Special Issue "Biomedical Systems Control"

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Systems".

Deadline for manuscript submissions: closed (15 July 2016)

Special Issue Editor

Guest Editor
Prof. Dr. B. Wayne Bequette

Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA
Website | E-Mail
Phone: 518-276-6377
Interests: process systems engineering; biomedical systems control; chemical process control; smart grid control

Special Issue Information

Dear Colleagues,

 

This Special Issue seeks papers that deal with modeling, control, and monitoring of biomedical systems. The scope of this Special Issue includes, but is not limited to, modeling of physiological systems, modeling of drug pharmacokinetics and pharmacodynamics, the use of advanced or novel sensors and actuators, control algorithms, and fault detection and isolation. A strong preference will be given to articles that include animal and or human studies and with clinical physicians as co-authors.

Professor B. Wayne Bequette
Guest Editor

Manuscript Submission Information

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

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

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1100 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2019 an APC of 1200 CHF applies. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Biomedical
  • Disease
  • Monitoring
  • Control
  • Clinical
  • Physiological models
  • Devices

Published Papers (5 papers)

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Research

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Open AccessArticle Environmental Control in Flow Bioreactors
Processes 2017, 5(2), 16; https://doi.org/10.3390/pr5020016
Received: 1 March 2017 / Revised: 2 April 2017 / Accepted: 4 April 2017 / Published: 7 April 2017
Cited by 1 | PDF Full-text (3117 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The realization of physiologically-relevant advanced in vitro models is not just related to the reproduction of a three-dimensional multicellular architecture, but also to the maintenance of a cell culture environment in which parameters, such as temperature, pH, and hydrostatic pressure are finely controlled. [...] Read more.
The realization of physiologically-relevant advanced in vitro models is not just related to the reproduction of a three-dimensional multicellular architecture, but also to the maintenance of a cell culture environment in which parameters, such as temperature, pH, and hydrostatic pressure are finely controlled. Tunable and reproducible culture conditions are crucial for the study of environment-sensitive cells, and can also be used for mimicking pathophysiological conditions related with alterations of temperature, pressure and pH. Here, we present the SUITE (Supervising Unit for In Vitro Testing) system, a platform able to monitor and adjust local environmental variables in dynamic cell culture experiments. The physical core of the control system is a mixing chamber, which can be connected to different bioreactors and acts as a media reservoir equipped with a pH meter and pressure sensors. The chamber is heated by external resistive elements and the temperature is controlled using a thermistor. A purpose-built electronic control unit gathers all data from the sensors and controls the pH and hydrostatic pressure by regulating air and CO2 overpressure and flux. The system’s modularity and the possibility of imposing different pressure conditions were used to implement a model of portal hypertension with both endothelial and hepatic cells. The results show that the SUITE platform is able to control and maintain cell culture parameters at fixed values that represent either physiological or pathological conditions. Thus, it represents a fundamental tool for the design of biomimetic in vitro models, with applications in disease modelling or toxicity testing. Full article
(This article belongs to the Special Issue Biomedical Systems Control)
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Open AccessArticle Modeling and Hemofiltration Treatment of Acute Inflammation
Processes 2016, 4(4), 38; https://doi.org/10.3390/pr4040038
Received: 1 August 2016 / Revised: 21 September 2016 / Accepted: 29 September 2016 / Published: 18 October 2016
Cited by 1 | PDF Full-text (609 KB) | HTML Full-text | XML Full-text
Abstract
The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known [...] Read more.
The body responds to endotoxins by triggering the acute inflammatory response system to eliminate the threat posed by gram-negative bacteria (endotoxin) and restore health. However, an uncontrolled inflammatory response can lead to tissue damage, organ failure, and ultimately death; this is clinically known as sepsis. Mathematical models of acute inflammatory disease have the potential to guide treatment decisions in critically ill patients. In this work, an 8-state (8-D) differential equation model of the acute inflammatory response system to endotoxin challenge was developed. Endotoxin challenges at 3 and 12 mg/kg were administered to rats, and dynamic cytokine data for interleukin (IL)-6, tumor necrosis factor (TNF), and IL-10 were obtained and used to calibrate the model. Evaluation of competing model structures was performed by analyzing model predictions at 3, 6, and 12 mg/kg endotoxin challenges with respect to experimental data from rats. Subsequently, a model predictive control (MPC) algorithm was synthesized to control a hemoadsorption (HA) device, a blood purification treatment for acute inflammation. A particle filter (PF) algorithm was implemented to estimate the full state vector of the endotoxemic rat based on time series cytokine measurements. Treatment simulations show that: (i) the apparent primary mechanism of HA efficacy is white blood cell (WBC) capture, with cytokine capture a secondary benefit; and (ii) differential filtering of cytokines and WBC does not provide substantial improvement in treatment outcomes vs. existing HA devices. Full article
(This article belongs to the Special Issue Biomedical Systems Control)
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Open AccessArticle Discrete Blood Glucose Control in Diabetic Göttingen Minipigs
Processes 2016, 4(3), 22; https://doi.org/10.3390/pr4030022
Received: 14 April 2016 / Revised: 5 July 2016 / Accepted: 13 July 2016 / Published: 22 July 2016
Cited by 4 | PDF Full-text (1748 KB) | HTML Full-text | XML Full-text
Abstract
Despite continuous research effort, patients with type 1 diabetes mellitus (T1D) experience difficulties in daily adjustments of their blood glucose concentrations. New technological developments in the form of implanted intravenous infusion pumps and continuous blood glucose sensors might alleviate obstacles for the automatic [...] Read more.
Despite continuous research effort, patients with type 1 diabetes mellitus (T1D) experience difficulties in daily adjustments of their blood glucose concentrations. New technological developments in the form of implanted intravenous infusion pumps and continuous blood glucose sensors might alleviate obstacles for the automatic adjustment of blood glucose concentration. These obstacles consist, for example, of large time-delays and insulin storage effects for the subcutaneous/interstitial route. Towards the goal of an artificial pancreas, we present a novel feedback controller approach that combines classical loop-shaping techniques with gain-scheduling and modern H -robust control approaches. A disturbance rejection design is proposed in discrete frequency domain based on the detailed model of the diabetic Göttingen minipig. The model is trimmed and linearised over a large operating range of blood glucose concentrations and insulin sensitivity values. Controller parameters are determined for each of these operating points. A discrete H loop-shaping compensator is designed to increase robustness of the artificial pancreas against general coprime factor uncertainty. The gain scheduled controller uses subcutaneous insulin injection as a control input and determines the controller input error from intravenous blood glucose concentration measurements, where parameter scheduling is achieved by an estimator of the insulin sensitivity parameter. Thus, only one controller stabilises a family of animal models. The controller is validated in silico with a total number of five Göttingen Minipig models, which were previously obtained by experimental identification procedures. Its performance is compared with an experimentally tested switching PI-controller. Full article
(This article belongs to the Special Issue Biomedical Systems Control)
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Review

