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González-Hernández Y, Perré P. Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells. Metab Eng Commun 2024; 18:e00232. [PMID: 38501051 PMCID: PMC10945193 DOI: 10.1016/j.mec.2024.e00232] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/12/2024] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.
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Affiliation(s)
- Yusmel González-Hernández
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 Rue des Rouges Terres, 51110, Pomacle, France
| | - Patrick Perré
- Université Paris-Saclay, CentraleSupélec, Laboratoire de Génie des Procédés et Matériaux, Centre Européen de Biotechnologie et de Bioéconomie (CEBB), 3 Rue des Rouges Terres, 51110, Pomacle, France
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2
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Dastgerdi AK, Esrafilian A, Carty CP, Nasseri A, Barzan M, Korhonen RK, Astori I, Hall W, Saxby DJ. Sensitivity analysis of paediatric knee kinematics to the graft surgical parameters during anterior cruciate ligament reconstruction: A sequentially linked neuromusculoskeletal-finite element analysis. Comput Methods Programs Biomed 2024; 248:108132. [PMID: 38503071 DOI: 10.1016/j.cmpb.2024.108132] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/26/2024] [Accepted: 03/08/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Incidence of paediatric anterior cruciate ligament (ACL) rupture has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is typically performed to restore passive knee stability. This surgery involves replacing the failed ACL with a graft, however, surgeons must select from range of surgical parameters (e.g., type, size, insertion, and pre-tension) with no robust evidence guiding these decisions. This study presents a systemmatic computational approach to study effects of surgical parameter variation on kinematics of paediatric knees. METHODS This study used sequentially-linked neuromusculoskeletal (NMSK) finite element (FE) models of three paediatric knees to estimate the: (i) sensitivity of post-operative knee kinematics to four surgical parameters (type, size, insertion, and pre-tension) through multi-input multi-output sensitivity analysis; (ii) influence of motion and loading conditions throughout stance phase of walking gait on sensitivity indices; and (iii) influence of subject-specific anatomy (i.e., knee size) on sensitivivty indices. A previously validated FE model of the intact knee for each subject served as a reference against which ACLR knee kinematics were compared. RESULTS Sensitivity analyses revealed significant influences of surgical parameters on ACLR knee kinematics, albeit without discernible trend favouring any one parameter. Graft size and pre-tension were primary drivers of variation in knee translations and rotations, however, their effects fluctuated across stance indicating motion and loading conditions affect system sensitivity to surgical parameters. Importantly, the sensitivity of knee kinematics to surgical parameter varied across subjects, indicating geometry (i.e., knee size) influenced system sensitivity. Notably, alterations in graft parameters yielded substantial effects on kinematics (normalized root-mean-square-error > 10 %) compared to intact knee models, indicating surgical parameters vary post-operative knee kinematics. CONCLUSIONS Overall, this initial study highlights the importance of surgical parameter selection on post-operative kinematics in the paediatric ACLR knee, and provides evidence of the need for personalized surgical planning to ultimately enhance patient outcomes.
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Affiliation(s)
- Ayda Karimi Dastgerdi
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia.
| | - Amir Esrafilian
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia; Department of Orthopedics, Children's Health Queensland Hospital and Health Service, QLD, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Martina Barzan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Ivan Astori
- Department of Orthopedics, Children's Health Queensland Hospital and Health Service, QLD, Australia
| | - Wayne Hall
- School of Engineering and Built Environment, Mechanical Engineering and Industrial Design, Griffith University, Gold Coast, QLD, Australia
| | - David John Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
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Jeremiah SR, Camacho D, Park JH. Maximizing throughput in NOMA-enable industrial IoT network using digital twin and reinforcement learning. J Adv Res 2024:S2090-1232(24)00164-4. [PMID: 38653372 DOI: 10.1016/j.jare.2024.04.021] [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] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/14/2024] [Accepted: 04/20/2024] [Indexed: 04/25/2024] Open
Abstract
INTRODUCTION Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. OBJECTIVE This study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. METHOD To achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. RESULTS Simulation results demonstrate the superior efficiency of our framework, realizing up to 92% performance compared to exhaustive search methods and reducing action decision time. CONCLUSION Considering various system dynamics, the proposed framework consistently outperforms benchmark solutions, showcasing its robustness and potential for real-world IIoT applications.
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Affiliation(s)
- Sekione Reward Jeremiah
- Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.
| | - David Camacho
- School of Computer Systems Engineering, Universidad Politécnica de Madrid, Calle de Alan Turing, 28038 Madrid, Spain.
| | - Jong Hyuk Park
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea.
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Espinoza D, Tallvod S, Andersson N, Nilsson B. Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation. J Chromatogr A 2024; 1720:464805. [PMID: 38471300 DOI: 10.1016/j.chroma.2024.464805] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/14/2024]
Abstract
The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.
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Affiliation(s)
- Daniel Espinoza
- Department of Chemical Engineering, Lund University, Lund, Sweden.
| | - Simon Tallvod
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Niklas Andersson
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Bernt Nilsson
- Department of Chemical Engineering, Lund University, Lund, Sweden
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Currie GM, Hawk KE, Rohren EM. The potential role of artificial intelligence in sustainability of nuclear medicine. Radiography (Lond) 2024:S1078-8174(24)00067-1. [PMID: 38582701 DOI: 10.1016/j.radi.2024.03.005] [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] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Strategies targeted at the five pillars of sustainability (social, human, economic, ecological and environmental) can be used to improve sustainability of clinical or research practices in nuclear medicine. KEY FINDINGS While the core principle of sustainability is ensuring depletion does not exceed regeneration, this manuscript considers the balance of benefits and detriments of artificial intelligence (AI) technologies across the five pillars of sustainability. Specifically, innovations such as AI, generative AI and digital twins could enhance sustainability. While AI has the potential to address social asymmetry and inequity to drive the social and human pillars of sustainability, there is potential for widening the equity gap. AI augmentation and generative AI present economic and environmental sustainability opportunities. Deep digital twins offers clinical and research benefits in economic, ecological and environmental sustainability pillars. CONCLUSION AI, digital twins and generative AI offer potential benefits to sustainability in nuclear medicine. Despite the benefits, caution is advised because these technologies confront a number of challenges that could potentially threaten sustainability. IMPLICATIONS FOR PRACTICE AI presents opportunities for improving sustainability of nuclear medicine practice although caution is recommended to avoid unintentional undermining of sustainability across the five pillars.
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Affiliation(s)
- G M Currie
- Charles Sturt University, NSW, Australia; Baylor College of Medicine, Texas, USA.
| | - K E Hawk
- University of California San Diego, California, USA; Stanford University, California, USA
| | - E M Rohren
- Charles Sturt University, NSW, Australia; Baylor College of Medicine, Texas, USA
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Kulkarni C, Quraishi A, Raparthi M, Shabaz M, Khan MA, Varma RA, Keshta I, Soni M, Byeon H. Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer. BMC Med Inform Decis Mak 2024; 24:92. [PMID: 38575951 PMCID: PMC10996111 DOI: 10.1186/s12911-024-02495-2] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024] Open
Abstract
Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.
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Affiliation(s)
- Chaitanya Kulkarni
- Department of Computer Engineering, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, 413133, Maharashtra, India
| | - Aadam Quraishi
- M.D. Research, Intervention Treatment Institute, Houston, TX, USA
| | - Mohan Raparthi
- Software Engineer, Alphabet Life Science, Dallas, TX, 75063, USA
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India.
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Raj A Varma
- Symbiosis Law School (SLS), Symbiosis International (Deemed University) (SIU), Vimannagar, Pune, Maharashtra, India
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Dr D Y Patil Vidyapeeth, Dr. D. Y. Patil School of Science and Technology, Pune, 411033, India
| | - Haewon Byeon
- Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea, 50834
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Baldassarre A, Dion JL, Peyret N, Renaud F. Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems. Heliyon 2024; 10:e27343. [PMID: 38509954 PMCID: PMC10951540 DOI: 10.1016/j.heliyon.2024.e27343] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/20/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024] Open
Abstract
The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are integrated into the dynamic models. These can then be used without ASEKFs to predict the actual energy use of the system under the desired operating conditions, including scenarios that differ from typical functioning. The approach is validated offline with reference to the electricity consumption of an automatic coffee machine, which represents a real test environment and a blueprint to design DTs for other industrial systems. The appliance is observed by measuring the supply voltage and the absorbed current. The accuracy of the results is analyzed and discussed. This method is developed in the context of energy consumption prediction and optimization in the manufacturing industry through refined energy management and planning.
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Affiliation(s)
- A. Baldassarre
- Equipe Vibroacoustique, Structures et Formes Mécaniques - Laboratoire Quartz (EA 7393) - ISAE-Supméca, 3 rue Fernand Hainaut, Saint-Ouen CEDEX, 93407, France
| | - J.-L. Dion
- Equipe Vibroacoustique, Structures et Formes Mécaniques - Laboratoire Quartz (EA 7393) - ISAE-Supméca, 3 rue Fernand Hainaut, Saint-Ouen CEDEX, 93407, France
| | - N. Peyret
- Equipe Vibroacoustique, Structures et Formes Mécaniques - Laboratoire Quartz (EA 7393) - ISAE-Supméca, 3 rue Fernand Hainaut, Saint-Ouen CEDEX, 93407, France
| | - F. Renaud
- Equipe Vibroacoustique, Structures et Formes Mécaniques - Laboratoire Quartz (EA 7393) - ISAE-Supméca, 3 rue Fernand Hainaut, Saint-Ouen CEDEX, 93407, France
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Sardari Nia P, Ganushchak Y, Maessen J. Proposal of statistical twin as a transition to full digital twin technology for cardiovascular interventions. Interdiscip Cardiovasc Thorac Surg 2024; 38:ivae032. [PMID: 38439576 PMCID: PMC11001486 DOI: 10.1093/icvts/ivae032] [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] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 01/17/2024] [Accepted: 02/29/2024] [Indexed: 03/06/2024]
Abstract
OBJECTIVES We introduced statistical twin as aggregates of multiple virtual patients' data throughout the treatment at any chosen time point. The goal of this manuscript was to provide the proof of concept of statistical twin by evaluating the feasibility of detection of distinctive aggregates of patients throughout the perioperative trajectory (prerequisite for development of statistical twin). METHODS We used a retrospective validated cohort of all comers with mitral valve disease treated (2014-2020) at a tertiary academic hospital. The end point was overall survival based on the decision of the heart team. We applied two-step cluster analysis to detect distinct aggregated virtual patients throughout the process of care. RESULTS The cluster procedure resulted in 5 distant clusters with relatively equal numbers of patients. Effects of the treatment (surgery, transcatheter or optimal medical therapy) on survival were as follows: For optimal medical therapy, the expected survival ranged from 95% to 96% in 30 days to 58% to 75% in 10 years independent of baseline characteristics. However, for transcatheter interventions, the 5-year survival was 60-92% and was dependant on the initial characteristics of the virtual patient. Furthermore, survival following an uncomplicated operation of normal duration was higher through all observation periods. The aggregated virtual patients of cluster 5 would have a better survival rate at all times if the intervention were done by a dedicated surgeon. CONCLUSIONS It is possible to detect distinctive aggregates of virtual patients based on baseline characteristics and to capture the impact of perioperative events and external and other factors at multiple time points throughout the postoperative phase.
