Evidence map›Paper›PMID 32217283›Full record

ArticleComputers in biology and medicine2020

Particle transport and deposition correlation with near-wall flow characteristic under inspiratory airflow in lung airways.

Ali Farghadan, Kamran Poorbahrami, Sahar Jalal, Jessica M Oakes, Filippo Coletti, Amirhossein Arzani

Open access · greenAbstract read
In one paragraph

Article in Computers in biology and medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
2.4field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 35 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Ali FarghadanDepartment of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, United States.
Kamran PoorbahramiDepartment of Mechanical and Industrial Engineering, Northeastern University, Boston, MA, United States.
Sahar JalalDepartment of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN, United States.
Jessica M OakesDepartment of Bioengineering, Northeastern University, Boston, MA, United States.
Filippo ColettiDepartment of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, MN, United States.
Amirhossein ArzaniDepartment of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, United States. Electronic address: amir.arzani@nau.edu.
Northeastern University · USNorthern Arizona University · USUniversity of Minnesota · US

Funding

Coupling MRI-Derived Ventilation with Computational Models to Assess Inhaled Aerosol Treatment Feasibility in Severe Asthmatic AdultsR21HL140436 · NHLBI · NORTHEASTERN UNIVERSITY · PI OAKES, JESSICA M · 2017 to 2018
$245k
NHLBI NIH HHS R21 HL140436
6 · The paper itself

Abstract

Exposure of lung airways to detrimental suspended aerosols in the environment increases the vulnerability of the respiratory and cardiovascular systems. In addition, recent developments in therapeutic inhalation devices magnify the importance of particle transport. In this manuscript, particle transport and deposition patterns in the upper tracheobronchial (TB) tree were studied where the inertial forces are considerable for microparticles. Wall shear stress divergence (WSSdiv) is proposed as a wall-based parameter that can predict particle deposition patterns. WSSdiv is proportional to near-wall normal velocity and can quantify the strength of flow towards and away from the wall. Computational fluid dynamics (CFD) simulations were performed to quantify airflow velocity and WSS vectors for steady inhalation in one case-control and unsteady inhalation in six subject-specific airway trees. Turbulent flow simulation was performed for the steady case using large eddy simulation to study the effect of turbulence. Magnetic resonance velocimetry (MRV) measurements were used to validate the case-control CFD simulation. Inertial particle transport was modeled by solving the Maxey-Riley equation in a Lagrangian framework. Deposition percentage (DP) was quantified for the case-control model over five particle sizes. DP was found to be proportional to particle size in agreement with previous studies in the literature. A normalized deposition concentration (DC) was defined to characterize localized deposition. A relatively strong correlation (Pearson value > 0.7) was found between DC and positive WSSdiv for physiologically relevant Stokes (St) numbers. Additionally, a regional analysis was performed after dividing the lungs into smaller areas. A spatial integral of positive WSSdiv over each division was shown to maintain a very strong correlation (Pearson value > 0.9) with cumulative spatial DC or regional dosimetry. The conclusions were generalized to a larger population in which two healthy and four asthmatic patients were investigated. This study shows that WSSdiv could be used to predict the qualitative surface deposition and relative regional dosimetry without the need to solve a particle transport problem.

Indexed as

HydrodynamicsLungAdministration, InhalationAerosolsBronchiComputer SimulationHumansModels, BiologicalParticle SizeAerosolsAirflowComputational fluid dynamicsDepositionDosimetryParticle transportTurbulenceWall shear stress

Identifiers

PMID32217283
PMCPMC7237327
OpenAlexW3011763754

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.