We have developed a bioinstrumentation course that emphasizes practical application of engineering and biological concepts by having students focus on the development of a single biomedical device: a cardiac pacemaker. In creating their benchtop pacemaker, students learn about and design sensing circuitry, data acquisition and processing code, control system algorithms, and stimulation electronics. They also gain an understanding of cardiac anatomy and electrophysiology. The separate elements of the pacemaker created throughout the semester will be repeatedly tested, re-designed, and integrated with one another, culminating in an emulated pacemaker whose efficacy will be tested on North American bullfrogs. It is hypothesized that the hands-on learning in this course, coupled with the practical application of concepts in the context of a single biomedical device, will enhance students' skills in bioinstrumentation design.
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http://dx.doi.org/10.1109/EMBC.2013.6610209 | DOI Listing |
Dermatol Ther (Heidelb)
December 2024
Department of Cell Biology and Physiology, The Neuroscience Center, College of Life Sciences, Brigham Young University, Provo, UT, 84602, USA.
Introduction: Retinol has a long history of treating skin conditions, including photoaging. However, skin irritation with repeated use of retinol is well documented. The present study assessed the effectiveness of a novel topical formulation, referred to as retinol topical formulation (RTF), to improve the quality of skin health.
View Article and Find Full Text PDFBMC Med Educ
May 2024
Aston Business School, Aston University, Birmingham, UK.
Background: Bioinstrumentation is essential to biomedical engineering (BME) undergraduate education and professional practice. Several strategies have been suggested to provide BME students with hands-on experiences throughout the curriculum, promoting their preparedness to pursue careers in industry and academia while increasing their learning and engagement. This paper describes the implementation of challenge-based learning (CBL) in an undergraduate bioinstrumentation blended course over the COVID-19 pandemic.
View Article and Find Full Text PDFGait Posture
May 2023
Grupo Rehabilitación en Salud, Sede de Investigación Universitaria, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia; Mahavir Kmina Artificial Limb Center, Carrera 54 No. 79 AA Sur 40, Bodegas La Troja, Local 116, La Estrella, Colombia.
Background: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal alignment.
View Article and Find Full Text PDFPlants (Basel)
January 2023
Department of Genomic and Applied Microbiology and Göttingen Genomics Laboratory, Georg-August University of Göttingen, 37077 Göttingen, Germany.
The most common approaches for the in-situ bioremediation of contaminated sites worldwide are bioaugmentation and biostimulation. Biostimulation has often proved more effective for chronically contaminated sites. This study examined the effectiveness of optimized water hyacinth compost in comparison with other organic and inorganic amendments for the remediation of crude oil-polluted soils.
View Article and Find Full Text PDFJ Pharmacokinet Pharmacodyn
February 2023
Laboratory of Applied Pharmacokinetics and Bioinformatics, Department of Infectious Diseases, Children's Hospital Los Angeles, Los Angeles, CA, USA.
The building of population pharmacokinetic models can be described as an iterative process in which given a model and a dataset, the pharmacometrician introduces some changes to the model specification, then perform an evaluation and based on the predictions obtained performs further optimization. This process (perform an action, witness a result, optimize your knowledge) is a perfect scenario for the implementation of Reinforcement Learning algorithms. In this paper we present the conceptual background and a implementation of one of those algorithms aiming to show pharmacometricians how to automate (to a certain point) the iterative model building process.
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