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Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering

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A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Regenerative Engineering".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 18326

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Dr. Sundeep Singh

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Faculty of Sustainable Design Engineering, University of Prince Edward Island, 550 University Ave, Charlottetown, PE C1A 4P3, Canada
Interests: thermo-fluids; mathematical modeling & simulations; computational fluid dynamics and heat transfer; biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Roderick Melnik

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MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, 75 University Avenue West, Waterloo, ON N2L 3C5, Canada
Interests: coupled multiscale problems in bioengineering and biomedicine; biomaterials, bionano systems; inverse problems; modelling dynamic diseases and pain; brain processes and brain models; geometry-based techniques such as 3D printing; statistical learning, human factor, control systems
Special Issues, Collections and Topics in MDPI journals

Dr. Esther Pueyo

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Aragón Institute for Engineering Research (I3A), University of Zaragoza, IIS Aragón, C/Mariano Esquillor s/n, 50018 Zaragoza, Spain
Interests: signal processing; modeling & simulation; electrocardiography; arrhythmias; cardiac electrophysiology

Special Issue Information

Dear Colleagues,

Mathematical modeling in biomedical engineering plays a vital role in understanding complex multidisciplinary interactions and processes at the organ, tissue and cellular scales. Mathematical modeling serves as a low-cost but powerful alternative for optimizing, predicting, and improving existing healthcare protocols, systems and equipment. This Special Issue aims at collecting original research articles related to the advancement and development of novel mathematical models broadly applied across a wide range of biomedical engineering and medical physics, including diagnostic, therapeutic, imaging, and interventional applications. Review articles pertaining to the overall scope of this issue are also welcome.

Topics of particular interest include, but are not limited to:

coupled multiphysics and multiscale models in bioengineering and biomedicine
bioheat models and thermography
models for cancer theranostics
patient-specific models
dynamic and network models (e.g., regulatory, metabolic, brain networks, etc.)
machine learning and multiscale modeling in the biological, biomedical, and behavioral sciences
synthetic biology and its applications
mathematical approaches into the regenerative medicine
hemodynamics and drug delivery models
computational neuroscience and neuroengineering, data-driven approaches in multidisciplinary neuroscience
numerical methods and algorithms in the biomedical engineering
computational and systems biology
computational biomechanics
reduced-order models
biomedical simulation and high-performance computing

Dr. Sundeep Singh
Prof. Dr. Roderick Melnik
Dr. Esther Pueyo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

modeling and simulation
biomedical engineering
medical physics
multiphysics and multiscale models
biomedical therapy
neurotechnology
regenerative medicine
drug delivery
data-driven models
machine learning
patient-specific models

Published Papers (10 papers)

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Research

20 pages, 3643 KiB

Open AccessArticle

Robust Control of Repeated Drug Administration with Variable Doses Based on Uncertain Mathematical Model

by Zuzana Vitková, Martin Dodek, Eva Miklovičová, Jarmila Pavlovičová, Andrej Babinec and Anton Vitko

Bioengineering 2023, 10(8), 921; https://doi.org/10.3390/bioengineering10080921 - 03 Aug 2023

Viewed by 735

Abstract

The aim of this paper was to design a repeated drug administration strategy to reach and maintain the requested drug concentration in the body. Conservative designs require an exact knowledge of pharmacokinetic parameters, which is considered an unrealistic demand. The problem is usually resolved using the trial-and-error open-loop approach; yet, this can be considered insufficient due to the parametric uncertainties as the dosing strategy may induce an undesired behavior of the drug concentrations. Therefore, the presented approach is rather based on the paradigms of system and control theory. An algorithm was designed that computes the required doses to be administered based on the blood samples. Since repeated drug dosing is essentially a discrete time process, the entire design considers the discrete time domain. We have also presented the idea of applying this methodology for the stabilization of an unstable model, for instance, a model of tumor growth. The simulation experiments demonstrated that all variants of the proposed control algorithm can reach and maintain the desired drug concentration robustly, i.e., despite the presence of parametric uncertainties, in a way that is superior to that of the traditional open-loop approach. It was shown that the closed-loop control with the integral controller and stabilizing state feedback is robust against large parametric uncertainties. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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24 pages, 2696 KiB

Open AccessArticle

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

by Sekar Kidambi Raju, Seethalakshmi Ramaswamy, Marwa M. Eid, Sathiamoorthy Gopalan, Faten Khalid Karim, Raja Marappan and Doaa Sami Khafaga

Bioengineering 2023, 10(7), 880; https://doi.org/10.3390/bioengineering10070880 - 24 Jul 2023

Viewed by 1535

Abstract

This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission’s stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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18 pages, 5194 KiB

