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Sharma S, Olgers K, Knollinger S, Somisetty S, Seol C, Yanamala N. Artificial intelligence augmented home sleep apnea testing device study (AISAP study). PLoS One 2024; 19:e0303076. [PMID: 38758825 PMCID: PMC11101079 DOI: 10.1371/journal.pone.0303076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/18/2024] [Indexed: 05/19/2024] Open
Abstract
STUDY OBJECTIVE This study aimed to prospectively validate the performance of an artificially augmented home sleep apnea testing device (WVU-device) and its patented technology. METHODOLOGY The WVU-device, utilizing patent pending (US 20210001122A) technology and an algorithm derived from cardio-pulmonary physiological parameters, comorbidities, and anthropological information was prospectively compared with a commercially available and Center for Medicare and Medicaid Services (CMS) approved home sleep apnea testing (HSAT) device. The WVU-device and the HSAT device were applied on separate hands of the patient during a single night study. The oxygen desaturation index (ODI) obtained from the WVU-device was compared to the respiratory event index (REI) derived from the HSAT device. RESULTS A total of 78 consecutive patients were included in the prospective study. Of the 78 patients, 38 (48%) were women and 9 (12%) had a Fitzpatrick score of 3 or higher. The ODI obtained from the WVU-device corelated well with the HSAT device, and no significant bias was observed in the Bland-Altman curve. The accuracy for ODI > = 5 and REI > = 5 was 87%, for ODI> = 15 and REI > = 15 was 89% and for ODI> = 30 and REI of > = 30 was 95%. The sensitivity and specificity for these ODI /REI cut-offs were 0.92 and 0.78, 0.91 and 0.86, and 0.94 and 0.95, respectively. CONCLUSION The WVU-device demonstrated good accuracy in predicting REI when compared to an approved HSAT device, even in patients with darker skin tones.
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Affiliation(s)
- Sunil Sharma
- Division of Pulmonary, Critical Care and Sleep Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Kassandra Olgers
- Division of Pulmonary, Critical Care and Sleep Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Scott Knollinger
- Department of Respiratory Care, Ruby Memorial Hospital, Morgantown, WV, United States of America
| | | | - Calvin Seol
- Eberly College of Arts and Science, West Virginia University, Morgantown, WV, United States of America
| | - Naveena Yanamala
- Rutgers Robert Wood Johnson Medical School, Division of Cardiovascular Disease and Hypertension, New Brunswick, NJ, United States of America
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Siva NK, Singh Y, Hathaway QA, Sengupta PP, Yanamala N. A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data. Sci Rep 2024; 14:10672. [PMID: 38724564 PMCID: PMC11082231 DOI: 10.1038/s41598-024-61201-4] [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: 06/21/2023] [Accepted: 05/02/2024] [Indexed: 05/12/2024] Open
Abstract
To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the "pattern" of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.
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Affiliation(s)
- Nanda K Siva
- School of Medicine, West Virginia University, Morgantown, WV, USA
- Division of Cardiology, Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Yashbir Singh
- Division of Cardiology, Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Quincy A Hathaway
- School of Medicine, West Virginia University, Morgantown, WV, USA
- Division of Cardiology, Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Partho P Sengupta
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, 125 Patterson St, New Brunswick, NJ, 08901, USA.
| | - Naveena Yanamala
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, 125 Patterson St, New Brunswick, NJ, 08901, USA.
- Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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Shah R, Tokodi M, Jamthikar A, Bhatti S, Akhabue E, Casaclang-Verzosa G, Yanamala N, Sengupta PP. A Deep Patient-Similarity Learning Framework for the Assessment of Diastolic Dysfunction in Elderly Patients. Eur Heart J Cardiovasc Imaging 2024:jeae037. [PMID: 38315669 DOI: 10.1093/ehjci/jeae037] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 01/27/2024] [Accepted: 02/01/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND AND AIMS Age-related changes in cardiac structure and function are well recognized and make the clinical determination of abnormal LV diastolic function (LVDD) particularly challenging in the elderly. We investigated whether a deep neural network (DeepNN) model[70] of LVDD, previously validated in a younger cohort, can be implemented in an older population to predict incident heart failure (HF). METHODS A previously developed DeepNN was tested on 5,596 older participants (66-90 years; 57% female; 20% black) from the Atherosclerosis Risk in Communities study. The association of DeepNN predictions with HF or all-cause death for the American College of Cardiology Foundation/American Heart Association Stage A/B (n = 4,054) and Stage C/D (n = 1,542) subgroups was assessed. RESULTS The DeepNN-predicted High-Risk compared to the Low-Risk phenogroup demonstrated an increased incidence of HF and death for both Stage A/B and Stage C/D (log-rank p < 0.0001 for all). In multivariable analyses, the High-Risk phenogroup remained an independent predictor of HF and death in both Stages A/B (adjusted hazard ratio (HR) [95% confidence interval], 6.52[4.20-10.13] and 2.21(1.68-2.91), both p < 0.0001) and Stage C/D (6.51[4.06-10.44] and 1.03(1.00-1.06), both p < 0.0001) respectively. In addition, DeepNN showed incremental value over the 2016 ASE/EACVI guidelines (Net reclassification index, 0.5[CI:0.4-0.6], p < 0.001; C-statistic improvement, DeepNN [0.76] vs. ASE/EACVI [0.70], p < 0.001) overall and maintained across stage-groups. CONCLUSIONS Despite training with a younger cohort, a deep patient-similarity-based learning framework for assessing LVDD provides a robust prediction of all-cause death and incident HF for older patients.
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Affiliation(s)
- Rohan Shah
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Marton Tokodi
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Ankush Jamthikar
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Sabha Bhatti
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Ehimare Akhabue
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Grace Casaclang-Verzosa
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Naveena Yanamala
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital (RWJUH) and Rutgers Robert Wood Johnson Medical School (RWJMS), New Brunswick, New Jersey, USA
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Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Curr Cardiol Rep 2023; 25:1897-1907. [PMID: 38091196 DOI: 10.1007/s11886-023-02005-2] [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] [Accepted: 11/21/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
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Affiliation(s)
- Daniel Aziz
- Department of Internal Medicine, Rutgers - Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Kameswari Maganti
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Partho Sengupta
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA.
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Bernard J, Yanamala N, Shah R, Seetharam K, Altes A, Dupuis M, Toubal O, Mahjoub H, Dumortier H, Tartar J, Salaun E, O'Connor K, Bernier M, Beaudoin J, Côté N, Vincentelli A, LeVen F, Maréchaux S, Pibarot P, Sengupta PP. Integrating Echocardiography Parameters With Explainable Artificial Intelligence for Data-Driven Clustering of Primary Mitral Regurgitation Phenotypes. JACC Cardiovasc Imaging 2023; 16:1253-1267. [PMID: 37178071 DOI: 10.1016/j.jcmg.2023.02.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/20/2023] [Accepted: 02/09/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Primary mitral regurgitation (MR) is a heterogeneous clinical disease requiring integration of echocardiographic parameters using guideline-driven recommendations to identify severe disease. OBJECTIVES The purpose of this preliminary study was to explore novel data-driven approaches to delineate phenotypes of MR severity that benefit from surgery. METHODS The authors used unsupervised and supervised machine learning and explainable artificial intelligence (AI) to integrate 24 echocardiographic parameters in 400 primary MR subjects from France (n = 243; development cohort) and Canada (n = 157; validation cohort) followed up during a median time of 3.2 years (IQR: 1.3-5.3 years) and 6.8 (IQR: 4.0-8.5 years), respectively. The authors compared the phenogroups' incremental prognostic value over conventional MR profiles and for the primary endpoint of all-cause mortality incorporating time-to-mitral valve repair/replacement surgery as a covariate for survival analysis (time-dependent exposure). RESULTS High-severity (HS) phenogroups from the French cohort (HS: n = 117; low-severity [LS]: n = 126) and the Canadian cohort (HS: n = 87; LS: n = 70) showed improved event-free survival in surgical HS subjects over nonsurgical subjects (P = 0.047 and P = 0.020, respectively). A similar benefit of surgery was not seen in the LS phenogroup in both cohorts (P = 0.70 and P = 0.50, respectively). Phenogrouping showed incremental prognostic value in conventionally severe or moderate-severe MR subjects (Harrell C statistic improvement; P = 0.480; and categorical net reclassification improvement; P = 0.002). Explainable AI specified how each echocardiographic parameter contributed to phenogroup distribution. CONCLUSIONS Novel data-driven phenogrouping and explainable AI aided in improved integration of echocardiographic data to identify patients with primary MR and improved event-free survival after mitral valve repair/replacement surgery.
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Affiliation(s)
- Jérémy Bernard
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Naveena Yanamala
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Rohan Shah
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Karthik Seetharam
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA
| | - Alexandre Altes
- Department of Cardiology, GCS-Groupement des Hôpitaux de l'Institut Catholique de Lille, Université Catholique de Lille, Lille, France
| | - Marlène Dupuis
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Oumhani Toubal
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Haïfa Mahjoub
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Hélène Dumortier
- Department of Cardiology, GCS-Groupement des Hôpitaux de l'Institut Catholique de Lille, Université Catholique de Lille, Lille, France
| | - Jean Tartar
- Department of Cardiology, GCS-Groupement des Hôpitaux de l'Institut Catholique de Lille, Université Catholique de Lille, Lille, France
| | - Erwan Salaun
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Kim O'Connor
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Mathieu Bernier
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Jonathan Beaudoin
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - Nancy Côté
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada
| | - André Vincentelli
- Cardiac Surgery Department, Centre Hospitalier Régional et Universitaire de Lille, Lille, France
| | - Florent LeVen
- Department of Cardiology, Hôpital La Cavale Blanche-Centre Hospitalier Regional Universitaire de Brest, Brest, France
| | - Sylvestre Maréchaux
- Department of Cardiology, GCS-Groupement des Hôpitaux de l'Institut Catholique de Lille, Université Catholique de Lille, Lille, France
| | - Philippe Pibarot
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval/Québec Heart and Lung Institute, Laval University, Québec City, Québec, Canada.
| | - Partho P Sengupta
- Robert Wood Johnson University Hospital, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.