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Affiliation(s)
- Peyman Sardari Nia
- Department of Cardiothoracic Surgery, Maastricht University Medical Centre, Maastricht, the Netherlands
- Foundation Heart Team Academy, Maastricht, the Netherlands
| | - Yuri Ganushchak
- Department of Cardiothoracic Surgery, Maastricht University Medical Centre, Maastricht, the Netherlands
- Foundation Heart Team Academy, Maastricht, the Netherlands
| | - Jos Maessen
- Department of Cardiothoracic Surgery, Maastricht University Medical Centre, Maastricht, the Netherlands
- Foundation Heart Team Academy, Maastricht, the Netherlands
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Dihan MS, Akash AI, Tasneem Z, Das P, Das SK, Islam MR, Islam MM, Badal FR, Ali MF, Ahamed MH, Abhi SH, Sarker SK, Hasan MM. Digital twin: Data exploration, architecture, implementation and future. Heliyon 2024; 10:e26503. [PMID: 38444502 PMCID: PMC10912257 DOI: 10.1016/j.heliyon.2024.e26503] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/07/2024] Open
Abstract
A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.
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Affiliation(s)
- Md. Shezad Dihan
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Anwar Islam Akash
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Zinat Tasneem
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Prangon Das
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Sajal Kumar Das
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md. Robiul Islam
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md. Manirul Islam
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Faisal R. Badal
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md. Firoj Ali
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md. Hafiz Ahamed
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Sarafat Hussain Abhi
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Subrata Kumar Sarker
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md. Mehedi Hasan
- Department of Mechatronics Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
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Zhang J, Wan C, Wang J, Chen C, Wang T, Zhang R, Guo H. Research on the "shape-performance-control" integrated digital twin system for boom-type roadheaders. Sci Rep 2024; 14:5780. [PMID: 38461195 DOI: 10.1038/s41598-024-56539-8] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/07/2024] [Indexed: 03/11/2024] Open
Abstract
The boom-type roadheader plays a crucial role in coal mining. However, conducting the real-time monitoring of the mechanical performance and comprehensive adaptive cutting in the dynamic cutting process are challenging. To address these issues, a digital twin system that integrates the elements of "shape, performance, and control" for roadheaders is presented in this paper. The system comprises three components: physical space, service space, and twin space. The service space forms the core of the entire system. Within this space, twin models and control models are created using numerical simulation, artificial intelligence and multi-source data fusion technology. These models serve the purpose of predicting the roadheader's mechanical performance and controlling the swing speed of the cutting arm. The physical space is built using technologies such as robot kinematics, electrical systems, hydraulic transmission, and other relevant techniques. This approach facilitates the transmission of multi-sensor data to twin models. The control model then manages the roadheader's function based on the output signals from the control model. The twin space is constructed utilizing physical rendering engines, databases, and 3D modelling tools. This space visualizes and stores the movement, performance, and control parameters of the roadheader. The results demonstrate that the average absolute error between the measured data from the test's three position strain gauges and the predicted data from the twin system is 10.38 MPa. Furthermore, the twin system achieves an average update interval of 0.34 s, allowing real-time stress monitoring of the structural components of the roadheader and preventing damage caused by overload. The proposed control model enables adaptive adjustment of the swing speed of the cutting arm in approximately 0.3 s. This improvement significantly enhances the adaptive cutting capabilities of roadheaders when dealing with complex coal and rock formations.
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Affiliation(s)
- Jianzhuo Zhang
- College of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China
| | - Chuanxu Wan
- College of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China.
| | - Jie Wang
- College of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China
| | - Ce Chen
- College of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China
| | - Tao Wang
- College of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China
| | - Runfeng Zhang
- Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control, School of Mechanical Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Hao Guo
- College of Mechanical Engineering, Liaoning Technical University, Fuxin, 123000, China
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Eminaga O, Abbas M, Kunder C, Tolkach Y, Han R, Brooks JD, Nolley R, Semjonow A, Boegemann M, West R, Long J, Fan RE, Bettendorf O. Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology. Sci Rep 2024; 14:5284. [PMID: 38438436 PMCID: PMC10912767 DOI: 10.1038/s41598-024-55228-w] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 02/21/2024] [Indexed: 03/06/2024] Open
Abstract
Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artificial intelligence (AI) shows promise in detecting prostate cancer and grading patterns. We tested an AI-based digital twin of a pathologist, vPatho, on 2603 histological images of prostate tissue stained with hematoxylin and eosin. We analyzed various factors influencing tumor grade discordance between the vPatho system and six human pathologists. Our results demonstrated that vPatho achieved comparable performance in prostate cancer detection and tumor volume estimation, as reported in the literature. The concordance levels between vPatho and human pathologists were examined. Notably, moderate to substantial agreement was observed in identifying complementary histological features such as ductal, cribriform, nerve, blood vessel, and lymphocyte infiltration. However, concordance in tumor grading decreased when applied to prostatectomy specimens (κ = 0.44) compared to biopsy cores (κ = 0.70). Adjusting the decision threshold for the secondary Gleason pattern from 5 to 10% improved the concordance level between pathologists and vPatho for tumor grading on prostatectomy specimens (κ from 0.44 to 0.64). Potential causes of grade discordance included the vertical extent of tumors toward the prostate boundary and the proportions of slides with prostate cancer. Gleason pattern 4 was particularly associated with this population. Notably, the grade according to vPatho was not specific to any of the six pathologists involved in routine clinical grading. In conclusion, our study highlights the potential utility of AI in developing a digital twin for a pathologist. This approach can help uncover limitations in AI adoption and the practical application of the current grading system for prostate cancer pathology.
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Affiliation(s)
| | - Mahmoud Abbas
- Department of Pathology, Prostate Center, University Hospital Muenster, Muenster, Germany.
| | - Christian Kunder
- Department of Pathology, Stanford University School of Medicine, Stanford, USA
| | - Yuri Tolkach
- Department of Pathology, Cologne University Hospital, Cologne, Germany
| | - Ryan Han
- Department of Computer Science, Stanford University, Stanford, USA
| | - James D Brooks
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Rosalie Nolley
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
| | - Axel Semjonow
- Department of Urology, Prostate Center, University Hospital Muenster, Muenster, Germany
| | - Martin Boegemann
- Department of Urology, Prostate Center, University Hospital Muenster, Muenster, Germany
| | - Robert West
- Department of Pathology, Cologne University Hospital, Cologne, Germany
| | - Jin Long
- Department of Pediatrics, Stanford University School of Medicine, Stanford, USA
| | - Richard E Fan
- Department of Urology, Stanford University School of Medicine, Stanford, CA, USA
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12
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Carbonaro D, Chiastra C, Bologna FA, Audenino AL, Terzini M. Determining the Mechanical Properties of Super-Elastic Nitinol Bone Staples Through an Integrated Experimental and Computational Calibration Approach. Ann Biomed Eng 2024; 52:682-694. [PMID: 38151644 DOI: 10.1007/s10439-023-03416-6] [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: 07/27/2023] [Accepted: 12/01/2023] [Indexed: 12/29/2023]
Abstract
Super-elastic bone staples have emerged as a safe and effective alternative for internal fixation. Nevertheless, several biomechanical aspects of super-elastic staples are still unclear and require further exploration. Within this context, this study presents a combined experimental and computational approach to investigate the mechanical characteristics of super-elastic staples. Two commercially available staples with distinct geometry, characterized by two and four legs, respectively, were examined. Experimental four-point bending tests were conducted to evaluate staple performance in terms of generated forces. Subsequently, a finite element-based calibration procedure was developed to capture the unique super-elastic behavior of the staple materials. Finally, a virtual bench testing framework was implemented to separate the effect of geometry from that of the material characteristics on the mechanical properties of the devices, including generated force, strain distribution, and fatigue behavior. The experimental tests indicated differences in the force vs. displacement curves between staples. The material calibration procedure revealed marked differences in the super-elastic properties of the materials employed in staple 1 and staple 2. The results obtained from the virtual bench testing framework have showed that both geometric features and material characteristics had a substantial impact on the mechanical properties of the device, especially on the generated force, whereas their effect on strain distribution and fatigue behavior was comparatively less pronounced. To conclude, this study advances the biomechanical understanding of Nitinol super-elastic staples by separately investigating the impact of geometry and material characteristics on the mechanical properties of two commercially available devices.
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Affiliation(s)
- Dario Carbonaro
- PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Turin, Italy.
| | - Claudio Chiastra
- PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Turin, Italy
| | - Federico A Bologna
- PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Turin, Italy
| | - Alberto L Audenino
- PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Turin, Italy
| | - Mara Terzini
- PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Turin, Italy
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13
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Zi X, Gao S, Xie Y. An online monitoring method of milling cutter wear condition driven by digital twin. Sci Rep 2024; 14:4956. [PMID: 38418504 DOI: 10.1038/s41598-024-55551-2] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024] Open
Abstract
Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process.
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Affiliation(s)
- Xintian Zi
- Taian Haina Spindle Science & Technology Co., Ltd, Taian, 271000, China
| | - Shangshang Gao
- School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212000, China
| | - Yang Xie
- School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212000, China.
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14
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Xu S, Lu Y, Yu C. Augmented reality-assisted cloud additive manufacturing with digital twin technology for multi-stakeholder value Co-creation in product innovation. Heliyon 2024; 10:e25722. [PMID: 38384529 PMCID: PMC10878916 DOI: 10.1016/j.heliyon.2024.e25722] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
Industry 4.0, boosting the integration of sophisticated computational and manufacturing technologies, has profoundly reshaped the way to deal with market dynamics. It increases the capabilities of multi-stakeholders to engage in value co-creation for product innovation. Nonetheless, it also poses challenges to operation efficiency, i.e., surged requirements meeting expedited delivery times, in account of stakeholders' diverse backgrounds and goals. To respond, by applying the technology of Digital Twin (DT), this study proposes an Augmented Reality-assisted Cloud Additive Manufacturing (AR-CAM) framework, establishing a sophisticated cyber-physical interface to cater to various coordinating interactions. By this means, it attempts to give a consistent understanding to multi-background stakeholders. A case study involving the fabrication of a speed rear derailleur is applied, thereby underscoring the validity of the AR-CAM framework.
