Y, Xu G, Li H, Chang M, Xiong C, Tao Y, Guan Y, Li Y, Yao S. Genome-wide mRNA profiling identifies the NRF2-regulated lymphocyte oxidative stress status in patients with silicosis. J Occup Med Toxicol 2021; 16:40. [PMID: 34517882 PMCID: PMC8436508 DOI: 10.1186/s12995-021-00332-0] [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: 02/24/2021] [Accepted: 08/30/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The immunomodulatory abnormalities of silicosis are related to the lymphocyte oxidative stress state. The potential effect of antioxidant therapy on silicosis may depend on the variation in nuclear factor erythroid 2-related factor 2 (NRF2)-regulated antioxidant genes in peripheral blood mononuclear cells (PBMCs). As NRF2 is a redox-sensitive transcription factor, its possible roles and underlying mechanism in the treatment of silicosis need to be clarified. METHODS Ninety-two male patients with silicosis and 87 male healthy volunteers were randomly selected. PBMCs were isolated from fresh blood from patients with silicosis and healthy controls. The lymphocyte oxidative stress state was investigated by evaluating NRF2 expression and NRF2-dependent antioxidative genes in PBMCs from patients with silicosis. Key differentially expressed genes (DEGs) and signaling pathways were identified utilizing RNA sequencing (RNA-Seq) and bioinformatics technology. Gene set enrichment analysis was used to identify the differences in NRF2 signaling networks between patients with silicosis and healthy controls. RESULTS The number of monocytes was significantly higher in patients with silicosis than that of healthy controls. Furthermore, RNA-Seq findings were confirmed using quantitative polymerase chain reaction and revealed that NRF2-regulated DEGs were associated with glutathione metabolism, transforming growth factor-β, and the extracellular matrix receptor interaction signaling pathway in PBMCs from patients with silicosis. The top 10 hub genes were identified by PPI analysis: SMAD2, MAPK3, THBS1, SMAD3, ITGB3, integrin alpha-V (ITGAV), von Willebrand factor (VWF), BMP4, CD44, and SMAD7. CONCLUSIONS These findings suggest that NRF2 signaling regulates the lymphocyte oxidative stress state and may contribute to fibrogenic responses in human PBMCs. Therefore, NRF2 might serve as a novel preventive and therapeutic candidate for silicosis.
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Affiliation(s)
- Yingzheng Zhao
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, 063009, People's Republic of China.,School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Guangcui Xu
- School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Haibin Li
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, 063009, People's Republic of China.,School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Meiyu Chang
- School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Cheng Xiong
- School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yingjun Tao
- School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Yi Guan
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, 063009, People's Republic of China
| | - Yuchun Li
- School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China
| | - Sanqiao Yao
- School of Public Health, North China University of Science and Technology, Tangshan, Hebei Province, 063009, People's Republic of China. .,School of Public Health, Xinxiang Medical University, Xinxiang, Henan Province, 453003, People's Republic of China.
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Tuza FADA, de Sá PM, Castro HA, Lopes AJ, de Melo PL. Combined forced oscillation and fractional-order modeling in patients with work-related asthma: a case-control study analyzing respiratory biomechanics and diagnostic accuracy. Biomed Eng Online 2020; 19:93. [PMID: 33298072 PMCID: PMC7724713 DOI: 10.1186/s12938-020-00836-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 11/23/2020] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND Fractional-order (FrOr) models have a high potential to improve pulmonary science. These models could be useful for biomechanical studies and diagnostic purposes, offering accurate models with an improved ability to describe nature. This paper evaluates the performance of the Forced Oscillation (FO) associated with integer (InOr) and FrOr models in the analysis of respiratory alterations in work-related asthma (WRA). METHODS Sixty-two individuals were evaluated: 31 healthy and 31 with WRA with mild obstruction. Patients were analyzed pre- and post-bronchodilation. The diagnostic accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). To evaluate how well do the studied models correspond to observed data, we analyzed the mean square root of the sum (MSEt) and the relative distance (Rd) of the estimated model values to the measured resistance and reactance measured values. RESULTS AND DISCUSSION Initially, the use of InOr and FrOr models increased our understanding of the WRA physiopathology, showing increased peripheral resistance, damping, and hysteresivity. The FrOr model (AUC = 0.970) outperformed standard FO (AUC = 0.929), as well as InOr modeling (AUC = 0.838) in the diagnosis of respiratory changes, achieving high accuracy. FrOr improved the curve fitting (MSEt = 0.156 ± 0.340; Rd = 3.026 ± 1.072) in comparison with the InOr model (MSEt = 0.367 ± 0.991; Rd = 3.363 ± 1.098). Finally, we demonstrated that bronchodilator use increased dynamic compliance, as well as reduced damping and peripheral resistance. CONCLUSIONS Taken together, these results show clear evidence of the utility of FO associated with fractional-order modeling in patients with WRA, improving our knowledge of the biomechanical abnormalities and the diagnostic accuracy in this disease.