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Sengupta S, Biswal S, Titus J, Burman A, Reddy K, Fulwani MC, Khan A, Deshpande N, Shrivastava S, Yanamala N, Sengupta PP. A novel breakthrough in wrist-worn transdermal troponin-I-sensor assessment for acute myocardial infarction. Eur Heart J Digit Health 2023; 4:145-154. [PMID: 37265867 PMCID: PMC10232240 DOI: 10.1093/ehjdh/ztad015] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [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] [Received: 02/02/2023] [Revised: 02/14/2023] [Indexed: 06/03/2023]
Abstract
Aims Clinical differentiation of acute myocardial infarction (MI) from unstable angina and other presentations mimicking acute coronary syndromes (ACS) is critical for implementing time-sensitive interventions and optimizing outcomes. However, the diagnostic steps are dependent on blood draws and laboratory turnaround times. We tested the clinical feasibility of a wrist-worn transdermal infrared spectrophotometric sensor (transdermal-ISS) in clinical practice and assessed the performance of a machine learning algorithm for identifying elevated high-sensitivity cardiac troponin-I (hs-cTnI) levels in patients hospitalized with ACS. Methods and results We enrolled 238 patients hospitalized with ACS at five sites. The final diagnosis of MI (with or without ST elevation) and unstable angina was adjudicated using electrocardiography (ECG), cardiac troponin (cTn) test, echocardiography (regional wall motion abnormality), or coronary angiography. A transdermal-ISS-derived deep learning model was trained (three sites) and externally validated with hs-cTnI (one site) and echocardiography and angiography (two sites), respectively. The transdermal-ISS model predicted elevated hs-cTnI levels with areas under the receiver operator characteristics of 0.90 [95% confidence interval (CI), 0.84-0.94; sensitivity, 0.86; and specificity, 0.82] and 0.92 (95% CI, 0.80-0.98; sensitivity, 0.94; and specificity, 0.64), for internal and external validation cohorts, respectively. In addition, the model predictions were associated with regional wall motion abnormalities [odds ratio (OR), 3.37; CI, 1.02-11.15; P = 0.046] and significant coronary stenosis (OR, 4.69; CI, 1.27-17.26; P = 0.019). Conclusion A wrist-worn transdermal-ISS is clinically feasible for rapid, bloodless prediction of elevated hs-cTnI levels in real-world settings. It may have a role in establishing a point-of-care biomarker diagnosis of MI and impact triaging patients with suspected ACS.
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Affiliation(s)
- Shantanu Sengupta
- Sengupta Hospital and Research Institute, Nagpur- 440033, Vidarbha (Dist), India
| | | | - Jitto Titus
- RCE Technologies, 2292 Faraday Avenue, Carlsbad, CA 92008, USA
| | - Atandra Burman
- RCE Technologies, 2292 Faraday Avenue, Carlsbad, CA 92008, USA
| | - Keshav Reddy
- Division of Cardiovascular Disease and Hypertension, Rutgers RobertWood Johnson Medical School, 125 Patterson St, New Brunswick, NJ 08901, USA
| | - Mahesh C Fulwani
- Shrikrishna Hrudayalay and Critical Care Center, Department of Cardiology, Dhantoli, Nagpur - 440010, Vidarbha (Dist), India
| | - Aziz Khan
- Department of Cardiology, Crescent Hospital and Heart Center, Dhantoli, Nagpur- 440010, Vidarbha (Dist), India
| | - Niteen Deshpande
- Department of Cardiology, Spandan Heart Institute and Research Center, Dhantoli, Nagpur- 440010, Vidarbha (Dist), India
| | - Smit Shrivastava
- Department of Cardiology, Advanced Cardiac Institute Pt JNM Medical College, Raipur- 492009, Chattisgarh, India
| | - Naveena Yanamala
- Division of Cardiovascular Disease and Hypertension, Rutgers RobertWood Johnson Medical School, 125 Patterson St, New Brunswick, NJ 08901, USA
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Jamthikar A, Tokodi M, Shah RV, Yanamala N, Sengupta PP. DISSECTING A DEEP NEURAL NETWORK TO UNCOVER HOW IT LEARNS THE AGE-INDEPENDENT RISK STRATIFICATION OF PATIENTS FROM ECHOCARDIOGRAPHIC VARIABLES. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02668-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Cai J, Sandhaus M, Yanamala N, Tokodi M, Sengupta PP. PREDICTION OF ATRIAL FIBRILLATION USING A MACHINE LEARNING MODEL FOR DIASTOLIC DYSFUNCTION. J Am Coll Cardiol 2023. [DOI: 10.1016/s0735-1097(23)02591-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Visaria A, Yanamala N, Moon SJ, Dave P, Sharma R. Abstract P408: Association Between South Asian Ethnicity and Clinical Characteristics of Patients Undergoing Cardiac Catheterization. Circulation 2023. [DOI: 10.1161/circ.147.suppl_1.p408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Background:
We sought to describe differences in the characteristics of coronary interventions in U.S. South Asians (SA), an ethnicity at high risk for CAD, as compared to other races/ethnicities in New Jersey.
Methods:
In this single-center, retrospective cohort study of 8,850 patients from 2019-2021 undergoing coronary interventions (elective or acute), SAs were identified by 2 expert adjudicators. We used multivariable linear regression to estimate differences in mean age of coronary intervention by race/ethnicity (SA, non-Hispanic White [NHW], non-Hispanic Black [NHB], Hispanic, Other Asian, Other/unspecified). Among a subset of 7,288 patients, we used logistic regression to estimate odds of premature CAD (age<50y for overall cohort, <45y for male, <55y for female) and odds of multi-vessel disease (MVD; >2 vessels with ≥70% stenosis), adjusted for confounders (Table).
Results:
Among 8,850 patients (mean age 68y; 34% female; 5,011 [58%] NHW, 1,054 [12%] SA), 8.6% had premature CAD, 38% had acute coronary syndrome, and 20% had MVD. SA males, on average, were 5.1y younger and SA females were 3.9y younger, than NHW (male: SA, 63y [SD,12], 67[11] for NHW; female: SA, 67[11], NHW, 70[12]. p<0.001). Compared to NHW, SAs had 4.0x higher odds (male: 4.6x, female: 2.1x), NHB had 2.6x higher odds (M: 2.7x, F: 2.3x), other Asians had 2.1x higher odds (M: 2.3x, F: 1.3x), and Hispanics had 4.5x higher odds (M: 5.4x, F: 2.5x) of premature CAD (Table). SAs also had 47% higher odds of MVD than NHW (male: 45%, female: 47%), while NHB and Hispanics had 46% and 24% lower odds, respectively (Table). Findings were similar when restricting the cohort to patients requiring acute intervention (SA vs. NHW; OR [95% CI]. premature CAD, 3.73[2.44,5.71]; MVD, 1.54[1.12,2.11]).
Conclusion:
Independent of CAD risk factors, NJ SA males and females undergoing coronary intervention have more than 4 times and 2 times the likelihood of premature CAD, respectively, and more than 1.4 times the likelihood of multi-vessel disease compared to NHW.
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Affiliation(s)
| | | | | | - Payal Dave
- Rutgers Robert Wood Johnson Med Sch, New Brunswick, NJ
| | - Ranita Sharma
- Rutgers Robert Wood Johnson Med Sch, New Brunswick, NJ
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Avalon JC, Fuqua J, Deskins S, Miller T, Conte J, Martin D, Marano G, Yanamala N, Mills J, Bianco C, Patel B, Seetharam K, Raylman R, Sengupta PP, Hamirani YS. Quantitative single photon emission computed tomography derived standardized uptake values on 99mTc-PYP scan in patients with suspected ATTR cardiac amyloidosis. J Nucl Cardiol 2023; 30:127-139. [PMID: 35655113 DOI: 10.1007/s12350-022-02988-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 04/02/2022] [Indexed: 01/15/2023]
Abstract
Technetium-99 pyrophosphate scintigraphy (99mTc-PYP) provides qualitative and semiquantitative diagnosis of ATTR cardiac amyloidosis (ATTR-CA) using the Perugini scoring system and heart/contralateral heart ratio (H/CL) on planar imaging. Standardized uptake values (SUV) with quantitative single photon emission computed tomography (xSPECT/CT) can offer superior diagnostic accuracy and quantification through precise myocardial contouring that enhances assessment of ATTR-CA burden. We examined the correlation of xSPECT/CT SUVs with Perugini score and H/CL ratio. We also assessed SUV correlation with cardiac magnetic resonance (CMR), echocardiographic, and baseline clinical characteristics. Retrospective review of 78 patients with suspected ATTR-CA that underwent 99mTc-PYP scintigraphy with xSPECT/CT. Patients were grouped off Perugini score (Grade 0-1 and Grade 2-3), H/CL ratio (≥ 1.5 and < 1.5). Two cohorts were also created: myocardium SUVmax > 1.88 and ≤ 1.88 at 1-hour based off an AUC curve with 1.88 showing the greatest sensitivity and specificity. Cardiac SUV retention index was calculated as [SUVmax myocardium/SUVmax vertebrae] × SUVmax paraspinal muscle. Primary outcome was myocardium SUVmax at 1-hour correlation with Perugini grades, H/CL ratio, CMR, and echocardiographic data. Higher Perugini Grades corresponded with higher myocardium SUVmax values, especially when comparing Perugini Grade 3 to Grade 2 and 1 (3.03 ± 2.1 vs 0.59 ± 0.97 and 0.09 ± 0.2, P < 0.001). Additionally, patients with H/CL ≥ 1.5 had significantly higher myocardium SUVmax compared to patients with H/CL ≤ 1.5 (2.92 ± 2.18 vs 0.35 ± 0.60, P < 0.01). Myocardium SUVmax at 1-hour strongly correlated with ECV (r = 0.91, P = 0.001), pre-contrast T1 map values (r = 0.66, P = 0.037), and left ventricle mass index (r = 0.80, P = 0.002) on CMR. SUVs derived from 99mTc-PYP scintigraphy with xSPECT/CT provides a discriminatory and quantitative method to diagnose and assess ATTR-CA burden. These findings strongly correlate with CMR.
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Affiliation(s)
| | - Jacob Fuqua
- School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Seth Deskins
- School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Tyler Miller
- School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Justin Conte
- School of Medicine, West Virginia University, Morgantown, WV, USA
| | - Daniel Martin
- Department of Radiology, West Virginia University, Morgantown, WV, USA
| | - Gary Marano
- Department of Radiology, West Virginia University, Morgantown, WV, USA
| | - Naveena Yanamala
- Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV, 26506, USA
| | - James Mills
- Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV, 26506, USA
| | - Christopher Bianco
- Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV, 26506, USA
| | - Brijesh Patel
- Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV, 26506, USA
| | - Karthik Seetharam
- Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV, 26506, USA
| | - Raymond Raylman
- Department of Radiology, West Virginia University, Morgantown, WV, USA
| | - Partho P Sengupta
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, 125 Patterson St, New Brunswick, NJ, 08901, USA
| | - Yasmin S Hamirani
- Heart and Vascular Institute, West Virginia University, 1 Medical Center Dr, Morgantown, WV, 26506, USA.
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11
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Radhakrishnan A, Spencer S, Yanamala N, Malepati S. Evaluating the Efficacy and Safety of EZC Pak, a 5-Day Combination Echinacea-Zinc-Vitamin C Dose Pack with or without Vitamin D, in the Management of Outpatient Upper Respiratory Infections. Infect Drug Resist 2023; 16:2561-2572. [PMID: 37163146 PMCID: PMC10164544 DOI: 10.2147/idr.s392087] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/16/2023] [Indexed: 05/11/2023] Open
Abstract
Background Growing antibiotic resistance is among the most serious threats to public health, with antibiotic misuse considered a leading driver of the problem. One of the largest areas of misuse is in outpatient upper respiratory infections (URIs). The purpose of this research is to evaluate the efficacy of EZC Pak, a combination Echinacea-Zinc-Vitamin C dose pack with or without Vitamin D, on the duration of illness and symptom severity of non-specific URIs as an alternative to antibiotics when none are deemed clinically necessary. A secondary analysis was carried out on patient satisfaction. Methods A total of 360 patients across the United States were enrolled and randomized in a double-blind manner across two intervention groups, EZC Pak, EZC Pak+Vitamin D, and one placebo group. The study utilized a smartphone-based app to capture data. Once a participant reported the first URI symptom, they were instructed to take the intervention as directed and complete the daily symptom survey score until their symptoms resolved. Results The average EZC Pak participant recovered 1.39 days (90% CI 1.05 to 1.73) faster than the average placebo participant (p=0.017). The average EZC Pak participant reported a 17.43% (90% CI 17.1 to 17.8) lower symptom severity score versus placebo (p=0.029). EZC Pak users reported 2.9 times higher patient satisfaction versus placebo users (p=0.012). The addition of Vitamin D neither benefited nor harmed illness duration or symptom severity. Conclusion The findings support the potential use of EZC Pak as an alternative to patient request for antibiotics when none are deemed clinically necessary at the time of initial clinical presentation. The decision to replete vitamin D in the acute phase of URI is an individualized decision left to the patient and their clinician. EZC Pak may play a critical role in improving outpatient URI management and antibiotic stewardship (ClinicalTrials.gov number, NCT04943575).
