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Purpose
Two semi-empirical models were recently published, both making use of existing literature data, but each taking into account different physical phenomena that trigger hemolysis. In the first model, hemoglobin (Hb) release is described as a permeation procedure across the membrane, assuming a shear stress-dependent process (sublethal model). The second model only accounts for hemoglobin release that is caused by cell membrane breakdown, which occurs when red blood cells (RBC) undergo mechanically induced shearing for a period longer than the threshold time (nonuniform threshold model). In this paper, we introduce a model that considers the hemolysis generated by both these possible phenomena.
Methods
Since hemolysis can possibly be caused by permeation of hemoglobin through the RBC functional membrane as well as by release of hemoglobin from RBC membrane breakdown, our proposed model combines both these models. An experimental setup consisting of a Couette device was utilized for validation of our proposed model.
Results
A comparison is presented between the damage index (DI) predicted by the proposed model vs. the sublethal model vs. the nonthreshold model and experimental datasets. This comparison covers a wide range of shear stress for both human and porcine blood. An appropriate agreement between the measured DI and the DI predicted by the present model was obtained.
Conclusions
The semiempirical hemolysis model introduced in this paper aims for significantly enhanced conformity with experimental data. Two phenomenological outcomes become possible with the proposed approach: an estimation of the average time after which cell membrane breakdown occurs under the applied conditions, and a prediction of the ratio between the phenomena involved in hemolysis.
A new in vitro tool to investigate cardiac contractility under physiological mechanical conditions
(2019)
Advances in polymer science have significantly increased polymer applications in life sciences. We report the use of free-standing, ultra-thin polydimethylsiloxane (PDMS) membranes, called CellDrum, as cell culture substrates for an in vitro wound model. Dermal fibroblast monolayers from 28- and 88-year-old donors were cultured on CellDrums. By using stainless steel balls, circular cell-free areas were created in the cell layer (wounding). Sinusoidal strain of 1 Hz, 5% strain, was applied to membranes for 30 min in 4 sessions. The gap circumference and closure rate of un-stretched samples (controls) and stretched samples were monitored over 4 days to investigate the effects of donor age and mechanical strain on wound closure. A significant decrease in gap circumference and an increase in gap closure rate were observed in trained samples from younger donors and control samples from older donors. In contrast, a significant decrease in gap closure rate and an increase in wound circumference were observed in the trained samples from older donors. Through these results, we propose the model of a cell monolayer on stretchable CellDrums as a practical tool for wound healing research. The combination of biomechanical cell loading in conjunction with analyses such as gene/protein expression seems promising beyond the scope published here.
To prevent the reduction of muscle mass and loss of strength coming along with the human aging process, regular training with e.g. a leg press is suitable. However, the risk of training-induced injuries requires the continuous monitoring and controlling of the forces applied to the musculoskeletal system as well as the velocity along the motion trajectory and the range of motion. In this paper, an adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed. The response of the algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee and compared to the results of a higher-order iterative learning control algorithm, a robust iterative learning control and a recently proposed conventional norm-optimal iterative learning control algorithm. Although significant improvements in performance are made compared to the conventional norm-optimal iterative learning control algorithm with a small learning factor, for the developed approach as well as the robust iterative learning control algorithm small steady state errors occur.