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Phung TKN, Sinclair SE, Makena P, Molthen RC, Waters CM. Dynamic airway constriction in rats: heterogeneity and response to deep inspiration. Am J Physiol Lung Cell Mol Physiol 2019; 317:L39-L48. [PMID: 31017015 DOI: 10.1152/ajplung.00050.2019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Airway narrowing due to hyperresponsiveness severely limits gas exchange in patients with asthma. Imaging studies in humans and animals have shown that bronchoconstriction causes patchy patterns of ventilation defects throughout the lungs, and several computational models have predicted that these regions are due to constriction of smaller airways. However, these imaging approaches are often limited in their ability to capture dynamic changes in small airways, and the patterns of constriction are heterogeneous. To directly investigate regional variations in airway narrowing and the response to deep inspirations (DIs), we utilized tantalum dust and microfocal X-ray imaging of rat lungs to obtain dynamic images of airways in an intact animal model. Airway resistance was simultaneously measured using the flexiVent system. Custom-developed software was used to track changes in airway diameters up to generation 19 (~0.3-3 mm). Changes in diameter during bronchoconstriction were then measured in response to methacholine (MCh) challenge. In contrast with the model predictions, we observed significantly greater percent constriction in larger airways in response to MCh challenge. Although there was a dose-dependent increase in total respiratory resistance with MCh, the percent change in airway diameters was similar for increasing doses. A single DI following MCh caused a significant reduction in resistance but did not cause a significant increase in airway diameters. Multiple DIs did, however, cause significant increases in airway diameters. These measurements allowed us to directly quantify dynamic changes in airways during bronchoconstriction and demonstrated greater constriction in larger airways.
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Affiliation(s)
- Thien-Khoi N Phung
- Department of Physiology, University of Tennessee Health Science Center , Memphis, Tennessee
| | - Scott E Sinclair
- Department of Physiology, University of Tennessee Health Science Center , Memphis, Tennessee.,Department of Medicine, University of Tennessee Health Science Center , Memphis, Tennessee
| | - Patrudu Makena
- Department of Medicine, University of Tennessee Health Science Center , Memphis, Tennessee
| | - Robert C Molthen
- Department of Medicine, Medical College of Wisconsin , Milwaukee, Wisconsin
| | - Christopher M Waters
- Department of Physiology, University of Tennessee Health Science Center , Memphis, Tennessee.,Department of Medicine, University of Tennessee Health Science Center , Memphis, Tennessee.,Department of Physiology and Saha Cardiovascular Research Center, University of Kentucky , Lexington, Kentucky
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Kabilan S, Suffield S, Recknagle K, Jacob R, Einstein D, Kuprat A, Carson J, Colby S, Saunders J, Hines S, Teeguarden J, Straub T, Moe M, Taft S, Corley R. Computational fluid dynamics modeling of Bacillus anthracis spore deposition in rabbit and human respiratory airways. JOURNAL OF AEROSOL SCIENCE 2016; 99:64-77. [PMID: 33311732 PMCID: PMC7731948 DOI: 10.1016/j.jaerosci.2016.01.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Three-dimensional computational fluid dynamics and Lagrangian particle deposition models were developed to compare the deposition of aerosolized Bacillus anthracis spores in the respiratory airways of a human with that of the rabbit, a species commonly used in the study of anthrax disease. The respiratory airway geometries for each species were derived respectively from computed tomography (CT) and μCT images. Both models encompassed airways that extended from the external nose to the lung with a total of 272 outlets in the human model and 2878 outlets in the rabbit model. All simulations of spore deposition were conducted under transient, inhalation-exhalation breathing conditions using average species-specific minute volumes. Two different exposure scenarios were modeled in the rabbit based upon experimental inhalation studies. For comparison, human simulations were conducted at the highest exposure concentration used during the rabbit experimental exposures. Results demonstrated that regional spore deposition patterns were sensitive to airway geometry and ventilation profiles. Due to the complex airway geometries in the rabbit nose, higher spore deposition efficiency was predicted in the nasal sinus compared to the human at the same air concentration of anthrax spores. In contrast, higher spore deposition was predicted in the lower conducting airways of the human compared to the rabbit lung due to differences in airway branching pattern. This information can be used to refine published and ongoing biokinetic models of inhalation anthrax spore exposures, which currently estimate deposited spore concentrations based solely upon exposure concentrations and inhaled doses that do not factor in species-specific anatomy and physiology for deposition.
