2011 panel on developing a biomaterials curriculum.

This article provides the transcript for the Panel on Developing a Biomaterials Curriculum held at the 2011 annual meeting of the Society for Biomaterials in Orlando, FL. The panelists were Thomas R. Harris of Vanderbilt University, Jack Lemons of the University of Alabama, Birmingham, Antonios G. Mikos of Rice University, David A. Puleo on the University of Kentucky, Frederick J. Schoen of Harvard Medical School, and Johnna S. Temenoff of Georgia Tech/Emory. The panelists, each an expert in engineering education and textbook author, presented their perspectives on key issues of developing undergraduate and graduate curricula that contain a biomaterials focus. The presentations were followed by a lively and informative discussion with the audience. A redacted portion of this discussion is included.

Challenge-based instruction in biomedical engineering: a scalable method to increase the efficiency and effectiveness of teaching and learning in biomedical engineering.

Vanderbilt University, Northwestern University, the University of Texas and the Harvard/MIT Health Sciences Technology Program have collaborated since 1999 to develop means to improve bioengineering education. This effort, funded by the National Science Foundation as the VaNTH Engineering Research Center in Bioengineering Educational Technologies, has sought a synthesis of learning science, learning technology, assessment and the domains of bioengineering in order to improve learning by bioengineering students. Research has shown that bioengineering educational materials may be designed to emphasize challenges that engage the student and, when coupled with a learning cycle and appropriate technologies, can lead to improvements in instruction.

Visualization-based analysis for a mixed-inhibition binary PBPK model: determination of inhibition mechanism.

A physiologically based pharmacokinetic (PBPK) model incorporating mixed enzyme inhibition was used to determine the mechanism of metabolic interactions occurring during simultaneous exposures to the organic solvents chloroform and trichloroethylene (TCE). Visualization-based sensitivity and identifiability analyses of the model were performed to determine the conditions under which four inhibitory parameters describing inhibitor binding could be estimated. The sensitivity methods were used to reduce the 4-parameter estimation problem into two distinct 2-parameter problems. The inhibitory parameters were then estimated from multiple closed-chamber gas-uptake experiments using graphical methods. The estimated values of the four inhibitory parameters predicted that chloroform and TCE interact in a competitive manner. Based on the model analysis, we present recommendations for the design of experiments for determination of inhibition mechanism in binary chemical mixtures. We assert that a thorough analysis of the parameter-dependent sensitivity and identifiability characteristics can be used to plan efficient experimental protocols for the quantitative analysis of inhalation pharmacokinetics.

Roles for learning sciences and learning technologies in biomedical engineering education: a review of recent advances.

Education in biomedical engineering offers a number of challenges to all constituents of the educational process-faculty, students, and employers of graduates. Although biomedical engineering educational systems have been under development for 40 years, interest in and the pace of development of these programs has accelerated in recent years. New advances in the learning sciences have provided a framework for the reexamination of instructional paradigms in biomedical engineering. This work shows that learning environments should be learner centered, knowledge centered, assessment centered, and community centered. In addition, learning technologies offer the potential to achieve this environment with efficiency. Biomedical engineering educators are in a position to design and implement new learning systems that can take advantage of advances in learning science, learning technology, and reform in engineering education.

Quantification of lung microvascular injury with ultrasound.

Quantification of water and solute exchange rates across the lung microvascular barrier (LMB) may be an important early-warning indicator of pulmonary microvascular diseases such as acute respiratory distress syndrome. Our objective was to determine the degree to which osmotic water movement across the LMB induced by injection of hypertonic solutions of NaCl and glucose could be detected downstream from the lung with a specialized ultrasonic velocity (USV) transducer manufactured by Transonic Systems. We hypothesized that mathematical modeling of the osmotic transients (OT) would yield estimates of osmotic exchange parameters that were sensitive to microvascular injury. Two groups of six dogs were studied under baseline conditions and after injury with high dose (HD) or low dose (LD) oleic acid. Osmotic conductances (sigmaK(1), sigmaK(2)), and volumes (V(1), V(2)) of two extravascular spaces were estimated by fitting the mathematical model to the OT data. HD results (mean +/- standard error) indicated a significant decrease (by paired t test) in sigmaK(1) from 1.59 +/- 0.09 to 1.04 +/- 0.015 [ml h(-1) (mosm/l)(-1) g(-1) WLW)], an increase in sigmaK(2) from 0.20 +/- 0.08 to 0.32 +/- 0.12 [ml h(-1) (mosm/l)(-1) g(-1)], and a significant increase in V2 from 23.26 +/- 2.51 to 78.0 +/- 15.23 (ml) for NaCl injections. LD V2 estimated from NaCl increased significantly from 21.57 +/- 2.15 to 37.59 +/- 2.36 (ml), sigmaK2 increased from 0.09 +/- 0.03 to 0.17 +/- 0.04 and no significant change in sigmaK(1) was found. Baseline glucose sigmaK(1), sigmaK(2), and V1 in the LD series were 2.08 +/- 0.18, 0.64 +/- 0.19 [ml h(-1) (mosm/l)(-1) g(-1)] and 13.08 +/- 1.89 (ml), respectively, and did not change significantly with injury. We conclude that OT data measured by USV is a sensitive and informative indicator of LMB osmotic properties, and may be useful for quantification of LMB permeability changes due to acute injury. We further conclude that V(1) represents microvascular endothelial volume and V(2) is an estimate of interstitial volume.