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Affiliation(s)
- Aditya Radhakrishnan
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA
- Department of Medicine, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
- School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, 15213, USA
| | | | - Naveena Yanamala
- Department of Medicine, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, USA
- Department of Medicine, West Virginia University School of Medicine, Morgantown, WV, 26506, USA
- School of Computer Science, Carnegie-Mellon University, Pittsburgh, PA, 15213, USA
| | - Sarath Malepati
- The PPC Group, Los Angeles, CA, 90049, USA
- Correspondence: Sarath Malepati, The PPC Group, 520 South Sepulveda Blvd, Suite 400, Los Angeles, CA, 90049, USA, Tel +1 310 749-8730, Fax +1 877 705-7327, Email
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12
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Hathaway QA, Yanamala N, Sengupta PP. Multimodal data for systolic and diastolic blood pressure prediction: The hypertension conscious artificial intelligence. EBioMedicine 2022; 84:104261. [PMID: 36113186 PMCID: PMC9483570 DOI: 10.1016/j.ebiom.2022.104261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 08/25/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Quincy A Hathaway
- Heart and Vascular Institute, West Virginia University School of Medicine, Morgantown, WV, USA.
| | - Naveena Yanamala
- Division of Cardiovascular Diseases, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Partho P Sengupta
- Division of Cardiovascular Diseases, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
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13
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Mehta C, Shah R, Yanamala N, Sengupta PP. Cardiovascular Imaging Databases: Building Machine Learning Algorithms for Regenerative Medicine. Curr Stem Cell Rep 2022. [DOI: 10.1007/s40778-022-00216-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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14
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Berber A, Abdelhalim H, Zeeshan S, Vadapalli S, von Oehsen B, Yanamala N, Sengupta P, Ahmed Z. RNA-seq-driven expression analysis to investigate cardiovascular disease genes with associated phenotypes among atrial fibrillation patients. Clin Transl Med 2022; 12:e974. [PMID: 35875838 PMCID: PMC9309637 DOI: 10.1002/ctm2.974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 11/28/2022] Open
Affiliation(s)
- Asude Berber
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, New Jersey, USA
| | - Sreya Vadapalli
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA
| | - Barr von Oehsen
- Office of Advanced Research Computing, Rutgers, The State University of New Jersey, Computing Research and Education (CoRE) Building, Piscataway, New Jersey, USA
| | - Naveena Yanamala
- Division of Cardiovascular Disease, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA
| | - Partho Sengupta
- Division of Cardiovascular Disease, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA
| | - Zeeshan Ahmed
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, USA.,Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, New Brunswick, New Jersey, USA
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15
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Patel HB, Yanamala N, Patel B, Raina S, Farjo PD, Sunkara S, Tokodi M, Kagiyama N, Casaclang-Verzosa G, Sengupta PP. Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study. J Patient Cent Res Rev 2022; 9:98-107. [DOI: 10.17294/2330-0698.1893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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16
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Shah RV, Hiltner E, Casaclang-Verzosa G, Yanamala N, Sengupta PP. AGE- AND SEX-INDEPENDENT MACHINE LEARNING MODELS OF DIASTOLIC DYSFUNCTION. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02992-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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17
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Bhasin V, Dalsania R, Ghosh B, Chaudhary A, Iyer DB, Yanamala N, Sengupta PP. USING EXPLAINABLE ARTIFICIAL INTELLIGENCE TO PREDICT LETHAL OUTCOMES IN PATIENTS WITH MYOCARDIAL INFARCTION BASED ON ELECTROCARDIOGRAPHIC AND CLINICAL DATA. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02078-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Kagiyama N, Yanamala N, Banga S, Shrestha S, Verzosa GC, Sengupta PP. IDENTIFICATION OF VALVULAR HEART DISEASE FROM BODY SURFACE ELECTROCARDIOGRAM: A PROSPECTIVE MULTICENTER STUDY. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)01021-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Hathaway Q, Yanamala N, Patel BD. ADVERSE CARDIOVASCULAR EVENTS WITH IMMUNE CHECKPOINT INHIBITORS AND ASSOCIATIONS WITH PATIENT OUTCOMES. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)02916-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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20
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Nahass M, Chaudhary A, Lindsay DA, Liang S, Yanamala N, Bhatti S, Sethi A, Hakeem A, Kassotis J, Lee LY, Russo M, Sengupta PP. MACHINE-LEARNING FOR THE ASSESSMENT OF PATIENT PROSTHESIS MISMATCH IN PATIENTS WITH LOW GRADIENT SEVERE AORTIC STENOSIS UNDERGOING TAVR. J Am Coll Cardiol 2022. [DOI: 10.1016/s0735-1097(22)01696-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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21
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Fraser K, Hubbs A, Yanamala N, Mercer RR, Stueckle TA, Jensen J, Eye T, Battelli L, Clingerman S, Fluharty K, Dodd T, Casuccio G, Bunker K, Lersch TL, Kashon ML, Orandle M, Dahm M, Schubauer-Berigan MK, Kodali V, Erdely A. Histopathology of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities in a murine model. Part Fibre Toxicol 2021; 18:47. [PMID: 34923995 PMCID: PMC8686255 DOI: 10.1186/s12989-021-00440-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 12/02/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Multi-walled carbon nanotubes and nanofibers (CNT/F) have been previously investigated for their potential toxicities; however, comparative studies of the broad material class are lacking, especially those with a larger diameter. Additionally, computational modeling correlating physicochemical characteristics and toxicity outcomes have been infrequently employed, and it is unclear if all CNT/F confer similar toxicity, including histopathology changes such as pulmonary fibrosis. Male C57BL/6 mice were exposed to 40 µg of one of nine CNT/F (MW #1-7 and CNF #1-2) commonly found in exposure assessment studies of U.S. facilities with diameters ranging from 6 to 150 nm. Human fibroblasts (0-20 µg/ml) were used to assess the predictive value of in vitro to in vivo modeling systems. RESULTS All materials induced histopathology changes, although the types and magnitude of the changes varied. In general, the larger diameter MWs (MW #5-7, including Mitsui-7) and CNF #1 induced greater histopathology changes compared to MW #1 and #3 while MW #4 and CNF #2 were intermediate in effect. Differences in individual alveolar or bronchiolar outcomes and severity correlated with physical dimensions and how the materials agglomerated. Human fibroblast monocultures were found to be insufficient to fully replicate in vivo fibrosis outcomes suggesting in vitro predictive potential depends upon more advanced cell culture in vitro models. Pleural penetrations were observed more consistently in CNT/F with larger lengths and diameters. CONCLUSION Physicochemical characteristics, notably nominal CNT/F dimension and agglomerate size, predicted histopathologic changes and enabled grouping of materials by their toxicity profiles. Particles of greater nominal tube length were generally associated with increased severity of histopathology outcomes. Larger particle lengths and agglomerates were associated with more severe bronchi/bronchiolar outcomes. Spherical agglomerated particles of smaller nominal tube dimension were linked to granulomatous inflammation while a mixture of smaller and larger dimensional CNT/F resulted in more severe alveolar injury.
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Affiliation(s)
- Kelly Fraser
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - Ann Hubbs
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Naveena Yanamala
- Division of Cardiovascular Disease and Hypertension, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ USA
| | - Robert R. Mercer
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Todd A. Stueckle
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - Jake Jensen
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Tracy Eye
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Lori Battelli
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Sidney Clingerman
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Kara Fluharty
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Tiana Dodd
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | | | | | | | - Michael L. Kashon
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Marlene Orandle
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Matthew Dahm
- Division of Field Studies Evaluation, National Institute for Occupational Safety and Health, Cincinnati, OH USA
| | - Mary K. Schubauer-Berigan
- Division of Field Studies Evaluation, National Institute for Occupational Safety and Health, Cincinnati, OH USA
- International Agency for Research On Cancer, Lyon, France
| | - Vamsi Kodali
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - Aaron Erdely
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, Pathology and Physiology Research Branch, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
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22
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Hathaway QA, Yanamala N, Budoff MJ, Sengupta PP, Zeb I. Deep neural survival networks for cardiovascular risk prediction: The Multi-Ethnic Study of Atherosclerosis (MESA). Comput Biol Med 2021; 139:104983. [PMID: 34749095 DOI: 10.1016/j.compbiomed.2021.104983] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 07/01/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND There is growing interest in utilizing machine learning techniques for routine atherosclerotic cardiovascular disease (ASCVD) risk prediction. We investigated whether novel deep learning survival models can augment ASCVD risk prediction over existing statistical and machine learning approaches. METHODS 6814 participants from the Multi-Ethnic Study of Atherosclerosis (MESA) were followed over 16 years to assess incidence of all-cause mortality (mortality) or a composite of major adverse events (MAE). Features were evaluated within the categories of traditional risk factors, inflammatory biomarkers, and imaging markers. Data was split into an internal training/testing (four centers) and external validation (two centers). Both machine learning (COXPH, RSF, and lSVM) and deep learning (nMTLR and DeepSurv) models were evaluated. RESULTS In comparison to the COXPH model, DeepSurv significantly improved ASCVD risk prediction for MAE (AUC: 0.82 vs. 0.80, P ≤ 0.001) and mortality (AUC: 0.87 vs. 0.84, P ≤ 0.001) with traditional risk factors alone. Implementing non-categorical NRI, we noted a >40% increase in correct reclassification compared to the COXPH model for both MAE and mortality (P ≤ 0.05). Assessing the relative risk of participants, DeepSurv was the only learning algorithm to develop a significantly improved risk score criteria, which outcompeted COXPH for both MAE (4.22 vs. 3.61, P = 0.043) and mortality (6.81 vs. 5.52, P = 0.044). The addition of inflammatory or imaging biomarkers to traditional risk factors showed minimal/no significant improvement in model prediction. CONCLUSION DeepSurv can leverage simple office-based clinical features alone to accurately predict ASCVD risk and cardiovascular outcomes, without the need for additional features, such as inflammatory and imaging biomarkers.
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Affiliation(s)
- Quincy A Hathaway
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
| | - Naveena Yanamala
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA; Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Matthew J Budoff
- Lundquist Institute, Harbor-University of California, Los Angeles, Torrance, CA, USA
| | - Partho P Sengupta
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA; Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
| | - Irfan Zeb
- Heart and Vascular Institute, West Virginia University, Morgantown, WV, USA
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23
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Yanamala N, Krishna NH, Hathaway QA, Radhakrishnan A, Sunkara S, Patel H, Farjo P, Patel B, Sengupta PP. A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients. NPJ Digit Med 2021; 4:95. [PMID: 34088961 PMCID: PMC8178379 DOI: 10.1038/s41746-021-00467-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/06/2021] [Indexed: 11/19/2022] Open
Abstract
Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.
