Biomedical subjects

Anthony S Wexler

Publications and source records attributed to Anthony S Wexler.

10 recordsLinked to original sources

PubMed2006

Interaction of epithelium with mesenchyme affects global features of lung architecture: a computer model of development.

Lung airway morphogenesis is simulated in a simplified diffusing environment that simulates the mesenchyme to explore the role of morphogens in airway architecture development. Simple rules govern local branching morphogenesis. Morphogen gradients are modeled by four pairs of sources and their diffusion through the mesenchyme. Sensitivity to lobar architecture and mesenchymal morphogen are explored. Even if the model accurately represents observed patterns of local development, it could not produce realistic global patterns of lung architecture if interaction with its environment was not taken into account, implying that reciprocal interaction between airway growth and morphogens in the mesenchyme plays a critical role in producing realistic global features of lung architecture.

Bronchi↗

PubMed2005

Mathematical model that predicts lower leg motion in response to electrical stimulation.

Electrical stimulation of skeletal muscles of patients with upper motor neuron lesions can be used to restore functional movements such as standing or walking. Mathematical muscle models can assist in designing stimulation patterns that will enable patients to perform particular tasks more efficiently. In this study we extend our previous model to allow us to predict changes in knee joint angle in response to electrical stimulation of the human quadriceps femoris muscle. The model was tested both with and without inertial loads placed around the ankle joints of healthy subjects. Results showed that the model predicted the knee extensions with a RMS angle error that was generally <or=8 degrees. The coefficients of determination between the measured and predicted data showed the model accounted for approximately 71%, approximately 94%, approximately 73%, and approximately 89% of the variances in the experimental maximum excursion, time to maximum excursion, maximum shortening velocity, and time to maximum shortening velocity, respectively. This study showed that our general non-isometric model predicted the lower limb motion in response to a range of stimulation frequencies and patterns, and external loads. This model can be implemented in an algorithm for controlling the position of the lower leg during the swing phase of gait during functional electrical stimulation.

Electric Stimulation↗

PubMed2005

Predicting optimal electrical stimulation for repetitive human muscle activation.

Functional electrical stimulation is the use of electrical currents to activate paralyzed muscles to produce functional movements. Muscle force output must meet or exceed the external load to maintain a posture or produce movements. A mathematical force-fatigue modeling system that predicts muscle force responses during repetitive electrical stimulation has been developed in our laboratory to help identify stimulation patterns that optimize force output for individual subjects. This study tests how well this model predicts the number of contractions that can be maintained above a required force level (successful contractions) during repetitive activation of a muscle. Healthy human quadriceps muscles were tested isometrically on 12 subjects. Data were first collected and used to parameterize the model. Next, the model was used to predict the number of successful contractions that were produced by trains with frequencies ranging from 5 to 100 Hz while the pulse durations and amplitudes were held constant. Finally, three clinically relevant stimulation frequencies were selected and tested to verify the model's predictions. Under these conditions, the model accurately predicted the number of successful contractions for clinically relevant stimulation frequencies. Furthermore, the model appears to have the potential to identify the stimulation frequency that maximizes muscle force output and minimizes fatigue for each subject.

Action Potentials↗

PubMed2005

Mathematical model that predicts isometric muscle forces for individuals with spinal cord injuries.

The ideal functional electrical stimulation (FES) system requires a mathematical model to provide feedforward control of the stimulation parameters such that they are optimal for different individuals across a range of physiological conditions, muscles, and tasks. Recently we tested and validated such a model using able-bodied subjects. The purpose of this study was to determine whether this model applied to persons with spinal cord injuries (SCI). To this end, the isometric force responses of the paralyzed quadriceps femoris muscles of 14 adolescents and young adults were tested. For each subject, the force responses to two six-pulse stimulation trains were used to identify the parameter values of the model and then the model was used to predict the force responses to three train patterns across a range of frequencies in both a nonfatigued and fatigued condition. The intraclass correlation coefficients (ICCs) between the experimental and predicted force-time integrals and peak forces were above 0.90 for 12 of the 13 stimulation trains tested in the nonfatigued condition and all 13 trains tested in the fatigued condition. The success of our model with SCI subjects leads us to believe that our model may be useful for designing optimal stimulation parameters for standing and ambulation in patients who use FES.

Adolescent↗

PubMed2003

Mass spectrometry of individual particles between 50 and 750 nm in diameter at the Baltimore Supersite.

The performance of the real-time single-particle mass spectrometer RSMS III is evaluated for ambient fine and ultrafine particle number concentration measurements. The RSMS III couples aerodynamic size selection with laser ablation time-of-flight mass spectrometry for single-particle analysis. It was deployed at the Baltimore particulate matter Supersite for semi-continuous operation over an 8-month period. The sampling protocol adopted for this study permitted the analysis of on average 2000 particles per day. The number of particles analyzed is a tradeoff between generating a statistically significant data set and maintaining instrument operation over a long period of time. The optimum particle size range of analysis was found to be ca. 50-770 nm in diameter, although particles as small as 45 nm and as large as 1250 nm were also analyzed. While nitrate, sulfate, and carbon (elemental and organic) were found to dominate the ambient aerosol, over 10% of the detected particles contained transition and/or heavy metals. The (size-dependent) detection efficiency, defined as the fraction of particles entering the inlet that are analyzed, was determined by comparison with scanning mobility particle sizing data. Using the experimentally determined detection efficiencies, particle number concentrations of specific chemical components were estimated. While the sampling protocol allowed the particle concentrations of major chemical components to be followed as a function of both time and particle size, minor components required averaging over time and/or size to achieve adequate precision.