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Affiliation(s)
- Shengyang Xu
- Design-AI Lab, China Academy of Art, China
- School of Art & Design, University of Illinois Urbana Champaign, USA
| | - Yuan Lu
- Department of Exhibition and Education, Zhejiang Science and Technology Museum, China
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15
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Coelho J, Oliveira T, Neves C, Karatzas S. Adoption of digital twins as a sustainable energy solution: Determinants to adoption in household. Heliyon 2024; 10:e25782. [PMID: 38375314 PMCID: PMC10875425 DOI: 10.1016/j.heliyon.2024.e25782] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/23/2024] [Accepted: 02/01/2024] [Indexed: 02/21/2024] Open
Abstract
Digital Twin (DT) consists of a recent technology that can enable sustainability. However, Digital Twins are still in early stages of adoption, especially in households, and so the determinants to this adoption have not yet been determined. The aim of this study is to fill this research gap through providing a conceptual model of the drivers to the adoption of Digital Twins in households and it's relation to well-being. This study is developed as a mixed-methods research. The model is produced qualitatively, based on literature discoveries and key findings from interviews with experts and possible consumers. Afterwards, the model was validated with data collected through a questionnaire with 149 respondents. Results show that a set of informational, social, environmental and utility factors can influence the intention to adopt Digital Twins as a sustainable energy solution, and consequently the perceived well-being.
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Affiliation(s)
- Joana Coelho
- NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal
| | - Tiago Oliveira
- NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal
| | - Catarina Neves
- NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal
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16
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Hwang GM, Kulwatno J, Cruz TH, Chen D, Ajisafe T, Monaco JD, Nitkin R, George SM, Lucas C, Zehnder SM, Zhang LT. NSF DARE-transforming modeling in neurorehabilitation: perspectives and opportunities from US funding agencies. J Neuroeng Rehabil 2024; 21:17. [PMID: 38310271 PMCID: PMC10837948 DOI: 10.1186/s12984-024-01308-x] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/24/2024] [Indexed: 02/05/2024] Open
Abstract
In recognition of the importance and timeliness of computational models for accelerating progress in neurorehabilitation, the U.S. National Science Foundation (NSF) and the National Institutes of Health (NIH) sponsored a conference in March 2023 at the University of Southern California that drew global participation from engineers, scientists, clinicians, and trainees. This commentary highlights promising applications of computational models to understand neurorehabilitation ("Using computational models to understand complex mechanisms in neurorehabilitation" section), improve rehabilitation care in the context of digital twin frameworks ("Using computational models to improve delivery and implementation of rehabilitation care" section), and empower future interdisciplinary workforces to deliver higher-quality clinical care using computational models ("Using computational models in neurorehabilitation requires an interdisciplinary workforce" section). The authors describe near-term gaps and opportunities, all of which encourage interdisciplinary team science. Four major opportunities were identified including (1) deciphering the relationship between engineering figures of merit-a term commonly used by engineers to objectively quantify the performance of a device, system, method, or material relative to existing state of the art-and clinical outcome measures, (2) validating computational models from engineering and patient perspectives, (3) creating and curating datasets that are made publicly accessible, and (4) developing new transdisciplinary frameworks, theories, and models that incorporate the complexities of the nervous and musculoskeletal systems. This commentary summarizes U.S. funding opportunities by two Federal agencies that support computational research in neurorehabilitation. The NSF has funding programs that support high-risk/high-reward research proposals on computational methods in neurorehabilitation informed by theory- and data-driven approaches. The NIH supports the development of new interventions and therapies for a wide range of nervous system injuries and impairments informed by the field of computational modeling. The conference materials can be found at https://dare2023.usc.edu/ .
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Affiliation(s)
- Grace M Hwang
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA.
| | - Jonathan Kulwatno
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Theresa H Cruz
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Daofen Chen
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA
| | - Toyin Ajisafe
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Joseph D Monaco
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Rockville, MD, 20852, USA
| | - Ralph Nitkin
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, 20817, USA
| | - Stephanie M George
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Carol Lucas
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Steven M Zehnder
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
| | - Lucy T Zhang
- Directorate for Engineering, National Science Foundation, 2415 Eisenhower Avenue, Alexandria, VA, 22314, USA
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17
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Sen A, Navarro L, Avril S, Aguirre M. A data-driven computational methodology towards a pre-hospital Acute Ischaemic Stroke screening tool using haemodynamics waveforms. Comput Methods Programs Biomed 2024; 244:107982. [PMID: 38134647 DOI: 10.1016/j.cmpb.2023.107982] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 12/24/2023]
Abstract
BACKGROUND AND OBJECTIVE Acute Ischaemic Stroke (AIS), a significant global health concern, results from occlusions in cerebral arteries, causing irreversible brain damage. Different type of treatments exist depending on the size and location of the occlusion. Challenges persist in achieving faster diagnosis and treatment, which needs to happen in the first hours after the onset of symptoms to maximize the chances of patient recovery. The current diagnostic pipeline, i.e. "drip and ship", involves diagnostic via advanced imaging tools, only available in large clinical facilities, which poses important delays. This study investigates the feasibility of developing a machine learning model to diagnose and locate occluding blood clots from velocity waveforms, which can be easily be obtained with portable devices such as Doppler Ultrasound. The goal is to explore this approach as a cost-effective and time-efficient alternative to advanced imaging techniques typically available only in large hospitals. METHODS Simulated haemodynamic data is used to conduct blood flow simulations representing healthy and different AIS scenarios using a population-based database. A Machine Learning classification model is trained to solve the inverse problem, this is, detect and locate a potentially occluding thrombus from measured waveforms. The classification process involves two steps. First, the region where the thrombus is located is classified into nine groups, including healthy, left or right large vessel occlusion, left or right anterior cerebral artery, and left or right posterior cerebral artery. In a second step, the bifurcation generation of the thrombus location is classified as small, medium, or large vessel occlusion. RESULTS The proposed methodology is evaluated for data without noise, achieving a true prediction rate exceeding 95% for both classification steps mentioned above. The inclusion of up to 20% noise reduces the true prediction rate to 80% for region detection and 70% for bifurcation generation detection. CONCLUSIONS This study demonstrates the potential effectiveness and efficiency of using haemodynamic data and machine learning to detect and locate occluding thrombi in AIS patients. Although the geometric and topological data used in this study are idealized, the results suggest that this approach could be applicable in real-world situations with appropriate adjustments. Source code is available in https://github.com/ahmetsenemse/Acute-Ischaemic-Stroke-screening-tool-.
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Affiliation(s)
- Ahmet Sen
- Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059 Sainbiose, F-42023, Saint-Etienne, France
| | - Laurent Navarro
- Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059 Sainbiose, F-42023, Saint-Etienne, France
| | - Stephane Avril
- Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059 Sainbiose, F-42023, Saint-Etienne, France.
| | - Miquel Aguirre
- Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059 Sainbiose, F-42023, Saint-Etienne, France; Laboratori de Càlcul Numèric, Universitat Politècnica de Catalunya, Jordi Girona 1, E-08034, Barcelona, Spain; International Centre for Numerical Methods in Engineering (CIMNE), Gran Capità, 08034, Barcelona, Spain.
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18
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Kabus D, De Coster T, de Vries AAF, Pijnappels DA, Dierckx H. Fast creation of data-driven low-order predictive cardiac tissue excitation models from recorded activation patterns. Comput Biol Med 2024; 169:107949. [PMID: 38199206 DOI: 10.1016/j.compbiomed.2024.107949] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 12/07/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024]
Abstract
Excitable systems give rise to important phenomena such as heat waves, epidemics and cardiac arrhythmias. Understanding, forecasting and controlling such systems requires reliable mathematical representations. For cardiac tissue, computational models are commonly generated in a reaction-diffusion framework based on detailed measurements of ionic currents in dedicated single-cell experiments. Here, we show that recorded movies at the tissue-level of stochastic pacing in a single variable are sufficient to generate a mathematical model. Via exponentially weighed moving averages, we create additional state variables, and use simple polynomial regression in the augmented state space to quantify excitation wave dynamics. A spatial gradient-sensing term replaces the classical diffusion as it is more robust to noise. Our pipeline for model creation is demonstrated for an in-silico model and optical voltage mapping recordings of cultured human atrial myocytes and only takes a few minutes. Our findings have the potential for widespread generation, use and on-the-fly refinement of personalised computer models for non-linear phenomena in biology and medicine, such as predictive cardiac digital twins.
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Affiliation(s)
- Desmond Kabus
- Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Etienne Sabbelaan 53, 8500, Kortrijk, Belgium; Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Tim De Coster
- Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Antoine A F de Vries
- Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Daniël A Pijnappels
- Laboratory of Experimental Cardiology, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Hans Dierckx
- Department of Mathematics, KU Leuven Campus Kortrijk (KULAK), Etienne Sabbelaan 53, 8500, Kortrijk, Belgium.
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19
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Caljé-van der Klei T, Sun Q, Chase JG, Zhou C, Tawhai MH, Knopp JL, Möller K, Heines SJ, Bergmans DC, Shaw GM. Pulmonary response prediction through personalized basis functions in a virtual patient model. Comput Methods Programs Biomed 2024; 244:107988. [PMID: 38171168 DOI: 10.1016/j.cmpb.2023.107988] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/16/2023] [Accepted: 12/17/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND AND OBJECTIVE Recruitment maneuvers with subsequent positive-end-expiratory-pressure (PEEP) have proven effective in recruiting lung volume and preventing alveoli collapse. However, determining a safe, effective, and patient-specific PEEP is not standardized, and this more optimal PEEP level evolves with patient condition, requiring personalised monitoring and care approaches to maintain optimal ventilation settings. METHODS This research examines 3 physiologically relevant basis function sets (exponential, parabolic, cumulative) to enable better prediction of elastance evolution for a virtual patient or digital twin model of MV lung mechanics, including novel elements to model and predict distension elastance. Prediction accuracy and robustness are validated against recruitment maneuver data from 18 volume-controlled ventilation (VCV) patients at 7 different baseline PEEP levels (0 to 12 cmH2O) and 14 pressure-controlled ventilation (PCV) patients at 4 different baseline PEEP levels (6 to 12 cmH2O), yielding 623 and 294 prediction cases, respectively. Predictions were made up to 12 cmH2O of added PEEP ahead, covering 6 × 2 cmH2O PEEP steps. RESULTS The 3 basis function sets yield median absolute peak inspiratory pressure (PIP) prediction error of 1.63 cmH2O for VCV patients, and median peak inspiratory volume (PIV) prediction error of 0.028 L for PCV patients. The exponential basis function set yields a better trade-off of overall performance across VCV and PCV prediction than parabolic and cumulative basis function sets from other studies. Comparing predicted and clinically measured distension prediction in VCV demonstrated consistent, robust high accuracy with R2 = 0.90-0.95. CONCLUSIONS The results demonstrate recruitment mechanics are best captured by an exponential basis function across different mechanical ventilation modes, matching physiological expectations, and accurately capture, for the first time, distension mechanics to within 5-10 % accuracy. Enabling the risk of lung injury to be predicted before changing ventilator settings. The overall outcomes significantly extend and more fully validate this digital twin or virtual mechanical ventilation patient model.