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Affiliation(s)
- Naveena Yanamala
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA. .,Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Nanda H Krishna
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA.,Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Quincy A Hathaway
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA
| | - Aditya Radhakrishnan
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA.,Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Srinidhi Sunkara
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA.,Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Heenaben Patel
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA
| | - Peter Farjo
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA
| | - Brijesh Patel
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA
| | - Partho P Sengupta
- Division of Cardiology, West Virginia University Medicine Heart & Vascular Institute, Morgantown, WV, USA.
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24
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Scala G, Delaval MN, Mukherjee SP, Federico A, Khaliullin TO, Yanamala N, Fatkhutdinova LM, Kisin ER, Greco D, Fadeel B, Shvedova AA. Multi-walled carbon nanotubes elicit concordant changes in DNA methylation and gene expression following long-term pulmonary exposure in mice. Carbon N Y 2021; 178:563-572. [PMID: 37206955 PMCID: PMC10193301 DOI: 10.1016/j.carbon.2021.03.045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Pulmonary exposure to multi-walled carbon nanotubes (MWCNTs) causes inflammation and fibrosis. Our previous work has shown that industrially produced MWCNTs trigger specific changes in gene expression in the lungs of exposed animals. To elucidate whether epigenetic effects play a role for these gene expression changes, we performed whole genome bisulphite sequencing to assess DNA methylation patterns in the lungs 56 days after exposure to MWCNTs. Lung tissues were also evaluated with respect to histopathological changes and cytokine profiling of bronchoalveolar lavage (BAL) fluid was conducted using a multi-plex array. Integrated analysis of transcriptomics data and DNA methylation data revealed concordant changes in gene expression. Functional analysis showed that the muscle contraction, immune system/inflammation, and extracellular matrix pathways were the most affected pathways. Taken together, the present study revealed that MWCNTs exert epigenetic effects in the lungs of exposed animals, potentially driving the subsequent gene expression changes.
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Affiliation(s)
- Giovanni Scala
- Department of Biology, University of Naples, Naples, Italy
| | - Mathilde N. Delaval
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sourav P. Mukherjee
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | | | - Naveena Yanamala
- Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA
| | - Liliya M. Fatkhutdinova
- Department of Hygiene and Occupational Medicine, Kazan State Medical University, Kazan, Russia
| | - Elena R. Kisin
- Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Corresponding author. (D. Greco)
| | - Bengt Fadeel
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Corresponding author. (B. Fadeel)
| | - Anna A. Shvedova
- Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA
- Department of Physiology and Pharmacology, West Virginia University, Morgantown, WV, USA
- Corresponding author. Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA. (A.A. Shvedova)
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25
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Pandey A, Kagiyama N, Yanamala N, Segar MW, Cho JS, Tokodi M, Sengupta PP. Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction. JACC Cardiovasc Imaging 2021; 14:1887-1900. [PMID: 34023263 DOI: 10.1016/j.jcmg.2021.04.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/03/2021] [Accepted: 04/01/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF). BACKGROUND The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise. METHODS The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate's Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346). RESULTS The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001). CONCLUSIONS This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone.
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Affiliation(s)
- Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Nobuyuki Kagiyama
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Department of Cardiovascular Biology and Medicine, Juntendo University, Tokyo, Japan; Department of Digital Health and Telemedicine R & D, Juntendo University, Tokyo, Japan
| | - Naveena Yanamala
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Matthew W Segar
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jung S Cho
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Division of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Márton Tokodi
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Heart and Vascular Center, Seemelweis University, Budapest, Hungary
| | - Partho P Sengupta
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA.
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Sengupta PP, Shrestha S, Kagiyama N, Hamirani Y, Kulkarni H, Yanamala N, Bing R, Chin CWL, Pawade TA, Messika-Zeitoun D, Tastet L, Shen M, Newby DE, Clavel MA, Pibarot P, Dweck MR. A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity. JACC Cardiovasc Imaging 2021; 14:1707-1720. [PMID: 34023273 DOI: 10.1016/j.jcmg.2021.03.020] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/02/2021] [Accepted: 03/16/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity. BACKGROUND In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty. METHODS Using echocardiography (ECHO) measurements (ECHO cohort, n = 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine-learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT cohort, n = 752) and cardiovascular magnetic resonance (CMR) imaging (CMR cohort, n = 160). The classifier's prognostic value was further validated using clinical outcomes (aortic valve replacement [AVR] and death) observed in the ECHO and CMR cohorts. RESULTS In 1,964 patients from the 3 multi-institutional cohorts, 1,346 (68%) subjects had either nonsevere or discordant AS severity. Machine learning identified 1,117 (57%) patients as having high-severity and 847 (43%) as having low-severity AS. High-severity patients in CT and CMR cohorts had higher valve calcium scores and left ventricular mass and fibrosis, respectively than the low-severity group. In the ECHO cohort, progression to AVR and progression to death in patients who did not receive AVR was faster in the high-severity group. Compared with the conventional classification of disease severity, machine-learning-based severity classification improved discrimination (integrated discrimination improvement: 0.07; 95% confidence interval: 0.02 to 0.12) and reclassification (net reclassification improvement: 0.17; 95% confidence interval: 0.11 to 0.23) for the outcome of AVR at 5 years. For both ECHO and CMR cohorts, we observed prognostic value of the machine-learning classifications for subgroups with asymptomatic, nonsevere or discordant AS. CONCLUSIONS Machine learning can integrate ECHO measurements to augment the classification of disease severity in most patients with AS, with major potential to optimize the timing of AVR.
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Affiliation(s)
- Partho P Sengupta
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA.
| | - Sirish Shrestha
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Nobuyuki Kagiyama
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Yasmin Hamirani
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Hemant Kulkarni
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; M&H Research, LLC, San Antonio, Texas, USA
| | - Naveena Yanamala
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Rong Bing
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Tania A Pawade
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Lionel Tastet
- Department of Medicine, Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, Laval University, Québec, Canada
| | - Mylène Shen
- Department of Medicine, Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, Laval University, Québec, Canada
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Marie-Annick Clavel
- Department of Medicine, Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, Laval University, Québec, Canada
| | - Phillippe Pibarot
- Department of Medicine, Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, Laval University, Québec, Canada.
| | - Marc R Dweck
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
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Hathaway Q, Yanamala N, Budoff M, Sengupta P, Zeby I. CARDIOVASCULAR RISK STRATIFICATION THROUGH DEEP NEURAL SURVIVAL NETWORKS - THE MULTI-ETHNIC STUDY OF ATHEROSCLEROSIS (MESA). J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)01920-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Yanamala N, Krishna NH, Hathaway Q, Radhakrishnan A, Sunkara S, Patel H, Farjo P, Patel B, Sengupta P. MACHINE-LEARNING CHARACTERIZATION OF SUBTLE HEMODYNAMIC INSTABILITY DIFFERENTIATES COVID-19 VERSUS SEASONAL INFLUENZA IN HOSPITALIZED PATIENTS. J Am Coll Cardiol 2021. [PMCID: PMC8091217 DOI: 10.1016/s0735-1097(21)04442-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Patel H, Yanamala N, Tokodi M, Kagiyama N, Piccirilli M, Shrestha S, Farjo P, Casaclang-Verzosa G, Tarhuni W, Nezarat N, Budoff M, Narula J, Sengupta P. SURFACE ECG-BASED MACHINE LEARNING MODEL FOR PREDICTING PATIENT SUBGROUP AT A HIGH RISK FOR MAJOR ADVERSE CARDIAC EVENTS. J Am Coll Cardiol 2021. [DOI: 10.1016/s0735-1097(21)04582-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Karunakaran KB, Yanamala N, Boyce G, Becich MJ, Ganapathiraju MK. Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions. Cancers (Basel) 2021; 13:1660. [PMID: 33916178 PMCID: PMC8037232 DOI: 10.3390/cancers13071660] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 03/19/2021] [Accepted: 03/22/2021] [Indexed: 12/20/2022] Open
Abstract
Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an 'MPM interactome' with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.
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Affiliation(s)
- Kalyani B. Karunakaran
- Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India;
| | - Naveena Yanamala
- Exposure Assessment Branch, National Institute of Occupational Safety and Health, Center for Disease Control, Morgantown, WV 26506, USA; (N.Y.); (G.B.)
| | - Gregory Boyce
- Exposure Assessment Branch, National Institute of Occupational Safety and Health, Center for Disease Control, Morgantown, WV 26506, USA; (N.Y.); (G.B.)
| | - Michael J. Becich
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206, USA;
| | - Madhavi K. Ganapathiraju
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15206, USA;
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Yanamala N, Krishna NH, Hathaway QA, Radhakrishnan A, Sunkara S, Patel H, Farjo P, Patel B, Sengupta PP. A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients. medRxiv 2021:2021.01.13.21249540. [PMID: 33469602 PMCID: PMC7814848 DOI: 10.1101/2021.01.13.21249540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.
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Brito D, Meester S, Yanamala N, Patel HB, Balcik BJ, Casaclang-Verzosa G, Seetharam K, Riveros D, Beto RJ, Balla S, Monseau AJ, Sengupta PP. High Prevalence of Pericardial Involvement in College Student Athletes Recovering From COVID-19. JACC Cardiovasc Imaging 2021; 14:541-555. [PMID: 33223496 PMCID: PMC7641597 DOI: 10.1016/j.jcmg.2020.10.023] [Citation(s) in RCA: 134] [Impact Index Per Article: 44.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 10/27/2020] [Accepted: 10/29/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVES This study sought to explore the spectrum of cardiac abnormalities in student athletes who returned to university campus in July 2020 with uncomplicated coronavirus disease 2019 (COVID-19). BACKGROUND There is limited information on cardiovascular involvement in young individuals with mild or asymptomatic COVID-19. METHODS Screening echocardiograms were performed in 54 consecutive student athletes (mean age 19 years; 85% male) who had positive results of reverse transcription polymerase chain reaction nasal swab testing of the upper respiratory tract or immunoglobulin G antibodies against severe acute respiratory syndrome coronavirus type 2. Sequential cardiac magnetic resonance imaging was performed in 48 (89%) subjects. RESULTS A total of 16 (30%) athletes were asymptomatic, whereas 36 (66%) and 2 (4%) athletes reported mild and moderate COVID-19 related symptoms, respectively. For the 48 athletes completing both imaging studies, abnormal findings were identified in 27 (56.3%) individuals. This included 19 (39.5%) athletes with pericardial late enhancements with associated pericardial effusion. Of the individuals with pericardial enhancements, 6 (12.5%) had reduced global longitudinal strain and/or an increased native T1. One patient showed myocardial enhancement, and reduced left ventricular ejection fraction or reduced global longitudinal strain with or without increased native T1 values was also identified in an additional 7 (14.6%) individuals. Native T2 findings were normal in all subjects, and no specific imaging features of myocardial inflammation were identified. Hierarchical clustering of left ventricular regional strain identified 3 unique myopericardial phenotypes that showed significant association with the cardiac magnetic resonance findings (p = 0.03). CONCLUSIONS More than 1 in 3 previously healthy college athletes recovering from COVID-19 infection showed imaging features of a resolving pericardial inflammation. Although subtle changes in myocardial structure and function were identified, no athlete showed specific imaging features to suggest an ongoing myocarditis. Further studies are needed to understand the clinical implications and long-term evolution of these abnormalities in uncomplicated COVID-19.
