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Despite the great developments above, numerous factors playing considerable roles in the process have not been entirely investigated while improving the microinjection task. Sufficient penetration force, penetration speed, and an accurate injection point are the key factors to achieve successful penetration in the microinjection process, which requires sensitive control methods [19]. The roles of microinjection speed and force feedback on cell deformation in cell penetration tasks are well recognized [20]. However, it has not been comprehensively studied.
Following the role of contact force in micro injection and how it affects the outcome of the system, analysing the effect of injection speed in microinjection on oocyte deformation is a must to reduce the deformation of a oocyte and improve embryo survivability rate. Since the injection micropipette is in contact with a cell at different indentation speeds, the contact force and deformation rate are also changing. There is a complex dynamical relation among contact force, injection speed, and oocyte deformation. Although speed analysis in microinjection is a promising way to obtain better outcomes in the context of getting less deformation on cells in comparison to constant injection speed [20], force and speed analysis on deformation creation in oocyte injection tasks remain a gap in the literature.
Figure 2 shows the graphical user interface (GUI) for controlling the injection system. This GUI can control the coarse and fine movement of the stage, enabling us to control the injection speed. The recorded force is also shown as graph on this window during the injection procedure. The software allows adjusting acceleration, deceleration, and injection speed.
Deformation of the oocyte membrane depends on the dynamics of injection, e.g., injection speed, and on the unique characteristics of the individual oocyte at the time of injection, e.g., morphological shape of the oocyte and maturation level.
Equation (6) represents the relation between injection speed, deformation and injection force, where coefficients K1, K2, K3, C2, and C3 are calibrated based on results obtained from in-house experiments.
Increasing the injection speed, the force fluctuation increases as shown in Figure 8. The fluctuation of the force is representative of the amount of vibrations during injection. In particular, the lateral and axial vibrations during injection cause the force fluctuation. Controlling the injection operation in the speeds over 0.6 mm/s has proved to be experimentally challenging due to the cell size and high fluctuations and vibrations occurring during injection. This is deemed to cause potential damage to the cell. At higher speeds a sudden increase in fluctuation (up to 55%) is experienced, preventing the injection to be conducted under stable conditions. Considering this, only one velocity has been examined above 0.6 mm/s, which is 1 mm/s in order to calibrate the numerical model.
The experimental results of the injection speed versus deformation at puncture are presented in Figure 9. As previously observed, the deformation reduces with an increase in the injection speed. However, it is possible to observe that the rate of reduction when increasing the injection speed is not constant.
Numerical results obtained for injection speed of 0.1 mm/s and 1 mm/s are compared with experimental findings in Figure 12a,c in terms of the force-deformation graphs. It is possible to observe as the numerical results are in good agreement with the experimental results, being able to accurately predict the force-deformation curve for both low and high injection speeds.
Comparison between experimental and numerical results in terms indentation force vs deformation for three various speeds: (a) 0.1 mm/s, (b) 0.6 mm/s, and (c) 1 mm/s.
The different stages of an oocyte injection are shown in Figure 13, where a comparison between experimental and numerical results is reported for injection speed of 0.1 mm/s. Results show that the numerical model is able to predict the oocyte deformation during injection in the three-dimensional space. In particular, it is able to capture the deformation caused by the injection micropipette motion and also the one due to the holding pipette on the opposite side.
For any other injection velocity, a linear variation of the Young modulus was then assumed between the two velocities used for calibration. Hence, for a different injection speed V the Young modulus can be calculated by means of Equation (9).
Figure 15 indicates the force-deformation graphs obtained by the FE model for the three different injection speeds examined. Numerical results indicate that an increase of the injection speed causes a reduction of deformation at puncture, an increase of the injection force, and an increase of fluctuations. These findings are in agreement with what was observed experimentally. In fact, increasing the injection speed, the force increases as well (Table 1), leading also to a considerable fluctuation (Figure 8).
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Conclusions: These findings indicated that Kenya's Ministry of Health needs to institute a concerted and continuous education program to bring refugee communities up to speed about COVID-19 and its prevention. In addition to disseminating information about the need to wear masks and repeatedly wash hands, supplies-masks, soap and access to water-need to be made available to poor refugee communities. Future research could explore which measures for disseminating factual information work best in refugee populations with different cultural norms and how best to target interventions to these groups.
Key features in the new financial industry include using supercomputers to analyze risk, manage assets, and execute trading at enhanced speeds. Increasing artificial intelligence, Lin argues, will continue to lower barriers to market entry and make traditional frameworks, like stock exchanges, less relevant. However, enhanced speed and inter-connected networks might make industry participants more vulnerable to industry crashes or cybercrimes.
Specifically, Lin advises that regulators should enhance existing disclosure rules, create ways to regulate safer speeds in finance, coordinate through inter-agency cooperation, develop improved governance tools, design customized rules whenever possible, and encourage industry participants to behave responsibly without providing public bailouts.
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Abstract:Oocyte deformation during injection is a major cause of potential cell damage which can lead to failure in the Intracytoplasmic Sperm Injection (ICSI) operation used as an infertility treatment. Injection speed plays an important role in the deformation creation. In this paper the effect of different speeds on deformation of zebrafish embryos is studied using a specially designed experimental set-up. An analytical model is developed in order to link injection force, deformation, and injection speed. A finite element (FE) model is also developed to analyse the effect of injection speed, allowing the production of additional information that is difficult to obtain experimentally, e.g., deformation and stress fields on the oocyte. The numerical model is validated against experimental results. Experimental results indicate that by increasing the injection speed, the deformation decreases. However, higher speeds cause higher levels of injection force and force fluctuation, leading to a higher vibration during injection. For this reason, an optimum injection speed range is determined. Finally, the FE model was validated against experimental results. The FE model is able to predict the force-deformation variation during injection for different speeds. This proves to be useful for future studies investigating different injection conditions.Keywords: Intracytoplasmic Sperm Injection (ICSI); injection speed; injection force; cell deformation; Finite Element Method (FEM); vibrations
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