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Affiliation(s)
- Trudy Caljé-van der Klei
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
| | - Qianhui Sun
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand; University of Liége, Liége, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Cong Zhou
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Merryn H Tawhai
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Jennifer L Knopp
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Knut Möller
- Institute for Technical Medicine, Furtwangen University, Villingen-Schwenningen, Germany
| | - Serge J Heines
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
| | - Dennis C Bergmans
- Department of Intensive Care, School of Medicine, Maastricht University, Maastricht, Netherlands
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Christchurch, New Zealand
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20
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Alonso R, Locci R, Reforgiato Recupero D. Improving digital twin experience through big data, IoT and social analysis: An architecture and a case study. Heliyon 2024; 10:e24741. [PMID: 38304842 PMCID: PMC10830541 DOI: 10.1016/j.heliyon.2024.e24741] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/08/2023] [Accepted: 01/12/2024] [Indexed: 02/03/2024] Open
Abstract
Industries such as construction and business companies are becoming increasingly digitized. The amount of data to be monitored and processed has increased significantly since the advent of the Internet of Things and the massive use of sensors. In addition to the data from these sensors, large amounts of data that require specific handling and processing are received. Much of this data is eventually represented in digital twins as a monitoring or decision-support tool. In this paper, we present an architecture to improve digital twin-based experiences that need to represent information from multiple sources. This architecture is demonstrated using the specific use case of a digital twin for an office of an Italian company. The implementation leverages the Matterport 3D media platform and integrates different technologies and sensors. An evaluation of the solution has also been carried out. The results show high user acceptance and the opening of multiple possibilities to enrich the virtual model with further data from different sources.
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Affiliation(s)
- Rubén Alonso
- ICT Division, R2M Solution s.r.l., Via Fratelli Cuzio, 42, Pavia, 27100, Italy
- Programa de Doctorado. Centro de Automática y Robótica, Universidad Politécnica de Madrid-CSIC, Madrid, Spain
| | - Riccardo Locci
- Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, Cagliari, 09121, Italy
| | - Diego Reforgiato Recupero
- ICT Division, R2M Solution s.r.l., Via Fratelli Cuzio, 42, Pavia, 27100, Italy
- Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, Cagliari, 09121, Italy
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21
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Chumnanvej S, Chumnanvej S, Tripathi S. Assessing the benefits of digital twins in neurosurgery: a systematic review. Neurosurg Rev 2024; 47:52. [PMID: 38236336 DOI: 10.1007/s10143-023-02260-5] [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] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/17/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
Digital twins are virtual replicas of their physical counterparts, and can assist in delivering personalized surgical care. This PRISMA guideline-based systematic review evaluates current literature addressing the effectiveness and role of digital twins in many stages of neurosurgical management. The aim of this review is to provide a high-quality analysis of relevant, randomized controlled trials and observational studies addressing the neurosurgical applicability of a variety of digital twin technologies. Using pre-specified criteria, we evaluated 25 randomized controlled trials and observational studies on the applications of digital twins, including navigation, robotics, and image-guided neurosurgeries. All 25 studies compared these technologies against usual surgical approaches. Risk of bias analyses using the Cochrane risk of bias tool for randomized trials (Rob 2) found "low" risk of bias in the majority of studies (23/25). Overall, this systematic review shows that digital twin applications have the potential to be more effective than conventional neurosurgical approaches when applied to brain and spinal surgery. Moreover, the application of these novel technologies may also lead to fewer post-operative complications.
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Affiliation(s)
- Sorayouth Chumnanvej
- Neurosurgery Division, Department of Surgery, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Siriluk Chumnanvej
- Department of Anesthesiology and Operating Room, Phramongkutklao Hospital, Bangkok, Thailand
| | - Susmit Tripathi
- Department of Neurology, New York Presbyterian-Weill Cornell Medical Center, New York, NY, USA.
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Mattioli F, Maglianella V, D'Antonio S, Trimarco E, Caligiore D. Non-invasive brain stimulation for patients and healthy subjects: Current challenges and future perspectives. J Neurol Sci 2024; 456:122825. [PMID: 38103417 DOI: 10.1016/j.jns.2023.122825] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 11/22/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
Non-invasive brain stimulation (NIBS) techniques have a rich historical background, yet their utilization has witnessed significant growth only recently. These techniques encompass transcranial electrical stimulation and transcranial magnetic stimulation, which were initially employed in neuroscience to explore the intricate relationship between the brain and behaviour. However, they are increasingly finding application in research contexts as a means to address various neurological, psychiatric, and neurodegenerative disorders. This article aims to fulfill two primary objectives. Firstly, it seeks to showcase the current state of the art in the clinical application of NIBS, highlighting how it can improve and complement existing treatments. Secondly, it provides a comprehensive overview of the utilization of NIBS in augmenting the brain function of healthy individuals, thereby enhancing their performance. Furthermore, the article delves into the points of convergence and divergence between these two techniques. It also addresses the existing challenges and future prospects associated with NIBS from ethical and research standpoints.
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Affiliation(s)
- Francesco Mattioli
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199 Rome, Italy; School of Computing, Electronics and Mathematics, University of Plymouth, Drake Circus, Plymouth PL4 8AA, United Kingdom
| | - Valerio Maglianella
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
| | - Sara D'Antonio
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
| | - Emiliano Trimarco
- Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
| | - Daniele Caligiore
- AI2Life s.r.l., Innovative Start-Up, ISTC-CNR Spin-Off, Via Sebino 32, 00199 Rome, Italy; Computational and Translational Neuroscience Laboratory, Institute of Cognitive Sciences and Technologies, National Research Council (CTNLab-ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy.
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23
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Bhoyar S, Kumar V, Foster M, Xu X, Traylor SJ, Guo J, Lenhoff AM. Predictive mechanistic modeling of loading and elution in protein A chromatography. J Chromatogr A 2024; 1713:464558. [PMID: 38096684 DOI: 10.1016/j.chroma.2023.464558] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/01/2023] [Accepted: 12/03/2023] [Indexed: 01/08/2024]
Abstract
Protein A chromatography is an enabling technology in current manufacturing processes of monoclonal antibodies (mAbs) and mAb derivatives, largely due to its ability to reduce the levels of process-related impurities by several orders of magnitude. Despite its widespread application, the use of mathematical modeling capable of accurately predicting the full protein A chromatographic process, including loading, post-loading wash and elution stages, has been limited. This work describes a mechanistic modeling approach utilizing the general rate model (GRM), the capabilities of which are explored and optimized using two isotherm models. Isotherm parameters were estimated by inverse-fitting simulated breakthrough curves to experimental data at various pH values. The parameter values so obtained were interpolated across the relevant pH range using a best-fit curve, thus enabling their use in predictive modeling, including of elution over a range of pH. The model provides accurate predictions (< 3% mean error in 10% dynamic binding capacity predictions and ∼ 5% mean error in elution mass and pool volume predictions, both on scale-up) for various residence times, buffer conditions and elution schemes and its effectiveness for use in scale-up and process development is shown by applying the same parameters to larger columns and a wider range of residence times.
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Affiliation(s)
- Soumitra Bhoyar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
| | - Vijesh Kumar
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
| | - Max Foster
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
| | - Xuankuo Xu
- Biologics Development, Bristol Myers Squibb Co, Devens, MA 01434, USA
| | - Steven J Traylor
- Biologics Development, Bristol Myers Squibb Co, Devens, MA 01434, USA
| | - Jing Guo
- Biologics Development, Bristol Myers Squibb Co, Devens, MA 01434, USA
| | - Abraham M Lenhoff
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA.
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24
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Severinsen I, Yu W, Walmsley T, Young B. COVERT: A classless approach to generating balanced datasets for process modelling. ISA Trans 2024; 144:1-10. [PMID: 37951753 DOI: 10.1016/j.isatra.2023.10.031] [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] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 09/04/2023] [Accepted: 10/27/2023] [Indexed: 11/14/2023]
Abstract
In this work, a classless oversampling technique, Covert, was developed to improve historical datasets from industrial processing plants to aid process modelling. Using kernel density estimation and nearest neighbour algorithms, sparse regions are identified and resampled, developing a more balanced dataset. When applied to a real dataset from a geothermal power plant, Covert outperforms current best practice (Smote) in uniformly populating the input feature space and generating credible data in the output variable. When used to develop a data-driven model Covert improved model accuracy by 20% when predicting outside the original data's feature space. Smote, however, reduced model accuracy by 6% in the same feature space. Developing reliable models of industrial processes continues to be a significant hurdle in developing a digital twin. Using Covert, existing imbalanced historical data can be used to extend the range of applicability of any process model.
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Affiliation(s)
- Isaac Severinsen
- Department of Chemical and Materials Engineering, University of Auckland, 5 Grafton Road, Auckland, 1010, New Zealand
| | - Wei Yu
- Department of Chemical and Materials Engineering, University of Auckland, 5 Grafton Road, Auckland, 1010, New Zealand
| | - Timothy Walmsley
- Ahuora - Centre for Smart Energy Systems, School of Engineering, The University of Waikato, Gate 8, Hillcrest Road, Hamilton, 3240, New Zealand
| | - Brent Young
- Department of Chemical and Materials Engineering, University of Auckland, 5 Grafton Road, Auckland, 1010, New Zealand.