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Affiliation(s)
- Daniel Brito
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Scott Meester
- Department of Emergency Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Naveena Yanamala
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Heenaben B Patel
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Brenden J Balcik
- Department of Emergency Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Grace Casaclang-Verzosa
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Karthik Seetharam
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Diego Riveros
- Department of Emergency Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Robert James Beto
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Sudarshan Balla
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA
| | - Aaron J Monseau
- Department of Emergency Medicine, West Virginia University, Morgantown, West Virginia, USA
| | - Partho P Sengupta
- Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia, USA.
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Kagiyama N, Piccirilli M, Yanamala N, Shrestha S, Farjo PD, Casaclang-Verzosa G, Tarhuni WM, Nezarat N, Budoff MJ, Narula J, Sengupta PP. Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features. J Am Coll Cardiol 2021; 76:930-941. [PMID: 32819467 DOI: 10.1016/j.jacc.2020.06.061] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 06/25/2020] [Indexed: 01/14/2023]
Abstract
BACKGROUND Left ventricular (LV) diastolic dysfunction is recognized as playing a major role in the pathophysiology of heart failure; however, clinical tools for identifying diastolic dysfunction before echocardiography remain imprecise. OBJECTIVES This study sought to develop machine-learning models that quantitatively estimate myocardial relaxation using clinical and electrocardiography (ECG) variables as a first step in the detection of LV diastolic dysfunction. METHODS A multicenter prospective study was conducted at 4 institutions in North America enrolling a total of 1,202 subjects. Patients from 3 institutions (n = 814) formed an internal cohort and were randomly divided into training and internal test sets (80:20). Machine-learning models were developed using signal-processed ECG, traditional ECG, and clinical features and were tested using the test set. Data from the fourth institution was reserved as an external test set (n = 388) to evaluate the model generalizability. RESULTS Despite diversity in subjects, the machine-learning model predicted the quantitative values of the LV relaxation velocities (e') measured by echocardiography in both internal and external test sets (mean absolute error: 1.46 and 1.93 cm/s; adjusted R2 = 0.57 and 0.46, respectively). Analysis of the area under the receiver operating characteristic curve (AUC) revealed that the estimated e' discriminated the guideline-recommended thresholds for abnormal myocardial relaxation and diastolic and systolic dysfunction (LV ejection fraction) the internal (area under the curve [AUC]: 0.83, 0.76, and 0.75) and external test sets (0.84, 0.80, and 0.81), respectively. Moreover, the estimated e' allowed prediction of LV diastolic dysfunction based on multiple age- and sex-adjusted reference limits (AUC: 0.88 and 0.94 in the internal and external sets, respectively). CONCLUSIONS A quantitative prediction of myocardial relaxation can be performed using easily obtained clinical and ECG features. This cost-effective strategy may be a valuable first clinical step for assessing the presence of LV dysfunction and may potentially aid in the early diagnosis and management of heart failure patients.
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Affiliation(s)
- Nobuyuki Kagiyama
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia. https://twitter.com/KagiyamaNobu
| | - Marco Piccirilli
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia
| | - Naveena Yanamala
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia; Institute for Software Research, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Sirish Shrestha
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia
| | - Peter D Farjo
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia
| | - Grace Casaclang-Verzosa
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia
| | | | - Negin Nezarat
- Lundquist Institute, Department of Medicine, Harbor-UCLA Medical Center, Torrance California
| | - Matthew J Budoff
- Lundquist Institute, Department of Medicine, Harbor-UCLA Medical Center, Torrance California
| | - Jagat Narula
- Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Partho P Sengupta
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia.
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Khaliullin TO, Kisin ER, Guppi S, Yanamala N, Zhernovkov V, Shvedova AA. Differential responses of murine alveolar macrophages to elongate mineral particles of asbestiform and non-asbestiform varieties: Cytotoxicity, cytokine secretion and transcriptional changes. Toxicol Appl Pharmacol 2020; 409:115302. [PMID: 33148505 DOI: 10.1016/j.taap.2020.115302] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 09/26/2020] [Revised: 10/17/2020] [Accepted: 10/21/2020] [Indexed: 01/19/2023]
Abstract
Human exposures to asbestiform elongate mineral particles (EMP) may lead to diffuse fibrosis, lung cancer, malignant mesothelioma and autoimmune diseases. Cleavage fragments (CF) are chemically identical to asbestiform varieties (or habits) of the parent mineral, but no consensus exists on whether to treat them as asbestos from toxicological and regulatory standpoints. Alveolar macrophages (AM) are the first responders to inhaled particulates, participating in clearance and activating other resident and recruited immunocompetent cells, impacting the long-term outcomes. In this study we address how EMP of asbestiform versus non-asbestiform habit affect AM responses. Max Planck Institute (MPI) cells, a non-transformed mouse line that has an AM phenotype and genotype, were treated with mass-, surface area- (s.a.), and particle number- (p.n.) equivalent concentrations of respirable asbestiform and non-asbestiform riebeckite/tremolite EMP for 24 h. Cytotoxicity, cytokines secretion and transcriptional changes were evaluated. At the equal mass, asbestiform EMP were more cytotoxic, however EMP of both habits induced similar LDH leakage and decrease in viability at s.a. and p.n. equivalent doses. DNA damage assessment and cell cycle analysis revealed differences in the modes of cell death between asbestos and respective CF. There was an increase in chemokines, but not pro-inflammatory cytokines after all EMP treatments. Principal component analysis of the cytokine secretion showed close clustering for the s.a. and p.n. equivalent treatments. There were mineral- and habit-specific patterns of gene expression dysregulation at s.a. equivalent doses. Our study reveals the critical nature of EMP morphometric parameters for exposure assessment and dosing approaches used in toxicity studies.
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Affiliation(s)
- T O Khaliullin
- West Virginia University, Morgantown, WV, United States of America; HELD, NIOSH, CDC, Morgantown, WV, United States of America.
| | - E R Kisin
- HELD, NIOSH, CDC, Morgantown, WV, United States of America.
| | - S Guppi
- HELD, NIOSH, CDC, Morgantown, WV, United States of America.
| | - N Yanamala
- West Virginia University, Morgantown, WV, United States of America; Carnegie Mellon University, Pittsburgh, PA, United States of America.
| | | | - A A Shvedova
- West Virginia University, Morgantown, WV, United States of America; HELD, NIOSH, CDC, Morgantown, WV, United States of America.
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Fraser K, Kodali V, Yanamala N, Birch ME, Cena L, Casuccio G, Bunker K, Lersch TL, Evans DE, Stefaniak A, Hammer MA, Kashon ML, Boots T, Eye T, Hubczak J, Friend SA, Dahm M, Schubauer-Berigan MK, Siegrist K, Lowry D, Bauer AK, Sargent LM, Erdely A. Physicochemical characterization and genotoxicity of the broad class of carbon nanotubes and nanofibers used or produced in U.S. facilities. Part Fibre Toxicol 2020; 17:62. [PMID: 33287860 PMCID: PMC7720492 DOI: 10.1186/s12989-020-00392-w] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/18/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Carbon nanotubes and nanofibers (CNT/F) have known toxicity but simultaneous comparative studies of the broad material class, especially those with a larger diameter, with computational analyses linking toxicity to their fundamental material characteristics was lacking. It was unclear if all CNT/F confer similar toxicity, in particular, genotoxicity. Nine CNT/F (MW #1-7 and CNF #1-2), commonly found in exposure assessment studies of U.S. facilities, were evaluated with reported diameters ranging from 6 to 150 nm. All materials were extensively characterized to include distributions of physical dimensions and prevalence of bundled agglomerates. Human bronchial epithelial cells were exposed to the nine CNT/F (0-24 μg/ml) to determine cell viability, inflammation, cellular oxidative stress, micronuclei formation, and DNA double-strand breakage. Computational modeling was used to understand various permutations of physicochemical characteristics and toxicity outcomes. RESULTS Analyses of the CNT/F physicochemical characteristics illustrate that using detailed distributions of physical dimensions provided a more consistent grouping of CNT/F compared to using particle dimension means alone. In fact, analysis of binning of nominal tube physical dimensions alone produced a similar grouping as all characterization parameters together. All materials induced epithelial cell toxicity and micronuclei formation within the dose range tested. Cellular oxidative stress, DNA double strand breaks, and micronuclei formation consistently clustered together and with larger physical CNT/F dimensions and agglomerate characteristics but were distinct from inflammatory protein changes. Larger nominal tube diameters, greater lengths, and bundled agglomerate characteristics were associated with greater severity of effect. The portion of tubes with greater nominal length and larger diameters within a sample was not the majority in number, meaning a smaller percentage of tubes with these characteristics was sufficient to increase toxicity. Many of the traditional physicochemical characteristics including surface area, density, impurities, and dustiness did not cluster with the toxicity outcomes. CONCLUSION Distributions of physical dimensions provided more consistent grouping of CNT/F with respect to toxicity outcomes compared to means only. All CNT/F induced some level of genotoxicity in human epithelial cells. The severity of toxicity was dependent on the sample containing a proportion of tubes with greater nominal lengths and diameters.
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Affiliation(s)
- Kelly Fraser
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - Vamsi Kodali
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - Naveena Yanamala
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - M. Eileen Birch
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Cincinnati, OH USA
| | | | | | | | | | - Douglas E. Evans
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Cincinnati, OH USA
| | - Aleksandr Stefaniak
- Repiratory Health Division, National Institute for Occupational Safety and Health, Morgantown, WV USA
| | - Mary Ann Hammer
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Michael L. Kashon
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Theresa Boots
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Tracy Eye
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - John Hubczak
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
| | - Sherri A. Friend
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Matthew Dahm
- Division of Field Studies Evaluation, National Institute for Occupational Safety and Health, Cincinnati, OH USA
| | - Mary K. Schubauer-Berigan
- Division of Field Studies Evaluation, National Institute for Occupational Safety and Health, Cincinnati, OH USA
- International Agency for Research on Cancer, Lyon, France
| | - Katelyn Siegrist
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - David Lowry
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Alison K. Bauer
- Department of Environmental and Occupational Health, University of Colorado Anschutz Medical Campus, Aurora, CO USA
| | - Linda M. Sargent
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
| | - Aaron Erdely
- Health Effect Laboratory Division, National Institute for Occupational Safety and Health, 1095 Willowdale Rd, MS-2015, Morgantown, WV 26505-2888 USA
- West Virginia University, Morgantown, WV USA
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Sager TM, Umbright CM, Mustafa GM, Yanamala N, Leonard HD, McKinney WG, Kashon ML, Joseph P. Tobacco Smoke Exposure Exacerbated Crystalline Silica-Induced Lung Toxicity in Rats. Toxicol Sci 2020; 178:375-390. [PMID: 32976597 PMCID: PMC7825013 DOI: 10.1093/toxsci/kfaa146] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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] [Indexed: 11/13/2022] Open
Abstract
Smoking may modify the lung response to silica exposure including cancer and silicosis. Nevertheless, the precise role of exposure to tobacco smoke (TS) on the lung response to crystalline silica (CS) exposure and the underlying mechanisms need further clarification. The objectives of the present study were to determine the role of TS on lung response to CS exposure and the underlying mechanism(s). Male Fischer 344 rats were exposed by inhalation to air, CS (15 mg/m3, 6 h/day, 5 days), TS (80 mg/m3, 3 h/day, twice weekly, 6 months), or CS (15 mg/m3, 6 h/day, 5 days) followed by TS (80 mg/m3, 3 h/day, twice weekly, 6 months). The rats were euthanized 6 months and 3 weeks following initiation of the first exposure and the lung response was assessed. Silica exposure resulted in significant lung toxicity as evidenced by lung histological changes, enhanced neutrophil infiltration, increased lactate dehydrogenase levels, enhanced oxidant production, and increased cytokine levels. The TS exposure alone had only a minimal effect on these toxicity parameters. However, the combined exposure to TS and CS exacerbated the lung response, compared with TS or CS exposure alone. Global gene expression changes in the lungs correlated with the lung toxicity severity. Bioinformatic analysis of the gene expression data demonstrated significant enrichment in functions, pathways, and networks relevant to the response to CS exposure which correlated with the lung toxicity detected. Collectively our data demonstrated an exacerbation of CS-induced lung toxicity by TS exposure and the molecular mechanisms underlying the exacerbated toxicity.
