Corresponding author der OTH Regensburg
Refine
Document Type
Language
- English (5)
Has Fulltext
- no (5) (remove)
Is part of the Bibliography
- no (5)
Keywords
- Biomechanik (2)
- Simulation (2)
- Dragonfly wing (1)
- Electromyography (1)
- Elektromyographie (1)
- Muscle (1)
- Musculoskeletal system (1)
- Signals (1)
- aerodynamics (1)
- computational fluid dynamics (1)
Institute
- Regensburg Center of Health Sciences and Technology - RCHST (5) (remove)
Begutachtungsstatus
- peer-reviewed (5)
BACKGROUND:
Tracheobronchial mucus plays a crucial role in pulmonary function by providing protection against inhaled pathogens. Due to its composition of water, mucins, and other biomolecules, it has a complex viscoelastic rheological behavior. This interplay of both viscous and elastic properties has not been fully described yet. In this study, we characterize the rheology of human mucus using oscillatory and transient tests. Based on the transient tests, we describe the material behavior of mucus under stress and strain loading by mathematical models.
METHODS:
Mucus samples were collected from clinically used endotracheal tubes. For rheological characterization, oscillatory amplitude-sweep and frequency-sweep tests, and transient creep-recovery and stress-relaxation tests were performed. The results of the transient test were approximated using the Burgers model, the Weibull distribution, and the six-element Maxwell model. The three-dimensional microstructure of the tracheobronchial mucus was visualized using scanning electron microscope imaging.
RESULTS:
Amplitude-sweep tests showed storage moduli ranging from 0.1 Pa to 10000 Pa and a median critical strain of 4 %. In frequency-sweep tests, storage and loss moduli increased with frequency, with the median of the storage modulus ranging from 10 Pa to 30 Pa, and the median of the loss modulus from 5 Pa to 14 Pa. The Burgers model approximates the viscoelastic behavior of tracheobronchial mucus during a constant load of stress appropriately (R2 of 0.99), and the Weibull distribution is suitable to predict the recovery of the sample after the removal of this stress (R2 of 0.99). The approximation of the stress-relaxation test data by a six-element Maxwell model shows a larger fit error (R2 of 0.91).
CONCLUSIONS:
This study provides a detailed description of all process steps of characterizing the rheology of tracheobronchial mucus, including sample collection, microstructure visualization, and rheological investigation. Based on this characterization, we provide mathematical models of the rheological behavior of tracheobronchial mucus. These can now be used to simulate mucus flow in the respiratory system through numerical approaches.
BACKGROUND:
Due to their corrugated profile, dragonfly wings have special aerodynamic characteristics during flying and gliding. OBJECTIVE: The aim of this study was to create a realistic 3D model of a dragonfly wing captured with a high-resolution micro-CT. To represent geometry changes in span and chord length and their aerodynamic effects, numerical investigations are carried out at different wing positions. METHODS:
The forewing of a Camacinia gigantea was captured using a micro-CT. After the wing was adapted an error-free 3D model resulted. The wing was cut every 5 mm and 2D numerical analyses were conducted in Fluent® 2020 R2 (ANSYS, Inc., Canonsburg, PA, USA). RESULTS: The highest lift coefficient, as well as the highest lift-to-drag ratio, resulted at 0 mm and an angle of attack (AOA) of 5∘. At AOAs of 10∘ or 15∘, the flow around the wing stalled and a Kármán vortex street behind the wing becomes
CONCLUSIONS:
The velocity is higher on the upper side of the wing compared to the lower side. The pressure acts vice versa. Due to the recirculation zones that are formed in valleys of the corrugation pattern the wing resembles the form of an airfoil.
Outputs of musculoskeletal models should be considered probabilistic rather than deterministic as they are affected by inaccuracies and estimations associated with the development of the model. One of these uncertainties being critical for modeling arises from the determination of the muscles' line of action and the physiological cross-sectional area. Therefore, the aim of this study was to evaluate the outcome sensitivity of model predictions from a musculoskeletal hand model in comparison to the uncertainty of these input parameters. For this purpose, the kinematics and muscle activities of different hand movements (abduction of the fingers, abduction of the thumb, and flexion of the thumb) were recorded. One thousand simulations were calculated for each movement using the Latin hypercube sampling method with a corresponding variation of the muscle origin/insertion points and the cross-sectional area. Comparing the standard hand to simulations incorporating uncertainties of input parameters shows no major deviations in on- and off-set time point of muscle activities. About 60% of simulations are located within a ± 30% interval around the standard model concerning joint reaction forces. The comparison with the variation of the input data leads to the conclusion that the standard hand model is able to provide not over-scattered outcomes and, therefore, can be considered relatively stable. These results are of practical importance to the personalization of a musculoskeletal model with subject-specific bone geometries and hence changed muscle line of action.
Regarding the prevention of injuries and rehabilitation of the human hand, musculoskeletal simulations using an inverse dynamics approach allow for insights of the muscle recruitment and thus acting forces on the hand. Currently, several hand models from various research groups are in use, which are mainly validated by the comparison of numerical and anatomical moment arms. In contrast to this validation and model-building technique by cadaver studies, the aim of this study is to further validate a recently published hand model [1] by analyzing numerically calculated muscle activities in comparison to experimentally measured electromyographical signals of the muscles. Therefore, the electromyographical signals of 10 hand muscles of five test subjects performing seven different hand movements were measured. The kinematics of these tasks were used as input for the hand model, and the numerical muscle activities were computed. To analyze the relationship between simulated and measured activities, the time difference of the muscle on- and off-set points was calculated, which resulted in a mean on- and off-set time difference of 0.58 s between the experimental data and the model. The largest differences were detected for movements that mainly addressed the wrist. One major issue comparing simulated and measured muscle activities of the hand is cross-talk. Nevertheless, the results show that the hand model fits the experiment quite accurately despite some limitations and is a further step toward patient-specific modeling of the upper extremity.
Ergonomic workplaces lead to fewer work-related musculoskeletal disorders and thus fewer sick days. There are various guidelines to help avoid harmful situations. However, these recommendations are often rather crude and often neglect the complex interaction of biomechanical loading and psychological stress. This study investigates whether machine learning algorithms can be used to predict mechanical and stress-related muscle activity for a standardized motion. For this purpose, experimental data were collected for trunk movement with and without additional psychological stress. Two different algorithms (XGBoost and TensorFlow) were used to model the experimental data. XGBoost in particular predicted the results very well. By combining it with musculoskeletal models, the method shown here can be used for workplace analysis but also for the development of real-time feedback systems in real workplace environments.