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25
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Idei H, Yamashita Y. Elucidating multifinal and equifinal pathways to developmental disorders by constructing real-world neurorobotic models. Neural Netw 2024; 169:57-74. [PMID: 37857173 DOI: 10.1016/j.neunet.2023.10.005] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023]
Abstract
Vigorous research has been conducted to accumulate biological and theoretical knowledge about neurodevelopmental disorders, including molecular, neural, computational, and behavioral characteristics; however, these findings remain fragmentary and do not elucidate integrated mechanisms. An obstacle is the heterogeneity of developmental pathways causing clinical phenotypes. Additionally, in symptom formations, the primary causes and consequences of developmental learning processes are often indistinguishable. Herein, we review developmental neurorobotic experiments tackling problems related to the dynamic and complex properties of neurodevelopmental disorders. Specifically, we focus on neurorobotic models under predictive processing lens for the study of developmental disorders. By constructing neurorobotic models with predictive processing mechanisms of learning, perception, and action, we can simulate formations of integrated causal relationships among neurodynamical, computational, and behavioral characteristics in the robot agents while considering developmental learning processes. This framework has the potential to bind neurobiological hypotheses (excitation-inhibition imbalance and functional disconnection), computational accounts (unusual encoding of uncertainty), and clinical symptoms. Developmental neurorobotic approaches may serve as a complementary research framework for integrating fragmented knowledge and overcoming the heterogeneity of neurodevelopmental disorders.
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Affiliation(s)
- Hayato Idei
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo 187-8502, Japan.
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26
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Pringle GJ. Computational Biomedicine (CompBioMed) Centre of Excellence: Selected Key Achievements. Methods Mol Biol 2024; 2716:31-49. [PMID: 37702935 DOI: 10.1007/978-1-0716-3449-3_3] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
CompBioMed is a Centre of Excellence for High Performance Computing Applications, funded by the European Commission's Horizon 2020 program, running from October 1, 2017, to April 1, 2024. CompBioMed develops computer-based tools to simulate each human body in health and disease. The author provides a general overview and then presents his personal opinion on key achievements: collaborations between industry and academia, the two IMAX short films, training to foster a culture of HPC among biomedical practitioners, our free service to port and tune biomedical applications to HPC, providing future supercomputers access to surgery, and bringing FDA-endorsed credibility to biomedical simulations.
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27
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Wan S, Coveney PV. Introduction to Computational Biomedicine. Methods Mol Biol 2024; 2716:1-13. [PMID: 37702933 DOI: 10.1007/978-1-0716-3449-3_1] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
The domain of computational biomedicine is a new and burgeoning one. Its areas of concern cover all scales of human biology, physiology, and pathology, commonly referred to as medicine, from the genomic to the whole human and beyond, including epidemiology and population health. Computational biomedicine aims to provide high-fidelity descriptions and predictions of the behavior of biomedical systems of both fundamental scientific and clinical importance. Digital twins and virtual humans aim to reproduce the extremely accurate duplicate of real-world human beings in cyberspace, which can be used to make highly accurate predictions that take complicated conditions into account. When that can be done reliably enough for the predictions to be actionable, such an approach will make an impact in the pharmaceutical industry by reducing or even replacing the extremely laboratory-intensive preclinical process of making and testing compounds in laboratories, and in clinical applications by assisting clinicians to make diagnostic and treatment decisions.
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Affiliation(s)
- Shunzhou Wan
- Department of Chemistry, Centre for Computational Science, University College London, London, UK
| | - Peter V Coveney
- Department of Chemistry, Centre for Computational Science, University College London, London, UK.
- Advanced Research Computing Centre, University College London, London, UK.
- Computational Science Laboratory, Institute for Informatics, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
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28
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Monji-Azad S, Männle D, Hesser J, Pohlmann J, Rotter N, Affolter A, Weis CA, Ludwig S, Scherl C. Point Cloud Registration for Measuring Shape Dependence of Soft Tissue Deformation by Digital Twins in Head and Neck Surgery. Biomed Hub 2024; 9:9-15. [PMID: 38322041 PMCID: PMC10845096 DOI: 10.1159/000535421] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/20/2023] [Indexed: 02/08/2024] Open
Abstract
Introduction A 2½ D point cloud registration method was developed to generate digital twins of different tissue shapes and resection cavities by applying a machine learning (ML) approach. This demonstrates the feasibility of quantifying soft tissue shifts. Methods An ML model was trained using simulated surface scan data obtained from tumor resections in a pig head cadaver model. It hereby uses 438 2½ D scans of the tissue surface. Tissue shift was induced by a temperature change from 7.91 ± 4.1°C to 36.37 ± 1.28°C. Results Digital twins were generated from various branched and compact resection cavities (RCs) and cut tissues (CT). A temperature increase induced a tissue shift with a significant volume increase of 6 mL and 2 mL in branched and compact RCs, respectively (p = 0.0443; 0.0157). The volumes of branched and compact CT were decreased by 3 and 4 mL (p < 0.001). In the warm state, RC and CT no longer fit together because of the significant tissue deformation. Although not significant, the compact RC showed a greater tissue deformation of 1 μL than the branched RC with 0.5 μL induced by the temperature change (p = 0.7874). The branched and compact CT forms responded almost equally to changes in temperature (p = 0.1461). Conclusions The simulation experiment of induced soft tissue deformation using digital twins based on 2½ D point cloud models proved that our method helps to quantify shape-dependent tissue shifts.
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Affiliation(s)
- Sara Monji-Azad
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - David Männle
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Jürgen Hesser
- Mannheim Institute for Intelligent Systems in Medicine (MIISM), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- AI Health Innovation Cluster, Heidelberg-Mannheim Health and Life Science Alliance, Heidelberg, Germany
- Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany
- Central Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
- CZS Heidelberg Center for Model-Based AI, Heidelberg University, Heidelberg, Germany
| | - Jan Pohlmann
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Nicole Rotter
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Annette Affolter
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Cleo Aron Weis
- Pathological Institute, Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Sonja Ludwig
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Claudia Scherl
- Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- AI Health Innovation Cluster, Heidelberg-Mannheim Health and Life Science Alliance, Heidelberg, Germany
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29
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Brown OR, Hullender DA. Darwinian evolution has become dogma; AI can rescue what is salvageable. Prog Biophys Mol Biol 2024; 186:53-56. [PMID: 38145808 DOI: 10.1016/j.pbiomolbio.2023.12.001] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/06/2023] [Accepted: 12/22/2023] [Indexed: 12/27/2023]
Abstract
Artificial Intelligence (AI), as an academic discipline, is traceable to the mid-1950s but it is currently exploding in applications with successes and concerns. AI can be defined as intelligence demonstrated by computers, with intelligence difficult to define but it must include concepts of ability to learn, reason, and generalize from a vast amount of information and, we propose, to infer meaning. The type of AI known as general AI, has strong, but unrealized potential both for assessing and also for solving major problems with the scientific theory of Darwinian evolution, including its modern variants and for origin of life studies. Specifically, AI should be applied first to evaluate the strengths and weaknesses of the assumptions and empirical information underpinning theories of the origin of life and probability of its evolution. AI should then be applied to assess the scientific validity of the theory of how abundant life came to be on earth.
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Affiliation(s)
- Olen R Brown
- Emeritus of Biomedical Sciences, at the University of Missouri, Columbia, MO, USA.
| | - David A Hullender
- Mechanical and Aerospace Engineering at the University of Texas at Arlington, USA
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30
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Herrgårdh T, Simonsson C, Ekstedt M, Lundberg P, Stenkula KG, Nyman E, Gennemark P, Cedersund G. A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects. Diabetol Metab Syndr 2023; 15:250. [PMID: 38044443 PMCID: PMC10694923 DOI: 10.1186/s13098-023-01223-6] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/15/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans. METHODS The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables. CONCLUSIONS The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance.
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Affiliation(s)
- Tilda Herrgårdh
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Christian Simonsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Radiation Physics, Linköping University, Linköping, Sweden
| | - Karin G Stenkula
- Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Elin Nyman
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Peter Gennemark
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), AstraZeneca, BioPharmaceuticals R&D, Gothenburg, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
- Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
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31
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Batagov A, Dalan R, Wu A, Lai W, Tan CS, Eisenhaber F. Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients. Health Inf Sci Syst 2023; 11:18. [PMID: 37008895 PMCID: PMC10060506 DOI: 10.1007/s13755-023-00218-x] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 02/19/2023] [Indexed: 03/31/2023] Open
Abstract
Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice. Supplementary Information The online version contains supplementary material available at 10.1007/s13755-023-00218-x.
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Affiliation(s)
- Arsen Batagov
- Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore
| | - Rinkoo Dalan
- Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Andrew Wu
- Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore
| | - Wenbin Lai
- Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore
| | - Colin S. Tan
- Fundus Image Reading Center, National Healthcare Group Eye Institute, Singapore, Singapore
- Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Frank Eisenhaber
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- School of Biological Science (SBS), Nanyang Technological University, Singapore, Singapore
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Shaikh TA, Rasool T, Verma P. Machine intelligence and medical cyber-physical system architectures for smart healthcare: Taxonomy, challenges, opportunities, and possible solutions. Artif Intell Med 2023; 146:102692. [PMID: 38042609 DOI: 10.1016/j.artmed.2023.102692] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 10/21/2023] [Accepted: 10/22/2023] [Indexed: 12/04/2023]
Abstract
Hospitals use medical cyber-physical systems (MCPS) more often to give patients quality continuous care. MCPS isa life-critical, context-aware, networked system of medical equipment. It has been challenging to achieve high assurance in system software, interoperability, context-aware intelligence, autonomy, security and privacy, and device certifiability due to the necessity to create complicated MCPS that are safe and efficient. The MCPS system is shown in the paper as a newly developed application case study of artificial intelligence in healthcare. Applications for various CPS-based healthcare systems are discussed, such as telehealthcare systems for managing chronic diseases (cardiovascular diseases, epilepsy, hearing loss, and respiratory diseases), supporting medication intake management, and tele-homecare systems. The goal of this study is to provide a thorough overview of the essential components of the MCPS from several angles, including design, methodology, and important enabling technologies, including sensor networks, the Internet of Things (IoT), cloud computing, and multi-agent systems. Additionally, some significant applications are investigated, such as smart cities, which are regarded as one of the key applications that will offer new services for industrial systems, transportation networks, energy distribution, monitoring of environmental changes, business and commerce applications, emergency response, and other social and recreational activities.The four levels of an MCPS's general architecture-data collecting, data aggregation, cloud processing, and action-are shown in this study. Different encryption techniques must be employed to ensure data privacy inside each layer due to the variations in hardware and communication capabilities of each layer. We compare established and new encryption techniques based on how well they support safe data exchange, secure computing, and secure storage. Our thorough experimental study of each method reveals that, although enabling innovative new features like secure sharing and safe computing, developing encryption approaches significantly increases computational and storage overhead. To increase the usability of newly developed encryption schemes in an MCPS and to provide a comprehensive list of tools and databases to assist other researchers, we provide a list of opportunities and challenges for incorporating machine intelligence-based MCPS in healthcare applications in our paper's conclusion.