Open AccessArticle

Numerical Modeling and Simulation of Non-Invasive Acupuncture Therapy Utilizing Near-Infrared Light-Emitting Diode

by Sundeep Singh, Andres Escobar, Zexi Wang, Zhiyi Zhang, Chundra Ramful and Chang-Qing Xu

Bioengineering 2023, 10(7), 837; https://doi.org/10.3390/bioengineering10070837 - 15 Jul 2023

Cited by 1 | Viewed by 1083

Abstract

Acupuncture is one of the most extensively used complementary and alternative medicine therapies worldwide. In this study, we explore the use of near-infrared light-emitting diodes (LEDs) to provide acupuncture-like physical stimulus to the skin tissue, but in a completely non-invasive way. A computational modeling framework has been developed to investigate the light-tissue interaction within a three-dimensional multi-layer model of skin tissue. Finite element-based analysis has been conducted, to obtain the spatiotemporal temperature distribution within the skin tissue, by solving Pennes’ bioheat transfer equation, coupled with the Beer-Lambert law. The irradiation profile of the LED has been experimentally characterized and imposed in the numerical model. The experimental validation of the developed model has been conducted through comparing the numerical model predictions with those obtained experimentally on the agar phantom. The effects of the LED power, treatment duration, LED distance from the skin surface, and usage of multiple LEDs on the temperature distribution attained within the skin tissue have been systematically investigated, highlighting the safe operating power of the selected LEDs. The presented information about the spatiotemporal temperature distribution, and critical factors affecting it, would assist in better optimizing the desired thermal dosage, thereby enabling a safe and effective LED-based photothermal therapy. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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11 pages, 3251 KiB

Open AccessCommunication

Spatial Dependence of Log-Transformed Electromyography–Force Relation: Model-Based Sensitivity Analysis and Experimental Study of Biceps Brachii

by Chengjun Huang, Maoqi Chen, Zhiyuan Lu, Cliff S. Klein and Ping Zhou

Bioengineering 2023, 10(4), 469; https://doi.org/10.3390/bioengineering10040469 - 12 Apr 2023

Viewed by 1202

Abstract

This study investigated electromyography (EMG)–force relations using both simulated and experimental approaches. A motor neuron pool model was first implemented to simulate EMG–force signals, focusing on three different conditions that test the effects of small or large motor units located more or less superficially in the muscle. It was found that the patterns of the EMG–force relations varied significantly across the simulated conditions, quantified by the slope (b) of the log-transformed EMG-force relation. b was significantly higher for large motor units, which were preferentially located superficially rather than for random depth or deep depth conditions (p < 0.001). The log-transformed EMG–force relations in the biceps brachii muscles of nine healthy subjects were examined using a high-density surface EMG. The slope (b) distribution of the relation across the electrode array showed a spatial dependence; b in the proximal region was significantly larger than the distal region, whereas b was not different between the lateral and medial regions. The findings of this study provide evidence that the log-transformed EMG–force relations are sensitive to different motor unit spatial distributions. The slope (b) of this relation may prove to be a useful adjunct measure in the investigation of muscle or motor unit changes associated with disease, injury, or aging. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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21 pages, 4584 KiB

Open AccessArticle

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

by Xingsi Xue, Seelammal Chinnaperumal, Ghaida Muttashar Abdulsahib, Rajasekhar Reddy Manyam, Raja Marappan, Sekar Kidambi Raju and Osamah Ibrahim Khalaf

Bioengineering 2023, 10(3), 363; https://doi.org/10.3390/bioengineering10030363 - 16 Mar 2023

Cited by 30 | Viewed by 2533

Abstract

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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17 pages, 3880 KiB

Open AccessArticle

Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression

by Xingsi Xue, Raja Marappan, Sekar Kidambi Raju, Rangarajan Raghavan, Rengasri Rajan, Osamah Ibrahim Khalaf and Ghaida Muttashar Abdulsahib

Bioengineering 2023, 10(3), 333; https://doi.org/10.3390/bioengineering10030333 - 06 Mar 2023

Cited by 13 | Viewed by 1574

Abstract

Due to rapidly developing technology and new research innovations, privacy and data preservation are paramount, especially in the healthcare industry. At the same time, the storage of large volumes of data in medical records should be minimized. Recently, several types of research on lossless medically significant data compression and various steganography methods have been conducted. This research develops a hybrid approach with advanced steganography, wavelet transform (WT), and lossless compression to ensure privacy and storage. This research focuses on preserving patient data through enhanced security and optimized storage of large data images that allow a pharmacologist to store twice as much information in the same storage space in an extensive data repository. Safe storage, fast image service, and minimum computing power are the main objectives of this research. This work uses a fast and smooth knight tour (KT) algorithm to embed patient data into medical images and a discrete WT (DWT) to protect shield images. In addition, lossless packet compression is used to minimize memory footprints and maximize memory efficiency. JPEG formats’ compression ratio percentages are slightly higher than those of PNG formats. When image size increases, that is, for high-resolution images, the compression ratio lies between 7% and 7.5%, and the compression percentage lies between 30% and 37%. The proposed model increases the expected compression ratio and percentage compared to other models. The average compression ratio lies between 7.8% and 8.6%, and the expected compression ratio lies between 35% and 60%. Compared to state-of-the-art methods, this research results in greater data security without compromising image quality. Reducing images makes them easier to process and allows many images to be saved in archives. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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19 pages, 11727 KiB