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Affiliation(s)
- S. Kabilan
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - S.R. Suffield
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - K.P. Recknagle
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - R.E. Jacob
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - D.R. Einstein
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - A.P. Kuprat
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - J.P. Carson
- Texas Advanced Computing Center, Austin, TX 78758, United States
| | - S.M Colby
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - J.H. Saunders
- Battelle, 505 King Avenue, Columbus, OH 43201, United States
| | - S.A. Hines
- Battelle, 505 King Avenue, Columbus, OH 43201, United States
| | - J.G. Teeguarden
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - T.M. Straub
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
| | - M. Moe
- Department of Homeland Security, Science and Technology Directorate, Washington, DC 20528, United States
| | - S.C. Taft
- U.S. Environmental Protection Agency, National Homeland Security Research Center, Threat and Consequence Assessment Division, Cincinnati, OH 45268, United States
| | - R.A. Corley
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J4-16, Richland, WA 99352, United States
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Ibrahim G, Rona A, Hainsworth SV. Non-uniform central airways ventilation model based on vascular segmentation. Comput Biol Med 2015; 65:137-45. [PMID: 26318114 DOI: 10.1016/j.compbiomed.2015.08.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 08/04/2015] [Accepted: 08/06/2015] [Indexed: 11/17/2022]
Abstract
Improvements in the understanding of the physiology of the central airways require an appropriate representation of the non-uniform ventilation at its terminal branches. This paper proposes a new technique for estimating the non-uniform ventilation at the terminal branches by modelling the volume change of their distal peripheral airways, based on vascular segmentation. The vascular tree is used for sectioning the dynamic CT-based 3D volume of the lung at 11 time points over the breathing cycle of a research animal. Based on the mechanical coupling between the vascular tree and the remaining lung tissues, the volume change of each individual lung segment over the breathing cycle was used to estimate the non-uniform ventilation of its associated terminal branch. The 3D lung sectioning technique was validated on an airway cast model of the same animal pruned to represent the truncated dynamic CT based airway geometry. The results showed that the 3D lung sectioning technique was able to estimate the volume of the missing peripheral airways within a tolerance of 2%. In addition, the time-varying non-uniform ventilation distribution predicted by the proposed sectioning technique was validated against CT measurements of lobar ventilation and showed good agreement. This significant modelling advance can be used to estimate subject-specific non-uniform boundary conditions to obtain subject-specific numerical models of the central airway flow.
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Affiliation(s)
- G Ibrahim
- Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | - A Rona
- Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
| | - S V Hainsworth
- Department of Engineering, University of Leicester, University Road, Leicester LE1 7RH, UK.
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Corley RA, Kabilan S, Kuprat AP, Carson JP, Jacob RE, Minard KR, Teeguarden JG, Timchalk C, Pipavath S, Glenny R, Einstein DR. Comparative Risks of Aldehyde Constituents in Cigarette Smoke Using Transient Computational Fluid Dynamics/Physiologically Based Pharmacokinetic Models of the Rat and Human Respiratory Tracts. Toxicol Sci 2015; 146:65-88. [PMID: 25858911 PMCID: PMC4476461 DOI: 10.1093/toxsci/kfv071] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Computational fluid dynamics (CFD) modeling is well suited for addressing species-specific anatomy and physiology in calculating respiratory tissue exposures to inhaled materials. In this study, we overcame prior CFD model limitations to demonstrate the importance of realistic, transient breathing patterns for predicting site-specific tissue dose. Specifically, extended airway CFD models of the rat and human were coupled with airway region-specific physiologically based pharmacokinetic (PBPK) tissue models to describe the kinetics of 3 reactive constituents of cigarette smoke: acrolein, acetaldehyde and formaldehyde. Simulations of aldehyde no-observed-adverse-effect levels for nasal toxicity in the rat were conducted until breath-by-breath tissue concentration profiles reached steady state. Human oral breathing simulations were conducted using representative aldehyde yields from cigarette smoke, measured puff ventilation profiles and numbers of cigarettes smoked per day. As with prior steady-state CFD/PBPK simulations, the anterior respiratory nasal epithelial tissues received the greatest initial uptake rates for each aldehyde in the rat. However, integrated time- and tissue depth-dependent area under the curve (AUC) concentrations were typically greater in the anterior dorsal olfactory epithelium using the more realistic transient breathing profiles. For human simulations, oral and laryngeal tissues received the highest local tissue dose with greater penetration to pulmonary tissues than predicted in the rat. Based upon lifetime average daily dose comparisons of tissue hot-spot AUCs (top 2.5% of surface area-normalized AUCs in each region) and numbers of cigarettes smoked/day, the order of concern for human exposures was acrolein > formaldehyde > acetaldehyde even though acetaldehyde yields were 10-fold greater than formaldehyde and acrolein.