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Affiliation(s)
- Tawseef Ayoub Shaikh
- Department of Computer Science & Engineering, National Institute of Technology (NIT), Srinagar 190006, Jammu & Kashmir, India.
| | - Tabasum Rasool
- NPDF Fellow, Interdisciplinary Centre for Water Research (ICWaR), Indian Institute of Science, Bengaluru, India.
| | - Prabal Verma
- Department of Information Technology, National Institute of Technology (NIT), Srinagar 190006, Jammu & Kashmir, India.
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33
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Demir O, Uslan I, Buyuk M, Salamci MU. Development and validation of a digital twin of the human lower jaw under impact loading by using non-linear finite element analyses. J Mech Behav Biomed Mater 2023; 148:106207. [PMID: 37922761 DOI: 10.1016/j.jmbbm.2023.106207] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 10/18/2023] [Accepted: 10/20/2023] [Indexed: 11/07/2023]
Abstract
Mandibular fractures are one of the most frequently observed injuries within craniofacial region mostly due to tumor-related problems and traumatic events, often related to non-linear effects like impact loading. Therefore, a validated digital twin of the mandible is required to develop the best possible patient-specific treatment. However, there is a need to obtain a fully compatible numerical model that can reflect the patients' characteristics, be available and accessible quickly, require an acceptable level of modeling efforts and knowledge to provide accurate, robust and fast results at the same time under highly non-linear effects. In this study, a validated simulation methodology is suggested to develop a digital twin of mandible, capable of predicting the non-linear response of the biomechanical system under impact loading, which then can be utilized to design treatment strategies even for multiple fractures of the mandibular system. Using Computed Tomography data containing cranial (skull) images of a patient, a 3-dimensional mandibular model, which consists cortical and cancellous bones, disks and fossa is obtained with high accuracy that is compatible with anatomical boundaries. A Finite Element Model (FEM) of the biomechanical system is then developed for a three-level validation procedure including (A) modal analysis, (B) dynamic loading and (C) impact loading. For the modal analysis stage: Free-free vibration modes and frequencies of the system are validated against cadaver test results. For the dynamic loading stage: Two different regions of the mandible are loaded, and maximum stress levels of the system are validated against finite element analyses (FEA) results, where the first loading condition (i) transfers a 2000 N force acting on the symphysis region and, the second loading condition (ii) transfers a 2000 N force acting on the left body region. In both cases, equivalent muscle forces dependent on time are applied. For the impact loading stage: Thirteen different human mandibular models with various tooth deficiencies are used under the effects of traumatic impact forces that are generated by using an impact hammer with different initial velocities to transfer the impulse and momentum, where contact forces and fracture patterns are validated against cadaver tests. Five different anatomical regions are selected as the impact site. The results of the analyzes (modal, dynamic and impact) performed to validate the digital twin model are compared with the similar FEA and cadaver test results published in the literature and the results are found to be compatible. It has been evaluated that the digital twin model and numerical models are quite realistic and perform well in terms of predicting the biomechanical behavior of the mandible. The three-level validation methodology that is suggested in this research by utilizing non-linear FEA has provided a reliable road map to develop a digital twin of a biomechanical system with enough confidence that it can be utilized for similar structures to offer patient-specific treatments and can help develop custom or tailor-made implants or prosthesis for best compliance with the patient even considering the most catastrophic effects of impact related trauma.
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Affiliation(s)
- Osman Demir
- Gulhane Medical Design and Manufacturing Application and Research Center-SBU-METUM, University of Health Sciences, 06010, Ankara, Turkey; Department of Mechanical Engineering, Gazi University, 06570, Ankara, Turkey.
| | | | - Murat Buyuk
- Department of Engineering Sciences, Middle East Technical University, 06800, Ankara, Turkey.
| | - Metin Uymaz Salamci
- Department of Mechanical Engineering, Gazi University, 06570, Ankara, Turkey; Additive Manufacturing Technologies Research and Application Center-EKTAM, Gazi University, 06980, Ankara, Turkey.
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Reimann AM, Schalk E, Jost F, Mougiakakos D, Weber D, Döhner H, Récher C, Dumas PY, Ditzhaus M, Fischer T, Sager S. AML consolidation therapy: timing matters. J Cancer Res Clin Oncol 2023; 149:13811-13821. [PMID: 37535164 PMCID: PMC10590325 DOI: 10.1007/s00432-023-05115-0] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/04/2023] [Indexed: 08/04/2023]
Abstract
PURPOSE Infections due to severe neutropenia are the most common therapy-associated causes of mortality in patients with acute myeloid leukemia (AML). New strategies to lessen the severity and duration of neutropenia are needed. METHODS Cytarabine is commonly used for AML consolidation therapy; we compared high- and intermediate-dose cytarabine administration on days 1, 2, and 3 (AC-123) versus days 1, 3, and 5 (AC-135) in consolidation therapy of AML. Recently, clinical trials demonstrated that high-dose AC-123 resulted in a shortened white blood cell (WBC) recovery time compared with high-dose AC-135. Our main hypothesis is that this is also the case for different cytarabine dosage, granulocyte colony-stimulating factor (G-CSF) administration, and cycle lengths. We analyzed 334 treatment schedules on virtual cohorts of digital twins. RESULTS Comparison of 32,565 simulated consolidation cycles resulted in a reduction in the WBC recovery time for AC-123 in 99.6% of the considered cycles (median reduction 3.5 days) without an increase in the number of leukemic blasts (lower value in 94.2% of all cycles), compared to AC-135. CONCLUSION Our numerical study supports the use of AC-123 plus G-CSF as standard conventional AML consolidation therapy to reduce the risk for life-threatening infectious complications.
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Affiliation(s)
| | - Enrico Schalk
- Clinics of Hematology and Oncology, Otto von Guericke University (OVGU), Magdeburg, Germany
| | - Felix Jost
- Department of Mathematics, Otto von Guericke University (OVGU), Magdeburg, Germany
- R&D, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
| | - Dimitrios Mougiakakos
- Clinics of Hematology and Oncology, Otto von Guericke University (OVGU), Magdeburg, Germany
| | - Daniela Weber
- Department of Internal Medicine III, University Hospital, Ulm, Germany
| | - Hartmut Döhner
- Department of Internal Medicine III, University Hospital, Ulm, Germany
| | - Christian Récher
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse Oncopole, Centre Hospitalier Universitaire, Toulouse, France
| | - Pierre-Yves Dumas
- Service d'Hématologie Clinique et de Thérapie Cellulaire, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Marc Ditzhaus
- Department of Mathematics, Otto von Guericke University (OVGU), Magdeburg, Germany
| | - Thomas Fischer
- Clinics of Hematology and Oncology, Otto von Guericke University (OVGU), Magdeburg, Germany
| | - Sebastian Sager
- Department of Mathematics, Otto von Guericke University (OVGU), Magdeburg, Germany.
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Wikström H, Martin de Juan L, Remmelgas J, Meier R, Altmeyer A, Emanuele D, Jormanainen M, Juppo A, Tajarobi P. Drying capacity of a continuous vibrated fluid bed dryer - Statistical and mechanistic model development. Int J Pharm 2023; 645:123368. [PMID: 37669728 DOI: 10.1016/j.ijpharm.2023.123368] [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] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/07/2023]
Abstract
The drying capacity of a continuous vibrated fluid bed dryer was studied using a DoE by varying microcrystalline cellulose content in the formulation, water amount in the twin-screw granulation, inlet air temperature, air flow rate and the acceleration of the horizontal fluid-bed. Temperature and humidity profiles were measured along the dryer using wireless sensors. For the parameter space explored in this study, acceleration was the most influential process parameter of the dryer regarding the resulting granule moisture content. An empirical model was developed that allowed for fast and accurate moisture content prediction that could be incorporated into an enhanced control strategy. In addition, a mechanistic model was formulated that allow for prediction of temperature and moisture profiles, and most importantly the moisture content of the granules inside the dryer. The mechanistic model can be integrated to other unit operation models to provide overall understanding of an integrated continuous process line. The mechanistic model also makes it possible to define the equipment design requirements (e.g., length of the dryer) to meet the specific needs in terms of drying capacity, temperature and moisture profile.
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Affiliation(s)
- Håkan Wikström
- Early Product Development and Manufacturing, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Luis Martin de Juan
- Oral Product Development, Pharmaceutical Technology & Development, AstraZeneca, Gothenburg, Sweden
| | | | - Robin Meier
- L.B. Bohle Maschinen und Verfahren GmbH, Ennigerloh, Germany
| | | | - Daniel Emanuele
- L.B. Bohle Maschinen und Verfahren GmbH, Ennigerloh, Germany
| | - Miika Jormanainen
- Division of Pharmaceutical Chemistry and Technology, University of Helsinki, Finland
| | - Anne Juppo
- Division of Pharmaceutical Chemistry and Technology, University of Helsinki, Finland
| | - Pirjo Tajarobi
- Early Product Development and Manufacturing, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
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Mohammed MA, Lakhan A, Abdulkareem KH, Abd Ghani MK, Marhoon HA, Kadry S, Nedoma J, Martinek R, Zapirain BG. Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆. J Adv Res 2023:S2090-1232(23)00297-7. [PMID: 37839503 DOI: 10.1016/j.jare.2023.10.005] [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] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 09/13/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023] Open
Abstract
INTRODUCTION The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity. OBJECTIVE This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met. METHODS We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes. RESULTS Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system. CONCLUSION Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture.