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Affiliation(s)
- Tina M Sager
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Christina M Umbright
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Gul Mehnaz Mustafa
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Naveena Yanamala
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Howard D Leonard
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Walter G McKinney
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Michael L Kashon
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
| | - Pius Joseph
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health (NIOSH), Morgantown, West Virginia 26505
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Farjo PD, Yanamala N, Kagiyama N, Patel HB, Casaclang-Verzosa G, Nezarat N, Budoff MJ, Sengupta PP. Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study. ACTA ACUST UNITED AC 2020; 1:51-61. [PMID: 37056293 PMCID: PMC10087019 DOI: 10.1093/ehjdh/ztaa008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/06/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
Abstract
Aims
Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.
Methods and results
In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.
Conclusion
ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.
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Affiliation(s)
- Peter D Farjo
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Naveena Yanamala
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
- Institute for Software Research, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
| | - Nobuyuki Kagiyama
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
- Department of Digital Health and Telemedicine R&D, Juntendo University, 211 Hongo, Bunkyo City, Tokyo 113-8421, Japan
- Department of Cardiovascular Biology and Medicine, Juntendo University, 211 Hongo, Bunkyo City, Tokyo 113-8421, Japan
| | - Heenaben B Patel
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Grace Casaclang-Verzosa
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Negin Nezarat
- Department of Medicine, Lundquist Institute, Harbor-UCLA Medical Center, 1124 West Carson St, Torrance, CA 90502, USA
| | - Matthew J Budoff
- Department of Medicine, Lundquist Institute, Harbor-UCLA Medical Center, 1124 West Carson St, Torrance, CA 90502, USA
| | - Partho P Sengupta
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
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Sengupta PP, Shrestha S, Berthon B, Messas E, Donal E, Tison GH, Min JK, D'hooge J, Voigt JU, Dudley J, Verjans JW, Shameer K, Johnson K, Lovstakken L, Tabassian M, Piccirilli M, Pernot M, Yanamala N, Duchateau N, Kagiyama N, Bernard O, Slomka P, Deo R, Arnaout R. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council. JACC Cardiovasc Imaging 2020; 13:2017-2035. [PMID: 32912474 PMCID: PMC7953597 DOI: 10.1016/j.jcmg.2020.07.015] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [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: 12/18/2019] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/20/2022]
Abstract
Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.
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Affiliation(s)
- Partho P Sengupta
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia.
| | - Sirish Shrestha
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Béatrice Berthon
- Physique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France
| | - Emmanuel Messas
- Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Erwan Donal
- Département de Cardiologie et Maladies Vasculaires, Service de Cardiologie et maladies vasculaires, CHU Rennes, Rennes, France
| | - Geoffrey H Tison
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| | | | - Jan D'hooge
- Laboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Jens-Uwe Voigt
- Department of Cardiovascular Science, KU Leuven, Leuven, Belgium; Department of Cardiovascular Diseases, University Hospitals Leuven, Belgium
| | - Joel Dudley
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Johan W Verjans
- Australian Institute for Machine Learning, University of Adelaide, North Terrace, Adelaide, South Australia, Australia; Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Khader Shameer
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kipp Johnson
- Department of Genetics and Genomic Sciences and Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York; Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Lasse Lovstakken
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Mahdi Tabassian
- Laboratory on Cardiovascular Imaging and Dynamics, Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
| | - Marco Piccirilli
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Mathieu Pernot
- Physique pour la Médecine Paris, Inserm U1273, CNRS FRE 2031, ESPCI Paris, PSL Research University, Paris, France
| | - Naveena Yanamala
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Nicolas Duchateau
- CREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France
| | - Nobuyuki Kagiyama
- West Virginia University Heart and Vascular Institute, Division of Cardiology, Morgantown, West Virginia
| | - Olivier Bernard
- CREATIS, CNRS UMR 5220, INSERM U1206, Université Lyon 1, INSA-LYON, France
| | - Piotr Slomka
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Rahul Deo
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
| | - Rima Arnaout
- Division of Cardiology, Department of Medicine, University of California San Francisco, San Francisco, California
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Abstract
BACKGROUND Recent decision rules for the management of febrile infants support the identification of infants at higher risk of serious bacterial infections (SBIs) without the performance of routine lumbar puncture. We derive and validate a model to identify febrile infants ≤60 days of age at low risk for SBIs using supervised machine learning approaches. METHODS We conducted a secondary analysis of a multicenter prospective study performed between December 2008 and May 2013 of febrile infants. Our outcome was SBI, (culture-positive urinary tract infection, bacteremia, and/or bacterial meningitis). We developed and validated 4 supervised learning models: logistic regression, random forest, support vector machine, and a single-hidden layer neural network. RESULTS A total of 1470 patients were included (1014 >28 days old). One hundred thirty-eight (9.3%) had SBIs (122 urinary tract infections, 20 bacteremia, and 8 meningitis; 11 with concurrent SBIs). Using 4 features (urinalysis, white blood cell count, absolute neutrophil count, and procalcitonin), we demonstrated with the random forest model the highest specificity (74.9, 95% confidence interval: 71.5%-78.2%) with a sensitivity of 98.6% (95% confidence interval: 92.2%-100.0%) in the validation cohort. One patient with bacteremia was misclassified. Among 1240 patients who received a lumbar puncture, this model could have prevented 849 (68.5%) such procedures. CONCLUSIONS We derived and internally validated a supervised learning model for the risk-stratification of febrile infants. Although computationally complex, lacking parameter cutoffs, and in need of external validation, this strategy may allow for reductions in unnecessary procedures, hospitalizations, and antibiotics while maintaining excellent sensitivity.
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Affiliation(s)
- Sriram Ramgopal
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago and Feinberg School of Medicine, Northwestern University, Chicago, Illinois;
| | - Christopher M Horvat
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Division of Health Informatics, Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania; and
| | - Naveena Yanamala
- Institute for Software Research, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Elizabeth R Alpern
- Division of Emergency Medicine, Department of Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago and Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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40
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Tarasov SA, Gorbunov EA, Don ES, Emelyanova AG, Kovalchuk AL, Yanamala N, Schleker ASS, Klein-Seetharaman J, Groenestein R, Tafani JP, van der Meide P, Epstein OI. Insights into the Mechanism of Action of Highly Diluted Biologics. J Immunol 2020; 205:1345-1354. [PMID: 32727888 DOI: 10.4049/jimmunol.2000098] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 07/04/2020] [Indexed: 12/12/2022]
Abstract
The therapeutic use of Abs in cancer, autoimmunity, transplantation, and other fields is among the major biopharmaceutical advances of the 20th century. Broader use of Ab-based drugs is constrained because of their high production costs and frequent side effects. One promising approach to overcome these limitations is the use of highly diluted Abs, which are produced by gradual reduction of an Ab concentration to an extremely low level. This technology was used to create a group of drugs for the treatment of various diseases, depending on the specificity of the used Abs. Highly diluted Abs to IFN-γ (hd-anti-IFN-γ) have been demonstrated to be efficacious against influenza and other respiratory infections in a variety of preclinical and clinical studies. In the current study, we provide evidence for a possible mechanism of action of hd-anti-IFN-γ. Using high-resolution solution nuclear magnetic resonance spectroscopy, we show that the drug induced conformational changes in the IFN-γ molecule. Chemical shift changes occurred in the amino acids located primarily at the dimer interface and at the C-terminal region of IFN-γ. These molecular changes could be crucial for the function of the protein, as evidenced by an observed hd-anti-IFN-γ-induced increase in the specific binding of IFN-γ to its receptor in U937 cells, enhanced induced production of IFN-γ in human PBMC culture, and increased survival of influenza A-infected mice.
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Affiliation(s)
- Sergey A Tarasov
- OOO "NPF "Materia Medica Holding," 127473 Moscow, Russian Federation.,The Institute of General Pathology and Pathophysiology, 125315 Moscow, Russian Federation
| | | | - Elena S Don
- OOO "NPF "Materia Medica Holding," 127473 Moscow, Russian Federation.,The Institute of General Pathology and Pathophysiology, 125315 Moscow, Russian Federation
| | - Alexandra G Emelyanova
- OOO "NPF "Materia Medica Holding," 127473 Moscow, Russian Federation.,The Institute of General Pathology and Pathophysiology, 125315 Moscow, Russian Federation
| | | | - Naveena Yanamala
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260
| | - A Sylvia S Schleker
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260
| | - Judith Klein-Seetharaman
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260
| | | | | | | | - Oleg I Epstein
- OOO "NPF "Materia Medica Holding," 127473 Moscow, Russian Federation.,The Institute of General Pathology and Pathophysiology, 125315 Moscow, Russian Federation
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Kisin ER, Yanamala N, Rodin D, Menas A, Farcas M, Russo M, Guppi S, Khaliullin TO, Iavicoli I, Harper M, Star A, Kagan VE, Shvedova AA. Enhanced morphological transformation of human lung epithelial cells by continuous exposure to cellulose nanocrystals. Chemosphere 2020; 250:126170. [PMID: 32114335 PMCID: PMC7750788 DOI: 10.1016/j.chemosphere.2020.126170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 01/30/2020] [Accepted: 02/09/2020] [Indexed: 05/06/2023]
Abstract
Cellulose nanocrystals (CNC), also known as nanowhiskers, have recently gained much attention due to their biodegradable nature, advantageous chemical and mechanical properties, economic value and renewability thus making them attractive for a wide range of applications. However, before these materials can be considered for potential uses, investigation of their toxicity is prudent. Although CNC exposures are associated with pulmonary inflammation and damage as well as oxidative stress responses and genotoxicity in vivo, studies evaluating cell transformation or tumorigenic potential of CNC's were not previously conducted. In this study, we aimed to assess the neoplastic-like transformation potential of two forms of CNC derived from wood (powder and gel) in human pulmonary epithelial cells (BEAS-2B) in comparison to fibrous tremolite (TF), known to induce lung cancer. Short-term exposure to CNC or TF induced intracellular ROS increase and DNA damage while long-term exposure resulted in neoplastic-like transformation demonstrated by increased cell proliferation, anchorage-independent growth, migration and invasion. The increased proliferative responses were also in-agreement with observed levels of pro-inflammatory cytokines. Based on the hierarchical clustering analysis (HCA) of the inflammatory cytokine responses, CNC powder was segregated from the control and CNC-gel samples. This suggests that CNC may have the ability to influence neoplastic-like transformation events in pulmonary epithelial cells and that such effects are dependent on the type/form of CNC. Further studies focusing on determining and understanding molecular mechanisms underlying potential CNC cell transformation events and their likelihood to induce tumorigenic effects in vivo are highly warranted.