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Open AccessFeature PaperReview Algorithms for a Single Hormone Closed-Loop Artificial Pancreas: Challenges Pertinent to Chemical Process Operations and Control
Processes 2016, 4(4), 39; https://doi.org/10.3390/pr4040039
Received: 6 September 2016 / Revised: 30 September 2016 / Accepted: 8 October 2016 / Published: 18 October 2016
PDF Full-text (1701 KB) | HTML Full-text | XML Full-text
Abstract
The development of a closed-loop artificial pancreas to regulate the blood glucose concentration of individuals with type 1 diabetes has been a focused area of research for over 50 years, with rapid progress during the past decade. The daily control challenges faced by [...] Read more.
The development of a closed-loop artificial pancreas to regulate the blood glucose concentration of individuals with type 1 diabetes has been a focused area of research for over 50 years, with rapid progress during the past decade. The daily control challenges faced by someone with type 1 diabetes include asymmetric objectives and risks, and one-sided manipulated input action with frequent relatively fast disturbances. The major automation steps toward a closed-loop artificial pancreas include (i) monitoring and overnight alarms for hypoglycemia (low blood glucose); (ii) overnight low glucose suspend (LGS) systems to prevent hypoglycemia; and (iii) fully closed-loop systems that adjust insulin (and perhaps glucagon) to maintain desired blood glucose levels day and night. We focus on the steps that we used to develop and test a probabilistic, risk-based, model predictive control strategy for a fully closed-loop artificial pancreas. We complete the paper by discussing ramifications of lessons learned for chemical process systems applications. Full article
(This article belongs to the Special Issue Biomedical Systems Control)
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Open AccessFeature PaperReview Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas
Processes 2016, 4(4), 35; https://doi.org/10.3390/pr4040035
Received: 14 July 2016 / Revised: 14 September 2016 / Accepted: 15 September 2016 / Published: 23 September 2016
Cited by 9 | PDF Full-text (2580 KB) | HTML Full-text | XML Full-text
Abstract
Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial [...] Read more.
Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform. Full article
(This article belongs to the Special Issue Biomedical Systems Control)
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