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Affiliation(s)
- Mazin Abed Mohammed
- Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq; Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic.
| | - Abdullah Lakhan
- Department of Cybersecurity and Computer Science, Dawood University of Engineering and Technology, Karachi City 74800, Sindh, Pakistan; Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
| | - Mohd Khanapi Abd Ghani
- Department of Software Engineering, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
| | - Haydar Abdulameer Marhoon
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq; College of Computer Sciences and Information Technology, University of Kerbala, Karbala, Iraq
| | - Seifedine Kadry
- Seifedine is with the Department of Applied Data Science, Noroff University College, Kristiansand, Norway
| | - Jan Nedoma
- Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
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Tang SY, Yuan YH, Chen YC, Yao SJ, Wang Y, Lin DQ. Physics-informed neural networks to solve lumped kinetic model for chromatography process. J Chromatogr A 2023; 1708:464346. [PMID: 37716084 DOI: 10.1016/j.chroma.2023.464346] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [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: 05/15/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/18/2023]
Abstract
Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neural network with physics laws, and is getting noticed for solving physics problems with a balanced accuracy and calculation speed. In this research, a proof-of-concept study was carried out to apply PINN to chromatography process simulation. The PINN model structure was designed for the lumped kinetic model (LKM) with all LKM parameters. The PINN structure, training data and model complexity were optimized, and an optimal mode was obtained by adopting an in-series structure with a nonuniform training data set focusing on the breakthrough transition region. A PINN for LKM (LKM-PINN) consisting of four neural networks, 12 layers and 606 neurons was then used for the simulation of breakthrough curves of chromatography processes. The LKM parameters were estimated with two breakthrough curves and used to infer the breakthrough curves at different residence times, loading concentrations and column sizes. The results were comparable to that obtained with numerical methods. With the same raw data and constraints, the average fitting error for LKM-PINN model was 0.075, which was 0.081 for numerical method. With the same initial guess, the LKM-PINN model took 160 s to complete the fitting, while the numerical method took 7 to 72 min, depending on the fitting settings. The fitting speed of LKM-PINN model was further improved to 30 s with random initial guess. Thus, the LKM-PINN model developed in this study is capable to be applied to real-time simulation for digital twin.
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Affiliation(s)
- Si-Yuan Tang
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China; Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China
| | - Yun-Hao Yuan
- Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China
| | - Yu-Cheng Chen
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shan-Jing Yao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Ying Wang
- Manufacturing Science and Technology, Global Manufacturing, WuXi Biologics, Wuxi 214000, China
| | - Dong-Qiang Lin
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.
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Straughan R, Kadry K, Parikh SA, Edelman ER, Nezami FR. Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography. Comput Biol Med 2023; 165:107341. [PMID: 37611423 PMCID: PMC10528179 DOI: 10.1016/j.compbiomed.2023.107341] [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: 05/01/2023] [Revised: 07/18/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.
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Affiliation(s)
- Ross Straughan
- Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA; Department of Mechanical and Process Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland.
| | - Karim Kadry
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA.
| | - Sahil A Parikh
- Division of Cardiology, Columbia University Irving Medical Center, New York, 10032, NY, USA.
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
| | - Farhad R Nezami
- Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
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Rouhollahi A, Willi JN, Haltmeier S, Mehrtash A, Straughan R, Javadikasgari H, Brown J, Itoh A, de la Cruz KI, Aikawa E, Edelman ER, Nezami FR. CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis. Comput Med Imaging Graph 2023; 109:102289. [PMID: 37633032 PMCID: PMC10599298 DOI: 10.1016/j.compmedimag.2023.102289] [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: 05/02/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/28/2023]
Abstract
Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the prevalence of AS is projected to rise, resulting in a progressively significant healthcare and economic burden. While surgical aortic valve replacement (SAVR) has been the gold standard approach, the less invasive transcatheter aortic valve replacement (TAVR) is poised to become the dominant method for high- and medium-risk interventions. Computational simulations using patient-specific models, have opened new research avenues for optimizing emerging devices and predicting clinical outcomes. The traditional techniques of generating digital replicas of patients' aortic root, native valve, and calcification are time-consuming and labor-intensive processes requiring specialized tools and expertise in anatomy. Alternatively, deep learning models, such as the U-Net architecture, have emerged as reliable and fully automated methods for medical image segmentation. Two-dimensional U-Nets have been shown to produce comparable or more accurate results than trained clinicians' manual segmentation while significantly reducing computational costs. In this study, we have developed a fully automatic AI tool capable of reconstructing the digital twin geometry and analyzing the calcification distribution on the aortic valve. The developed automatic segmentation package enables the modeling of patient-specific anatomies, which can then be used to simulate virtual interventional procedures, optimize emerging prosthetic devices, and predict clinical outcomes.
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Affiliation(s)
- Amir Rouhollahi
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - James Noel Willi
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sandra Haltmeier
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alireza Mehrtash
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ross Straughan
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Hoda Javadikasgari
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan Brown
- Clinical and Translation Science Institute, Tufts University, Boston, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Akinobu Itoh
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kim I de la Cruz
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Center for Excellence in Vascular Biology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Farhad R Nezami
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Li J, Mohamad NNN, Sharma K, Yuan Z. Establishing boundary conditions in sewer pipe/soil heat transfer modelling using physics-informed learning. Water Res 2023; 244:120441. [PMID: 37562102 DOI: 10.1016/j.watres.2023.120441] [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] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
Modelling heat transfer in sewers and the surrounding soil is important for effective sewer maintenance, and for heat recovery from wastewater. The boundary conditions, including both the thickness of the soil layer to be modelled and the temperature distribution around the boundary of the soil layer, directly determine both the efficiency and accuracy of the models. Yet there is no systematic method to establish these conditions. This study presents a novel and generic approach to establishing efficient boundary conditions for sewer heat transfer modelling. Fourier transform is applied to identify the dominant frequencies of the temperatures of the heat sources/sinks, namely the atmosphere, sewer air and wastewater. A simple data-driven model for determining the thickness of the soil-layer to be included, and three physics-informed models for predicting the temperatures at the soil-layer boundary are then learnt from mechanistic models for sewer heat transfer, taking into consideration the frequency spectra. The methodology achieved high fidelity to the mechanistic models in predicting the soil-layer boundary temperatures and sewer wall temperatures for real-life sewers. This approach offers an easy yet reliable way to obtain efficient boundary conditions that significantly improve both the accuracy and speed of sewer heat transfer modelling.
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Affiliation(s)
- Jiuling Li
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia
| | - Nur Nabilah Naina Mohamad
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia
| | - Keshab Sharma
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia
| | - Zhiguo Yuan
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, St. Lucia, Brisbane QLD 4072, Australia; School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China.
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Sørensen HM, Cunningham D, Balakrishnan R, Maye S, MacLeod G, Brabazon D, Loscher C, Freeland B. Steps toward a digital twin for functional food production with increased health benefits. Curr Res Food Sci 2023; 7:100593. [PMID: 37790857 PMCID: PMC10543970 DOI: 10.1016/j.crfs.2023.100593] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin. Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses. The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-α secretion. Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.
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Affiliation(s)
- Helena Mylise Sørensen
- School of Biotechnology, Dublin City University, D9 Dublin, Ireland
- I-Form, Advanced Manufacturing Research Centre, Dublin City University, D9 Dublin, Ireland
| | - David Cunningham
- School of Biotechnology, Dublin City University, D9 Dublin, Ireland
| | | | - Susan Maye
- Dairygold Co-Operative Society Limited, Clonmel Road, Co. Cork, P67 DD36, Mitchelstown, Ireland
| | - George MacLeod
- Dairygold Co-Operative Society Limited, Clonmel Road, Co. Cork, P67 DD36, Mitchelstown, Ireland
| | - Dermot Brabazon
- I-Form, Advanced Manufacturing Research Centre, Dublin City University, D9 Dublin, Ireland
| | | | - Brian Freeland
- School of Biotechnology, Dublin City University, D9 Dublin, Ireland
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Guo Y, Liu Y, Sun W, Yu S, Han XJ, Qu XH, Wang G. Digital twin-driven dynamic monitoring system of the upper limb force. Comput Methods Biomech Biomed Engin 2023:1-13. [PMID: 37713212 DOI: 10.1080/10255842.2023.2254881] [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] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/01/2023] [Accepted: 08/26/2023] [Indexed: 09/16/2023]
Abstract
Digital twin represents the core technology to realize the dynamic monitoring of complex industrial systems. However, the human body, as the most complex system in the physical world, digital twin is rarely applied in it. In this study, we successfully demonstrated a digital twin in the human biomedical application by proposing a dynamic monitoring system of the upper limb force. In this system, the real upper limb drives the motion of the virtual one in real-time and dynamically updates the force. Meanwhile, the virtual upper limb feeds back the monitoring-results of the force to the controller of the real upper limb via immersive virtual reality interaction. Experimental results of the typical motions of the upper limb revealed that the proposed system functioned interactively in real-time in a non-invasive manner, while ensuring the accurate solving of the muscle force. In conclusion, our digital twin-driven system is of great importance for rehabilitation medicine, biomechanical scientific research and physical training, promoting the application of the digital twin in the human biomedical field.
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Affiliation(s)
- Yanbin Guo
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Yingbin Liu
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Wenxuan Sun
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Shuai Yu
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Xiao-Jian Han
- Institute of Geriatrics, Jiangxi Provincial People's Hospital & The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, P.R. China
- Department of Neurology, Jiangxi Provincial People's Hospital & the First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, P.R. China
| | - Xin-Hui Qu
- Department of Neurology, Jiangxi Provincial People's Hospital & the First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, P.R. China
| | - Guoping Wang
- Hubei Bioinformatics & Molecular Imaging Key Laboratory, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
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Azzolin L, Eichenlaub M, Nagel C, Nairn D, Sánchez J, Unger L, Arentz T, Westermann D, Dössel O, Jadidi A, Loewe A. AugmentA: Patient-specific augmented atrial model generation tool. Comput Med Imaging Graph 2023; 108:102265. [PMID: 37392493 DOI: 10.1016/j.compmedimag.2023.102265] [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] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/07/2023] [Accepted: 06/03/2023] [Indexed: 07/03/2023]
Abstract
Digital twins of patients' hearts are a promising tool to assess arrhythmia vulnerability and to personalize therapy. However, the process of building personalized computational models can be challenging and requires a high level of human interaction. We propose a patient-specific Augmented Atria generation pipeline (AugmentA) as a highly automated framework which, starting from clinical geometrical data, provides ready-to-use atrial personalized computational models. AugmentA identifies and labels atrial orifices using only one reference point per atrium. If the user chooses to fit a statistical shape model to the input geometry, it is first rigidly aligned with the given mean shape before a non-rigid fitting procedure is applied. AugmentA automatically generates the fiber orientation and finds local conduction velocities by minimizing the error between the simulated and clinical local activation time (LAT) map. The pipeline was tested on a cohort of 29 patients on both segmented magnetic resonance images (MRI) and electroanatomical maps of the left atrium. Moreover, the pipeline was applied to a bi-atrial volumetric mesh derived from MRI. The pipeline robustly integrated fiber orientation and anatomical region annotations in 38.4 ± 5.7 s. In conclusion, AugmentA offers an automated and comprehensive pipeline delivering atrial digital twins from clinical data in procedural time.