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Affiliation(s)
- E R Kisin
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA
| | - N Yanamala
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA
| | - D Rodin
- Institute for Personalized and Translational Medicine, Ariel University, Ariel, Israel
| | - A Menas
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA
| | - M Farcas
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA
| | - M Russo
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA; Institute of Public Health, Section of Occupational Medicine, Catholic University of the Sacred Heart, Rome, Italy
| | - S Guppi
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA
| | - T O Khaliullin
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA; Department of Physiology & Pharmacology, WVU, Morgantown, WV, USA
| | - I Iavicoli
- Department of Public Health, University of Naples Federico II, Naples, Italy
| | - M Harper
- Zefon International, Ocala, FL, USA
| | - A Star
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
| | - V E Kagan
- Department of Environmental & Occupational Health, University of Pittsburgh, Pittsburgh, PA, USA; Center for Free Radical and Antioxidant Health, University of Pittsburgh, Pittsburgh, PA, USA; Laboratory of Navigational Redox Lipidomics, IM Sechenov Moscow State Medical University, Moscow, Russian Federation
| | - A A Shvedova
- EAB, HELD, NIOSH, CDC, Morgantown, WV, USA; Department of Physiology & Pharmacology, WVU, Morgantown, WV, USA.
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42
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Khaliullin TO, Yanamala N, Newman MS, Kisin ER, Fatkhutdinova LM, Shvedova AA. Comparative analysis of lung and blood transcriptomes in mice exposed to multi-walled carbon nanotubes. Toxicol Appl Pharmacol 2020; 390:114898. [PMID: 31978390 DOI: 10.1016/j.taap.2020.114898] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 12/23/2019] [Revised: 01/16/2020] [Accepted: 01/21/2020] [Indexed: 12/16/2022]
Abstract
Pulmonary exposure to multi-walled carbon nanotubes (MWCNT) causes inflammation, fibroproliferation, immunotoxicity, and systemic responses in rodents. However, the search for representative biomarkers of exposure is an ongoing endeavor. Whole blood gene expression profiling is a promising new approach for the identification of novel disease biomarkers. We asked if the whole blood transcriptome reflects pathology-specific changes in lung gene expression caused by MWCNT. To answer this question, we performed mRNA sequencing analysis of the whole blood and lung in mice administered MWCNT or vehicle solution via pharyngeal aspiration and sacrificed 56 days later. The pattern of lung mRNA expression as determined using Ingenuity Pathway Analysis (IPA) was indicative of continued inflammation, immune cell trafficking, phagocytosis, and adaptive immune responses. Simultaneously, innate immunity-related transcripts (Plunc, Bpifb1, Reg3g) and cancer-related pathways were downregulated. IPA analysis of the differentially expressed genes in the whole blood suggested increased hematopoiesis, predicted activation of cancer/tumor development pathways, and atopy. There were several common upregulated genes between whole blood and lungs, important for adaptive immune responses: Cxcr1, Cd72, Sharpin, and Slc11a1. Trim24, important for TH2 cell effector function, was downregulated in both datasets. Hla-dqa1 mRNA was upregulated in the lungs and downregulated in the blood, as was Lilrb4, which controls the reactivity of immune response. "Cancer" disease category had opposing activation status in the two datasets, while the only commonality was "Hypersensitivity". Transcriptome changes occurring in the lungs did not produce a completely replicable pattern in whole blood; however, specific systemic responses may be shared between transcriptomic profiles.
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Affiliation(s)
- Timur O Khaliullin
- Department of Physiology and Pharmacology, West Virginia University, Morgantown, WV, USA; Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA.
| | - Naveena Yanamala
- Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA.
| | - Mackenzie S Newman
- Department of Physiology and Pharmacology, West Virginia University, Morgantown, WV, USA.
| | - Elena R Kisin
- Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA.
| | - Liliya M Fatkhutdinova
- Department of Hygiene and Occupational Medicine, Kazan State Medical University, Kazan, Russia
| | - Anna A Shvedova
- Department of Physiology and Pharmacology, West Virginia University, Morgantown, WV, USA; Health Effects Laboratory Division, NIOSH, CDC, Morgantown, WV, USA.
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Mitchell J, Balem F, Tirupula K, Man D, Dhiman HK, Yanamala N, Ollesch J, Planas-Iglesias J, Jennings BJ, Gerwert K, Iannaccone A, Klein-Seetharaman J. Correction: Comparison of the molecular properties of retinitis pigmentosa P23H and N15S amino acid replacements in rhodopsin. PLoS One 2019; 14:e0225153. [PMID: 31697785 PMCID: PMC6837281 DOI: 10.1371/journal.pone.0225153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Yanamala N, Desai IC, Miller W, Kodali VK, Syamlal G, Roberts JR, Erdely AD. Grouping of carbonaceous nanomaterials based on association of patterns of inflammatory markers in BAL fluid with adverse outcomes in lungs. Nanotoxicology 2019; 13:1102-1116. [DOI: 10.1080/17435390.2019.1640911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Naveena Yanamala
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
| | - Ishika C. Desai
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
- Department of Molecular Genetics, The Ohio State University, Columbus, OH, USA
| | - William Miller
- Respiratory Health Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
| | - Vamsi K. Kodali
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
| | - Girija Syamlal
- Respiratory Health Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
| | - Jenny R. Roberts
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
| | - Aaron D. Erdely
- Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV, USA
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Mitchell J, Balem F, Tirupula K, Man D, Dhiman HK, Yanamala N, Ollesch J, Planas-Iglesias J, Jennings BJ, Gerwert K, Iannaccone A, Klein-Seetharaman J. Comparison of the molecular properties of retinitis pigmentosa P23H and N15S amino acid replacements in rhodopsin. PLoS One 2019; 14:e0214639. [PMID: 31100078 PMCID: PMC6524802 DOI: 10.1371/journal.pone.0214639] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2018] [Accepted: 03/19/2019] [Indexed: 12/16/2022] Open
Abstract
Mutations in the RHO gene encoding for the visual pigment protein, rhodopsin, are among the most common cause of autosomal dominant retinitis pigmentosa (ADRP). Previous studies of ADRP mutations in different domains of rhodopsin have indicated that changes that lead to more instability in rhodopsin structure are responsible for more severe disease in patients. Here, we further test this hypothesis by comparing side-by-side and therefore quantitatively two RHO mutations, N15S and P23H, both located in the N-terminal intradiscal domain. The in vitro biochemical properties of these two rhodopsin proteins, expressed in stably transfected tetracycline-inducible HEK293S cells, their UV-visible absorption, their Fourier transform infrared, circular dichroism and Metarhodopsin II fluorescence spectroscopy properties were characterized. As compared to the severely impaired P23H molecular function, N15S is only slightly defective in structure and stability. We propose that the molecular basis for these structural differences lies in the greater distance of the N15 residue as compared to P23 with respect to the predicted rhodopsin folding core. As described previously for WT rhodopsin, addition of the cytoplasmic allosteric modulator chlorin e6 stabilizes especially the P23H protein, suggesting that chlorin e6 may be generally beneficial in the rescue of those ADRP rhodopsin proteins whose stability is affected by amino acid replacement.
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Affiliation(s)
- James Mitchell
- Division of Biomedical Sciences, Medical School, University of Warwick, Coventry, United Kingdom
| | - Fernanda Balem
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Kalyan Tirupula
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - David Man
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Harpreet Kaur Dhiman
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Naveena Yanamala
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
| | - Julian Ollesch
- Department of Biophysics, Ruhr-University Bochum, Bochum, Germany
| | - Joan Planas-Iglesias
- Division of Biomedical Sciences, Medical School, University of Warwick, Coventry, United Kingdom
| | - Barbara J Jennings
- Retinal Degeneration & Ophthalmic Genetics Service & Lions Visual Function Diagnostic Lab, Hamilton Eye Institute, Dept. Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Klaus Gerwert
- Department of Biophysics, Ruhr-University Bochum, Bochum, Germany
| | - Alessandro Iannaccone
- Retinal Degeneration & Ophthalmic Genetics Service & Lions Visual Function Diagnostic Lab, Hamilton Eye Institute, Dept. Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
| | - Judith Klein-Seetharaman
- Division of Biomedical Sciences, Medical School, University of Warwick, Coventry, United Kingdom
- Department of Structural Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America
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Mitchell J, Yanamala N, Tan YL, Gardner EE, Tirupula KC, Balem F, Sheves M, Nietlispach D, Klein‐Seetharaman J. Structural and Functional Consequences of the Weak Binding of Chlorin e6 to Bovine Rhodopsin. Photochem Photobiol 2019; 95:787-802. [DOI: 10.1111/php.13074] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 12/07/2018] [Indexed: 12/14/2022]
Affiliation(s)
- James Mitchell
- Biomedical Sciences Division Warwick Medical School University of Warwick Coventry UK
| | - Naveena Yanamala
- Department of Structural Biology School of Medicine University of Pittsburgh Pittsburgh PA
| | - Yi Lei Tan
- Department of Biochemistry University of Cambridge Cambridge UK
| | - Eric E. Gardner
- Department of Structural Biology School of Medicine University of Pittsburgh Pittsburgh PA
| | - Kalyan C. Tirupula
- Department of Structural Biology School of Medicine University of Pittsburgh Pittsburgh PA
| | - Fernanda Balem
- Department of Structural Biology School of Medicine University of Pittsburgh Pittsburgh PA
| | - Mordechai Sheves
- Organic Chemistry Department Weizmann Institute of Science Rehovot Israel
| | | | - Judith Klein‐Seetharaman
- Biomedical Sciences Division Warwick Medical School University of Warwick Coventry UK
- Department of Structural Biology School of Medicine University of Pittsburgh Pittsburgh PA
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Khaliullin TO, Kisin ER, Yanamala N, Guppi S, Harper M, Lee T, Shvedova AA. Comparative cytotoxicity of respirable surface-treated/untreated calcium carbonate rock dust particles in vitro. Toxicol Appl Pharmacol 2018; 362:67-76. [PMID: 30393145 DOI: 10.1016/j.taap.2018.10.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [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: 09/07/2018] [Revised: 10/20/2018] [Accepted: 10/23/2018] [Indexed: 01/17/2023]
Abstract
Calcium carbonate rock dust (RD) is used in mining to reduce the explosivity of aerosolized coal. During the dusting procedures, potential for human exposure occurs, raising health concerns. To improve RD aerosolization, several types of anti-caking surface treatments exist. The aim of the study was to evaluate cytotoxicity of four respirable RD samples: untreated/treated limestone (UL/TL), untreated/treated marble (UM/TM), and crystalline silica (SiO2) as a positive control in A549 and THP-1 transformed human cell lines. Respirable fractions were generated and collected using FSP10 high flow-rate cyclone samplers. THP-1 cells were differentiated with phorbol-12-myristate-13-acetate (20 ng/ml, 48 h). Cells were exposed to seven different concentrations of RD and SiO2 (0-0.2 mg/ml). RD caused a slight decrease in viability at 24 or 72 h post-exposure and were able to induce inflammatory cytokine production in A549 cells, however, with considerably less potency than SiO2. In THP-1 cells at 24 h, there was significant dose-dependent lactate dehydrogenase, inflammatory cytokine and chemokine release. Caspase-1 activity was increased in SiO2- and, on a lesser scale, in TM- exposed cells. To test if the increased toxicity of TM was uptake-related, THP-1 cells were pretreated with Cytochalasin D (CytD) or Bafilomycin A (BafA), followed by exposure to RD or SiO2 for 6 h. CytD blocked the uptake and significantly decreased cytotoxicity of all particles, while BafA prevented caspase-1 activation but not cytotoxic effects of TM. Only TM was able to induce an inflammatory response in THP-1 cells, however it was much less pronounced compared to silica.