Aerosols↗

PubMed2003

Mathematical models for fatigue minimization during functional electrical stimulation.

We previously reported the development of a force- and fatigue-model system that predicted accurately forces during repetitive fatiguing activation of human skeletal muscles using brief duration (six-pulse) stimulation trains. The model system was tested in the present study using force responses produced by longer duration stimulation trains, containing up to 50 pulses. Our results showed that our model successfully predicted the peak forces produced when the muscle was repetitively activated with stimulation trains of frequencies ranging from 20 to 40 Hz, train durations ranging from 0.5 to 1 s, and varied pulse patterns. The predicted peak forces throughout each protocol matched the experimental peak forces with r2 values above 0.9 and predicted successfully the forces at the end of each protocol with <15% error for all protocols tested. The success of our model system further supports its potential use for the design of optimal stimulation patterns for individual users during functional electrical stimulation.

Electric Stimulation↗

PubMed2002

A mathematical model that predicts the force-frequency relationship of human skeletal muscle.

In previous work we developed and validated a mathematical model that predicted force output from skeletal muscles subjected to six-pulse stimulation trains under isometric condition. The current study investigated the model's ability to predict force responses to longer stimulation trains under both nonfatigued and fatigued conditions. Using the six-pulse train model to predict the force produced by longer stimulation trains showed that the model was successful, but a modified parameter identification scheme was required. For most of the trains tested the model accounted for 95% of the variance in the experimental forces produced by stimulation trains, with mean frequencies from 12.5 to 100 HZ, train durations from 485 to 1000 ms, and number of pulses from 14 to 50 for both nonfatigued and fatigued muscles. The success of our mathematical model in predicting forces produced by stimulations with a wide range of frequencies, durations, and number of pulses implies great potential of the model for the identification of optimal activation patterns that should be used during functional electrical stimulation.

Actins↗

PubMed2002

Modeling the length dependence of isometric force in human quadriceps muscles.

Functional electrical stimulation is used to restore movement and function of paralyzed muscles by activating skeletal muscle artificially. An accurate and predictive mathematical model can facilitate the design of stimulation patterns that produce the desired force. The present study is a first step in developing a mathematical model for non-isometric muscle contractions. The goals of this study were to: (1) identify how our isometric force model's parameters vary with changes in knee joint angle, (2) identify the best knee flexion angle to parameterize this model, and (3) validate the model by comparing experimental data to predictions in response to a wide range of stimulation frequencies and muscle lengths. Results showed that by parabolically varying one of the free parameters with knee joint angle and fixing the other parameters at the values identified at 40 degrees of knee flexion, the model could predict the force responses to a wide range of stimulation frequencies and patterns at different muscle lengths. This work showed that the current isometric force model is capable of predicting the changes in skeletal muscle force at different muscle lengths.

Biomechanical Phenomena↗

PubMed2002

A predictive fatigue model--I: Predicting the effect of stimulation frequency and pattern on fatigue.

Previously we developed a mathematical force- and fatigue-model system that could predict fatigue produced by a wide range of frequencies and pulse patterns. However, the models tended to overestimate the forces produced by higher frequency trains. This paper presents modifications to our previously developed force- and fatigue-model system to improve the accuracy in predicting forces during repetitive activation of human skeletal muscle. By comparing the predictions produced by the modified force and fatigue models to those by our previous models, the modification appears to be successful. The current force- and fatigue-model system accounts for about 93% variance in experimental data produced by fatigue protocols consisting of trains with a wide range of frequencies and pulse patterns. In addition, the present models successfully predict the effect of stimulation frequency and pulse pattern on muscle fatigue. The success of our current force- and fatigue-model system suggests its potential use in helping to identify the optimal activation pattern to use during the clinical application of functional electrical stimulation.

Algorithms↗

PubMed2002

A predictive fatigue model--II: Predicting the effect of resting times on fatigue.

We have recently developed a force- and fatigue-model system that accurately predicted the effect of stimulation frequency on muscle fatigue. The data used to test the model were produced by stimulation trains with resting times of 500 ms. Because the resting times between stimulation trains affect muscle fatigue, this study tested the model's ability to predict the effect of resting times on fatigue. In addition, because this study included different subjects than those used to develop the model, the validity of the model could be tested. Data were collected from human quadriceps femoris muscles using fatigue protocols that included resting times of 500, 750, or 1000 ms. Our results showed that the model predicted fatigue as being a decreasing function of resting time, which was consistent with experimental data. Reliability tests between the experimental data and predictions showed interclass correlation coefficients of 0.97, 0.95, and 0.81 for the initial, final, and percentage decline in peak forces, respectively, suggesting strong agreement between the experimental data and the predictions by the model. The success of our current force- and fatigue-model system helps to validate the model and suggests its potential use in identifying the optimal activation pattern during clinical application of functional electrical stimulation.

Algorithms↗