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Affiliation(s)
- Luca Azzolin
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany.
| | - Martin Eichenlaub
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Claudia Nagel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Deborah Nairn
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Jorge Sánchez
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Laura Unger
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Thomas Arentz
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Dirk Westermann
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Amir Jadidi
- University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering at Karlsruhe Institute of Technology, Karlsruhe, Germany
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McMahon‑Cole H, Johnson A, Sadat Aghamiri S, Helikar T, Crawford LB. Modeling and Remodeling the Cell: How Digital Twins and HCMV Can Elucidate the Complex Interactions of Viral Latency, Epigenetic Regulation, and Immune Responses. Curr Clin Microbiol Rep 2023; 10:141-151. [PMID: 37901689 PMCID: PMC10601359 DOI: 10.1007/s40588-023-00201-w] [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] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/12/2023] [Indexed: 10/31/2023]
Abstract
Purpose of Review Human cytomegalovirus (HCMV), while asymptomatic in most, causes significant complications during fetal development, following transplant or in immunosuppressed individuals. The host-virus interactions regulating viral latency and reactivation and viral control of the cellular environment (immune regulation, differentiation, epigenetics) are highly complex. Understanding these processes is essential to controlling infection and can be leveraged as a novel approach for understanding basic cell biology. Recent Findings Immune digital twins (IDTs) are digital simulations integrating knowledge of human immunology, physiology, and patient-specific clinical data to predict individualized immune responses and targeted treatments. Recent studies used IDTs to elucidate mechanisms of T cells, dendritic cells, and epigenetic control-all key to HCMV biology. Summary Here, we discuss how leveraging the unique biology of HCMV and IDTs will clarify immune response dynamics, host-virus interactions, and viral latency and reactivation and serve as a powerful IDT-validation platform for individualized and holistic health management.
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Affiliation(s)
- Hana McMahon‑Cole
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Alicia Johnson
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Lindsey B. Crawford
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
- Nebraska Center for Virology, Lincoln, NE, USA
- Nebraska Center for Integrated Biomolecular Communication, Lincoln, NE, USA
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Machado TM, Berssaneti FT. Literature review of digital twin in healthcare. Heliyon 2023; 9:e19390. [PMID: 37809792 PMCID: PMC10558347 DOI: 10.1016/j.heliyon.2023.e19390] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 05/26/2023] [Accepted: 08/21/2023] [Indexed: 10/10/2023] Open
Abstract
This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified. The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.
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Affiliation(s)
- Tatiana Mallet Machado
- Production Engineering Department, Polytechnic School University of São Paulo, Av. Prof. Almeida Prado, Brazil
| | - Fernando Tobal Berssaneti
- Production Engineering Department, Polytechnic School University of São Paulo, Av. Prof. Almeida Prado, Brazil
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Monteiro M, Fadda S, Kontoravdi C. Towards advanced bioprocess optimization: A multiscale modelling approach. Comput Struct Biotechnol J 2023; 21:3639-3655. [PMID: 37520284 PMCID: PMC10371800 DOI: 10.1016/j.csbj.2023.07.003] [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] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 08/01/2023] Open
Abstract
Mammalian cells produce up to 80 % of the commercially available therapeutic proteins, with Chinese Hamster Ovary (CHO) cells being the primary production host. Manufacturing involves a train of reactors, the last of which is typically run in fed-batch mode, where cells grow and produce the required protein. The feeding strategy is decided a priori, from either past operations or the design of experiments and rarely considers the current state of the process. This work proposes a Model Predictive Control (MPC) formulation based on a hybrid kinetic-stoichiometric reactor model to provide optimal feeding policies in real-time, which is agnostic to the culture, hence transferable across CHO cell culture systems. The benefits of the proposed controller formulation are demonstrated through a comparison between an open-loop simulation and closed-loop optimization, using a digital twin as an emulator of the process.
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O’Hara RP, Meijborg VM, Jelvehgaran P, van der Waal J, Boink GJ, Trayanova NA, Coronel R, Boukens BJ. Site-specific prolongation of repolarization prevents postmyocardial infarction tachycardia. Heart Rhythm O2 2023; 4:466-468. [PMID: 37520014 PMCID: PMC10373146 DOI: 10.1016/j.hroo.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/01/2023] Open
Affiliation(s)
- Ryan P. O’Hara
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Veronique M.F. Meijborg
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Pouya Jelvehgaran
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jeanne van der Waal
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Gerard J.J. Boink
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Medical Biology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Natalia A. Trayanova
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Ruben Coronel
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Fondation Bordeaux Université, Inserm, U1045 and Université de Bordeaux, Bordeaux, France
| | - Bastiaan J. Boukens
- Department of Medical Biology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
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Attaran M, Attaran S, Celik BG. The impact of digital twins on the evolution of intelligent manufacturing and Industry 4.0. Adv Comput Intell 2023; 3:11. [PMID: 37305021 PMCID: PMC10246533 DOI: 10.1007/s43674-023-00058-y] [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] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 05/03/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023]
Abstract
As the adoption of Industry 4.0 advances and the manufacturing process becomes increasingly digital, the Digital Twin (DT) will prove invaluable for testing and simulating new parameters and design variants. DT solutions build a 3D digital replica of the physical object allowing the managers to develop better products, detect physical issues sooner, and predict outcomes more accurately. In the past few years, Digital Twins (DTs) dramatically reduced the cost of developing new manufacturing approaches, improved efficiency, reduced waste, and minimized batch-to-batch variability. This paper aims to highlight the evolution of DTs, review its enabling technologies, identify challenges and opportunities for implementing DT in Industry 4.0, and examine its range of applications in manufacturing, including smart logistics and supply chain management. The paper also highlights some real examples of the application of DT in manufacturing.
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Affiliation(s)
- Mohsen Attaran
- School of Business and Public Administration, California State University, Bakersfield, 9001 Stockdale Highway, Bakersfield, CA 93311-1099 USA
| | - Sharmin Attaran
- Bryant University, 1150 Douglas Pike, Smithfield, RI 02917 USA
| | - Bilge Gokhan Celik
- School of Engineering Computing and Construction Management, Roger Williams University, One Old Ferry Road, Bristol, RI 02809 USA
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Xie W, Li J, Shi J, Zhang X, Usmani AS, Chen G. Probabilistic real-time natural gas jet fire consequence modeling of offshore platforms by hybrid deep learning approach. Mar Pollut Bull 2023; 192:115098. [PMID: 37295257 DOI: 10.1016/j.marpolbul.2023.115098] [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] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 05/01/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Natural gas jet fire induced by igniting blowouts has the potential to cause critical structure damage and great casualties of offshore platforms. Real-time natural gas jet fire plume prediction is essential to support the emergency planning to mitigate subsequent damage consequence and ocean pollution. Deep learning based on a large amount of Computational fluid dynamics (CFD) simulations has recently been applied to real-time fire modeling. However, existing approaches based on point-estimation theory are 'over-confident' when prediction deficiency exists, which reduce robustness and accuracy for emergency planning support. This study proposes probabilistic deep learning approach for real-time natural gas jet fire consequence modeling by integrating variational Bayesian inference with deep learning. Numerical model of natural gas jet fire from offshore platform is built and the natural gas jet fire scenarios are simulated to construct the benchmark dataset. Sensitivity analysis of pre-defined parameters such as MC (Monte Carlo) sampling number m and dropout probability p is conducted to determine the trade-off between model's accuracy and efficiency. The results demonstrated our model exhibits competitive accuracy with R2 = 0.965 and real-time capacity with an inference time of 12 ms. In addition, the predicted spatial uncertainty corresponding to spatial jet fire flame plume provides more comprehensive and reliable support for the following mitigation decision-makings compared to the state-of-the-art point-estimation based deep learning model. This study provides a robust alternative for constructing a digital twin of fire and explosion associated emergency management on offshore platforms.
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Affiliation(s)
- Weikang Xie
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao, China
| | - Junjie Li
- Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao, China
| | - Jihao Shi
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong; Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao, China.
| | - Xinqi Zhang
- Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao, China
| | - Asif Sohail Usmani
- Department of Building Environment and Energy Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Guoming Chen
- Centre for Offshore Engineering and Safety Technology, China University of Petroleum, Qingdao, China
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50
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Wu W, Shen L, Zhao Z, Harish AR, Zhong RY, Huang GQ. Internet of Everything and Digital Twin enabled Service Platform for Cold Chain Logistics. J Ind Inf Integr 2023; 33:100443. [PMID: 36820130 PMCID: PMC9932793 DOI: 10.1016/j.jii.2023.100443] [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] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
The proliferation of the e-commerce market has posed challenges to staff safety, product quality, and operational efficiency, especially for cold chain logistics (CCL). Recently, the logistics of vaccine supply under the worldwide COVID-19 pandemic rearouses public attention and calls for innovative solutions to tackle the challenges remaining in CCL. Accordingly, this study proposes a cyber-physical platform framework applying the Internet of Everything (IoE) and Digital Twin (DT) technologies to promote information integration and provide smart services for different stakeholders in the CCL. In the platform, reams of data are generated, gathered, and leveraged to interconnect and digitalize physical things, people, and processes in cyberspace, paving the way for digital servitization. Deep learning techniques are used for accident identification and indoor localization based on Bluetooth Low Energy (BLE) to actualize real-time staff safety supervision in the cold warehouse. Both algorithms are designed to take advantage of the IoE infrastructure to achieve online self-adapting in response to surrounding evolutions. Besides, with the help of mobile and desktop applications, paperless operation for shipment, remote temperature and humidity (T&H) monitoring, anomaly detection and warning, and customer interaction are enabled. Thus, information traceability and visibility are highly fortified in this way. Finally, a real-life case study is conducted in a pharmaceutical distribution center to demonstrate the feasibility and practicality of the proposed platform and methods. The dedicated hardware and software are developed and deployed on site. As a result, the effectiveness of staff safety management, operational informatization, product quality assurance, and stakeholder loyalty maintenance shows a noticeable improvement. The insights and lessons harvested in this study may spark new ideas for researchers and inspire practitioners to meet similar needs in the industry.
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Affiliation(s)
- Wei Wu
- College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, P.R. China
| | - Leidi Shen
- Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, P.R. China
| | - Zhiheng Zhao
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, P.R. China
| | - Arjun Rachana Harish
- Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, P.R. China
| | - Ray Y Zhong
- Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong, P.R. China
| | - George Q Huang
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, P.R. China
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