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Affiliation(s)
- Timur O Khaliullin
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Health Effects Laboratory Division, Exposure Assessment Branch, 1095 Willowdale road, Morgantown, WV 26505, USA; West Virginia University, Department of Physiology and Pharmacology, PO Box 9229, Morgantown, WV, USA.
| | - Elena R Kisin
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Health Effects Laboratory Division, Exposure Assessment Branch, 1095 Willowdale road, Morgantown, WV 26505, USA.
| | - Naveena Yanamala
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Health Effects Laboratory Division, Exposure Assessment Branch, 1095 Willowdale road, Morgantown, WV 26505, USA.
| | - Supraja Guppi
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Health Effects Laboratory Division, Exposure Assessment Branch, 1095 Willowdale road, Morgantown, WV 26505, USA.
| | - Martin Harper
- Zefon International, 5350 SW 1st Lane, Ocala, FL 34474, USA.
| | - Taekhee Lee
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Pittsburgh Mining Research Division, 626 Cochrans Mill Road, Pittsburgh, PA 15236, USA.
| | - Anna A Shvedova
- Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Health Effects Laboratory Division, Exposure Assessment Branch, 1095 Willowdale road, Morgantown, WV 26505, USA; West Virginia University, Department of Physiology and Pharmacology, PO Box 9229, Morgantown, WV, USA.
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Park EJ, Khaliullin TO, Shurin MR, Kisin ER, Yanamala N, Fadeel B, Chang J, Shvedova AA. Fibrous nanocellulose, crystalline nanocellulose, carbon nanotubes, and crocidolite asbestos elicit disparate immune responses upon pharyngeal aspiration in mice. J Immunotoxicol 2018; 15:12-23. [PMID: 29237319 DOI: 10.1080/1547691x.2017.1414339] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
With the rapid development of synthetic alternatives to mineral fibers, their possible effects on the environment and human health have become recognized as important issues worldwide. This study investigated effects of four fibrous materials, i.e. nanofibrillar/nanocrystalline celluloses (NCF and CNC), single-walled carbon nanotubes (CNTs), and crocidolite asbestos (ASB), on pulmonary inflammation and immune responses found in the lungs, as well as the effects on spleen and peripheral blood immune cell subsets. BALB/c mice were given NCF, CNC, CNT, and ASB on Day 1 by oropharyngeal aspiration. At 14 days post-exposure, the animals were evaluated. Total cell number, mononuclear phagocytes, polymorphonuclear leukocytes, lymphocytes, and LDH levels were significantly increased in ASB and CNT-exposed mice. Expression of cytokines and chemokines in bronchoalveolar lavage (BAL) was quite different in mice exposed to four particle types, as well as expression of antigen presentation-related surface proteins on BAL cells. The results revealed that pulmonary exposure to fibrous materials led to discrete local immune cell polarization patterns with a TH2-like response caused by ASB and TH1-like immune reaction to NCF, while CNT and CNC caused non-classical or non-uniform responses. These alterations in immune response following pulmonary exposure should be taken into account when testing the applicability of new nanosized materials with fibrous morphology.
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Affiliation(s)
- Eun-Jung Park
- a Department of Brain Science , Ajou University School of Medicine , Suwon , Republic of Korea
| | - Timur O Khaliullin
- b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV , USA.,c Department of Physiology, Pharmacology and Neuroscience , West Virginia University , Morgantown , WV , USA
| | - Michael R Shurin
- d Department of Pathology and Immunology , University of Pittsburgh , Pittsburgh , PA , USA
| | - Elena R Kisin
- b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV , USA
| | - Naveena Yanamala
- b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV , USA
| | - Bengt Fadeel
- e Division of Molecular Toxicology, Institute of Environmental Medicine , Karolinska Institute , Stockholm , Sweden
| | - Jaerak Chang
- a Department of Brain Science , Ajou University School of Medicine , Suwon , Republic of Korea.,f Graduate School of Biomedical Sciences , Ajou University School of Medicine , Suwon , Republic of Korea
| | - Anna A Shvedova
- b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV , USA.,c Department of Physiology, Pharmacology and Neuroscience , West Virginia University , Morgantown , WV , USA
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Khaliullin TO, Kisin ER, Murray AR, Yanamala N, Shurin MR, Gutkin DW, Fatkhutdinova LM, Kagan VE, Shvedova AA. Mediation of the single-walled carbon nanotubes induced pulmonary fibrogenic response by osteopontin and TGF-β1. Exp Lung Res 2018; 43:311-326. [PMID: 29140132 DOI: 10.1080/01902148.2017.1377783] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
PURPOSE OF THE STUDY A number of in vivo studies have shown that pulmonary exposure to carbon nanotubes (CNTs) may lead to an acute local inflammatory response, pulmonary fibrosis, and granulomatous lesions. Among the factors that play direct roles in initiation and progression of fibrotic processes are epithelial-mesenchymal transition and myofibroblasts recruitment/differentiation, both mediated by transforming growth factor-β1 (TGF-β1). Yet, other contributors to TGF-β1 associated signaling, such as osteopontin (OPN) has not been fully investigated. MATERIALS AND METHODS OPN-knockout female mice (OPN-KO) along with their wild-type (WT) counterparts were exposed to single-walled carbon nanotubes (SWCNT) (40 µg/mouse) via pharyngeal aspiration and fibrotic response was assessed 1, 7, and 28 days post-exposure. Simultaneously, RAW 264.7 and MLE-15 cells were treated with SWCNT (24 hours, 6 µg/cm2 to 48 µg/cm2) or bleomycin (0.1 µg/ml) in the presence of OPN-blocking antibody or isotype control, and TGF-β1 was measured in supernatants. RESULTS AND CONCLUSIONS Diminished lactate dehydrogenase activity at all time points, along with less pronounced neutrophil influx 24 h post-exposure, were measured in broncho-alveolar lavage (BAL) of OPN-KO mice compared to WT. Pro-inflammatory cytokine release (IL-6, TNF-α, MCP-1) was reduced. A significant two-fold increase of TGF-β1 was found in BAL of WT mice at 7 days, while TGF-β1 levels in OPN-KO animals remained unaltered. Histological examination revealed marked decrease in granuloma formation and less collagen deposition in the lungs of OPN-KO mice compared to WT. RAW 264.7 but not MLE-15 cells exposed to SWCNT and bleomycin had significantly less TGF-β1 released in the presence of OPN-blocking antibody. We believe that OPN is important in initiating the cellular mechanisms that produce an overall pathological response to SWCNT and it may act upstream of TGF-β1. Further investigation to understand the mechanistic details of such interactions is critical to predict outcomes of pulmonary exposure to CNT.
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Affiliation(s)
- Timur O Khaliullin
- a Department of Physiology & Pharmacology , West Virginia University , Morgantown , WV.,b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV
| | - Elena R Kisin
- b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV
| | | | | | - Michael R Shurin
- c Department Pathology , University of Pittsburgh , Pittsburgh , PA
| | - Dmitriy W Gutkin
- c Department Pathology , University of Pittsburgh , Pittsburgh , PA
| | - Liliya M Fatkhutdinova
- d Department of Hygiene and Occupational Medicine , Kazan State Medical University , Kazan , Russia
| | - Valerian E Kagan
- e Department of Pathology , University of Pittsburgh , Pittsburgh , PA
| | - Anna A Shvedova
- a Department of Physiology & Pharmacology , West Virginia University , Morgantown , WV.,b Exposure Assessment Branch , NIOSH/CDC , Morgantown , WV
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Mohammadyani D, Yanamala N, Samhan-Arias AK, Kapralov AA, Stepanov G, Nuar N, Planas-Iglesias J, Sanghera N, Kagan VE, Klein-Seetharaman J. Structural characterization of cardiolipin-driven activation of cytochrome c into a peroxidase and membrane perturbation. Biochim Biophys Acta Biomembr 2018; 1860:1057-1068. [PMID: 29317202 DOI: 10.1016/j.bbamem.2018.01.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 12/14/2017] [Accepted: 01/04/2018] [Indexed: 01/25/2023]
Abstract
The interaction between cardiolipin (CL) and cytochrome c (cyt-c) results in a gain of function of peroxidase activity by cyt-c. Despite intensive research, disagreements on nature and molecular details of this interaction remain. In particular, it is still not known how the interaction triggers the onset of apoptosis. Enzymatic characterization of peroxidase activity has highlighted the need for a critical threshold concentration of CL, a finding of profound physiological relevance in vivo. Using solution NMR, fluorescence spectroscopy, and in silico modeling approaches we here confirm that full binding of cyt-c to the membrane requires a CL:cyt-c threshold ratio of 5:1. Among three binding sites, the simultaneous binding of two sites, at two opposing sides of the heme, provides a mechanism to open the heme crevice to substrates. This results in "productive binding" in which cyt-c then sequesters CL, inducing curvature in the membrane. Membrane perturbation along with lipid peroxidation, due to interactions of heme/CL acyl chains, initiates the next step in the apoptotic pathway of making the membrane leaky. The third CL binding site while allowing interaction with the membrane, does not cluster CL or induce subsequent events, making this interaction "unproductive".
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Affiliation(s)
- Dariush Mohammadyani
- Department of Environmental and Occupational Health, University of Pittsburgh, PA 15219, USA; Thomas C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Naveena Yanamala
- National Institute for Occupational Safety and Health/Centers for Disease Control and Prevention, Morgantown, WV 26505, USA
| | - Alejandro K Samhan-Arias
- LAQV, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
| | - Alexander A Kapralov
- Department of Environmental and Occupational Health, University of Pittsburgh, PA 15219, USA
| | - German Stepanov
- Department of General and Medical Biophysics, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Nick Nuar
- Department of Bioengineering, University of Pittsburgh, PA 15213, USA
| | - Joan Planas-Iglesias
- Division of Metabolic and Vascular Health, Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Narinder Sanghera
- Division of Metabolic and Vascular Health, Medical School, University of Warwick, Coventry CV4 7AL, UK
| | - Valerian E Kagan
- Department of Environmental and Occupational Health, University of Pittsburgh, PA 15219, USA
| | - Judith Klein-Seetharaman
- Division of Metabolic and Vascular Health, Medical School, University of Warwick, Coventry CV4 7AL, UK.
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