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Affiliation(s)
- Fábio Augusto D Alegria Tuza
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Haroldo Lisboa da Cunha Pavilion Number 104 and 105, São Francisco Xavier Street 524 Maracanã, Rio de Janeiro, RJ, 20550-013, Brazil
- BioVasc Research Laboratory, Institute of Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Paula Morisco de Sá
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Haroldo Lisboa da Cunha Pavilion Number 104 and 105, São Francisco Xavier Street 524 Maracanã, Rio de Janeiro, RJ, 20550-013, Brazil
- BioVasc Research Laboratory, Institute of Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Hermano A Castro
- National School of Public Health, Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Agnaldo José Lopes
- School of Medical Sciences, Pulmonary Function Testing Laboratory, State University of Rio de Janeiro, Rio de Janeiro, RJ, Brazil
- Rehabilitation Sciences Post-Graduation Program, Augusto Motta University Centre, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Haroldo Lisboa da Cunha Pavilion Number 104 and 105, São Francisco Xavier Street 524 Maracanã, Rio de Janeiro, RJ, 20550-013, Brazil.
- BioVasc Research Laboratory, Institute of Biology, State University of Rio de Janeiro, Rio de Janeiro, Brazil.
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Ribeiro CO, Lopes AJ, de Melo PL. Oscillation Mechanics, Integer and Fractional Respiratory Modeling in COPD: Effect of Obstruction Severity. Int J Chron Obstruct Pulmon Dis 2020; 15:3273-3289. [PMID: 33324050 PMCID: PMC7733470 DOI: 10.2147/copd.s276690] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 11/09/2020] [Indexed: 12/28/2022] Open
Abstract
Purpose This research examines the emerging role of respiratory oscillometry associated with integer (InOr) and fractional order (FrOr) respiratory models in the context of groups of patients with increasing severity. The contributions to our understanding of the respiratory abnormalities along the course of increasing COPD severity and the diagnostic use of this method were also evaluated. Patients and Methods Forty-five individuals with no history of smoking or pulmonary diseases (control group) and 141 individuals with diagnoses of COPD were studied, being classified into 45 mild, 42 moderate, 36 severe and 18 very severe cases. Results This study has shown initially that the course of increasing COPD severity was adequately described by the model parameters. This resulted in significant and consistent correlations among these parameters and spirometric indexes. Additionally, this evaluation enhanced our understanding of the respiratory abnormalities in different COPD stages. The diagnostic accuracy analyses provided evidence that hysteresivity, obtained from FrOr modeling, allowed a highly accurate identification in patients with mild changes [area under the receiver operator characteristic curve (AUC)= 0.902]. Similar analyses in groups of moderate and severe patients showed that peripheral resistance, derived from InOr modeling, provided the most accurate parameter (AUC=0.898 and 0.998, respectively), while in very severe patients, traditional, InOr and FrOr parameters were able to reach high diagnostic accuracy (AUC>0.9). Conclusion InOr and FrOr modeling improved our knowledge of the respiratory abnormalities along the course of increasing COPD severity. In addition, the present study provides evidence that these models may contribute in the diagnosis of COPD. Respiratory oscillometry exams require only tidal breathing and are easy to perform. Taken together, these practical considerations and the results of the present study suggest that respiratory oscillometry associated with InOr and FrOr models may help to improve lung function tests in COPD.
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Affiliation(s)
- Caroline Oliveira Ribeiro
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Agnaldo José Lopes
- Pulmonary Function Laboratory, State University of Rio de Janeiro, Rio de Janeiro, Brazil.,Pulmonary Rehabilitation Laboratory, Augusto Motta University Center, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Biomedical Instrumentation Laboratory, Institute of Biology and Faculty of Engineering, State University of Rio de Janeiro, Rio de Janeiro, Brazil
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