Open AccessArticle

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations

by Mehreen Ghufran, Mehran Ullah, Haider Ali Khan, Sabreen Ghufran, Muhammad Ayaz, Muhammad Siddiq, Syed Qamar Abbas, Syed Shams ul Hassan and Simona Bungau

Bioengineering 2023, 10(1), 100; https://doi.org/10.3390/bioengineering10010100 - 11 Jan 2023

Cited by 10 | Viewed by 2572

Abstract

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M^pro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M^pro inhibitors against SARS-CoV-2. The 33.8 kDa M^pro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2’s protease (M^pro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M^pro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes’ stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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13 pages, 3263 KiB

Open AccessArticle

On the Bioconvective Aspect of Viscoelastic Micropolar Nanofluid Referring to Variable Thermal Conductivity and Thermo-Diffusion Characteristics

by Omar T. Bafakeeh, Kamel Al-Khaled, Sami Ullah Khan, Aamar Abbasi, Charankumar Ganteda, M. Ijaz Khan, Kamel Guedri and Sayed M. Eldin

Bioengineering 2023, 10(1), 73; https://doi.org/10.3390/bioengineering10010073 - 05 Jan 2023

Cited by 12 | Viewed by 1239

Abstract

The bioconvective flow of non-Newtonian fluid induced by a stretched surface under the aspects of combined magnetic and porous medium effects is the main focus of the current investigation. Unlike traditional aspects, here the viscoelastic behavior has been examined by a combination of both micropolar and second grade fluid. Further thermophoresis, Brownian motion and thermodiffusion aspects, along with variable thermal conductivity, have also been utilized for the boundary process. The solution of the nonlinear fundamental flow problem is figured out via convergent approach via Mathematica software. It is noted that this flow model is based on theoretical flow assumptions instead of any experimental data. The efficiency of the simulated solution has been determined by comparing with previously reported results. The engineering parameters’ effects are computationally evaluated for some definite range. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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15 pages, 4256 KiB

Open AccessArticle

Holistic Design of Experiments Using an Integrated Process Model

by Thomas Oberleitner, Thomas Zahel, Barbara Pretzner and Christoph Herwig

Bioengineering 2022, 9(11), 643; https://doi.org/10.3390/bioengineering9110643 - 03 Nov 2022

Viewed by 2409

Abstract

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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18 pages, 9376 KiB

Open AccessArticle

Peristaltic Phenomenon in an Asymmetric Channel Subject to Inclined Magnetic Force and Porous Space

by Muhammad Ijaz Khan, Maha M. A. Lashin, Nidhal Ben Khedher, Bilal Ahmed, Sami Ullah Khan, Mowffaq Oreijah, Kamel Guedri, El Sayed Mohamed Tag-ElDin and Ahmed M. Galal

Bioengineering 2022, 9(10), 588; https://doi.org/10.3390/bioengineering9100588 - 20 Oct 2022

Cited by 2 | Viewed by 1595

Abstract

This research is engaged to explore biological peristaltic transport under the action of an externally applied magnetic field passing through an asymmetric channel which is saturated with porous media. The set of governing partial differential equations for the present peristaltic flow are solved in the absence of a low Reynolds number and long wavelength assumptions. The governing equations are to be solved completely, so that inertial effects can be studied. The numerical simulations and results are obtained by the help of a finite element method based on quadratic six-noded triangular elements equipped with a Galerkin residual procedure. The inertial effects and effects of other pertinent parameters are discussed by plotting graphs based on a finite element (FEM) solution. Trapped bolus is discussed using the graphs of streamlines. The obtained results are also compared with the results given in the literature which are highly convergent. It is concluded that velocity and the number of boluses is enhanced by an increase in Hartmann number and porosity parameter

K

Increasing inertial forces increase the velocity of flow but increasing values of the porosity parameter lead to a decrease in the pressure gradient. The study elaborates that magnetic field and porosity are useful tools to control the velocity, pressure, and boluses in the peristaltic flow pattern. Full article

(This article belongs to the Special Issue Recent Advances in the Application of Mathematical and Computational Models in Biomedical Science and Engineering)

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