Articles
Fatahi, H.,
Dastan, A.,
Sadrizadeh, S.,
Abouali, O. Biomechanics and Modeling in Mechanobiology (16177959)
Nasal hairs, often overlooked in human respiratory system studies, can be a decisive factor in maintaining respiratory health. Vibrissae can capture a certain range of particle sizes due to their filtering function, while they may also contribute to more breathing resistance. In this study, the role of nasal hairs in particle filtration and pressure drop within the nasal vestibule was investigated using computational fluid dynamics (CFD) simulations. Seven nasal hair specifications were examined in simplified human nasal vestibule models under steady laminar flow conditions at two airflow rates of 10 and 15 L/min. The deposition of microparticles in the simulated geometries was also numerically studied. The simulation results showed that the investigated nasal hairs lead to about a 2–20 Pa increase in the pressure drop, depending on the hair specifications and airflow rates. The associated growth in nasal resistance could potentially influence breathing comfort. Additionally, nasal hair was shown to enhance particle filtration, with the deposition fraction of particles correlating with the projected area of the hairs on a normal plane to the flow direction, which goes up by an increase in the number of hairs or their length. These findings clarify the significance of nasal hairs in the respiratory system and aim to balance the trade-off between improved particle filtration and increased breathing resistance due to nasal hairs. The acquired knowledge can be used in recommendations to different individuals regarding nasal hair trimming based on their health conditions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
Rezazadeh, M.R.,
Dastan, A.,
Sadrizadeh, S.,
Abouali, O. Medical and Biological Engineering and Computing (17410444)62(10)pp. 3025-3041
The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka’s deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka’s model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15 l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract. Graphical Abstract: (Figure presented.) © International Federation for Medical and Biological Engineering 2024.
Davari F.,
Isfahani, M.T.,
Atighechian A.,
Ghobadian E.,
Dastan, A.,
Abouali, O.,
Ahmadi, G. BMC Medical Informatics and Decision Making (14726947)(1)pp. 132-149
Objective: Overcrowding and extended waiting times in emergency departments are a pervasive issue, leading to patient dissatisfaction. This study aims to compare the efficacy of two process mining and simulation models in identifying bottlenecks and optimizing patient flow in the emergency department of Al-Zahra Hospital in Isfahan. The ultimate goal is to reduce patient waiting times and alleviate population density, ultimately enhancing the overall patient experience. Methods: This study employed a descriptive, applied, cross-sectional, and retrospective design. The study population consisted of 39,264 individuals referred to Al-Zahra Hospital, with a sample size of at least 1,275 participants, selected using systematic random sampling at a confidence level of 99%. Data were collected through a questionnaire and the Hospital Information System (HIS). Statistical analysis was conducted using Excel software, with a focus on time-averaged data. Two methods of simulation and process mining were utilized to analyze the data. First, the model was run 1000 times using ARENA software, with simulation techniques. In the second step, the emergency process model was discovered using process mining techniques through Access software, and statistical analysis was performed on the event log. The relationships between the data were identified, and the discovered model was analyzed using the Fuzzy Miner algorithm and Disco tool. Finally, the results of the two models were compared, and proposed scenarios to reduce patient waiting times were examined using simulation techniques. Results: The analysis of the current emergency process at Al-Zahra Hospital revealed that the major bottlenecks in the process are related to waiting times, inefficient implementation of doctor’s orders, delays in recording patient test results, and congestion at the discharge station. Notably, the process mining exercise corroborated the findings from the simulation, providing a comprehensive understanding of the inefficiencies in the emergency process. Next, 34 potential solutions were proposed to reduce waiting times and alleviate these bottlenecks. These solutions were simulated using Arena software, allowing for a comprehensive evaluation of their effectiveness. The results were then compared to identify the most promising strategies for improving the emergency process. Conclusion: In conclusion, the results of this research demonstrate the effectiveness of using simulation techniques and process mining in making informed, data-driven decisions that align with available resources and conditions. By leveraging these tools, unnecessary waste and additional expenses can be significantly reduced. The comparative analysis of the 34 proposed scenarios revealed that two solutions stood out as the most effective in improving the emergency process. Scenario 19, which involves dedicating two personnel to jointly referring patients to the ward, and scenario 34, which creates a dedicated discharge hall, have the potential to create a more favorable situation. © The Author(s) 2024.