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Affiliation(s)
- Richard A Corley
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Senthil Kabilan
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Andrew P Kuprat
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - James P Carson
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Richard E Jacob
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Kevin R Minard
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Justin G Teeguarden
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Charles Timchalk
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Sudhakar Pipavath
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Robb Glenny
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
| | - Daniel R Einstein
- *Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352; Texas Advanced Computing Center, University of Texas, Austin, Texas 78758; Radiology, University of Washington, Seattle, Washington 98195; and Division of Pulmonary and Critical Care Medicine, University of Washington, Seattle, Washington 98195
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Miller FJ, Asgharian B, Schroeter JD, Price O, Corley RA, Einstein DR, Jacob RE, Cox TC, Kabilan S, Bentley T. Respiratory tract lung geometry and dosimetry model for male Sprague-Dawley rats. Inhal Toxicol 2015; 26:524-44. [PMID: 25055841 DOI: 10.3109/08958378.2014.925991] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
While inhalation toxicological studies of various compounds have been conducted using a number of different strains of rats, mechanistic dosimetry models have only had tracheobronchial (TB) structural data for Long-Evans rats, detailed morphometric data on the alveolar region of Sprague-Dawley rats and limited alveolar data on other strains. Based upon CT imaging data for two male Sprague-Dawley rats, a 15-generation, symmetric typical path model was developed for the TB region. Literature data for the alveolar region of Sprague-Dawley rats were analyzed to develop an eight-generation model, and the two regions were joined to provide a complete lower respiratory tract model for Sprague-Dawley rats. The resulting lung model was used to examine particle deposition in Sprague-Dawley rats and to compare these results with predicted deposition in Long-Evans rats. Relationships of various physiologic variables and lung volumes were either developed in this study or extracted from the literature to provide the necessary input data for examining particle deposition. While the lengths, diameters and branching angles of the TB airways differed between the two Sprague-Dawley rats, the predicted deposition patterns in the three major respiratory tract regions were very similar. Between Sprague-Dawley and Long-Evans rats, significant differences in TB and alveolar predicted deposition fractions were observed over a wide range of particle sizes, with TB deposition fractions being up to 3- to 4-fold greater in Sprague-Dawley rats and alveolar deposition being significantly greater in Long-Evans rats. Thus, strain-specific lung geometry models should be used for particle deposition calculations and interspecies dose comparisons.
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Haenssgen K, Makanya AN, Djonov V. Casting materials and their application in research and teaching. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2014; 20:493-513. [PMID: 24564951 DOI: 10.1017/s1431927613014050] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
From a biological point of view, casting refers to filling of anatomical and/or pathological spaces with extraneous material that reproduces a three-dimensional replica of the space. Casting may be accompanied by additional procedures such as corrosion, in which the soft tissue is digested out, leaving a clean cast, or the material may be mixed with radiopaque substances to allow x-ray photography or micro computed topography (µCT) scanning. Alternatively, clearing of the surrounding soft tissue increases transparency and allows visualization of the casted cavities. Combination of casting with tissue fixation allows anatomical dissection and didactic surgical procedures on the tissue. Casting materials fall into three categories namely, aqueous substances (India ink, Prussian blue ink), pliable materials (gelatins, latex, and silicone rubber), or hard materials (methyl methacrylates, polyurethanes, polyesters, and epoxy resins). Casting has proved invaluable in both teaching and research and many phenomenal biological processes have been discovered through casting. The choice of a particular material depends inter alia on the targeted use and the intended subsequent investigative procedures, such as dissection, microscopy, or µCT. The casting material needs to be pliable where anatomical and surgical manipulations are intended, and capillary-passable for ultrastructural investigations.
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Affiliation(s)
- Kati Haenssgen
- 1 Institute of Anatomy, University of Bern, Baltzerstrasse 2, Ch-3000 Bern 9, Switzerland
| | - Andrew N Makanya
- 1 Institute of Anatomy, University of Bern, Baltzerstrasse 2, Ch-3000 Bern 9, Switzerland
| | - Valentin Djonov
- 1 Institute of Anatomy, University of Bern, Baltzerstrasse 2, Ch-3000 Bern 9, Switzerland
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