Journal of Mechanics in Medicine and Biology

This paper analyzes the impact of hybrid nanoparticles (Cu–TiO₂) on a two-dimensional peristaltic blood flow pattern in a nonuniform cylindrical annulus in the presence of an external induced magnetic field with wall slip. Further, this study focuses on the flow dynamics of single and hybrid nanofluids through endoscopic or catheterized effects. The mathematical model consisting of continuity, linear momentum, thermal energy, and Maxwell’s equations is simplified under the assumptions of long wavelength and negligible Reynolds number. The Homotopy perturbation method (HPM) is employed to get an approximate analytical solution of nonlinear dimensionless momentum equations. Based on the mathematical relationships and graphic visualization, the influence of the pertinent parameters described the velocity profile, temperature distribution, induced magnetic field, current density distribution, wall shear stress, and heat transfer coefficient. With the help of contours, the trapping phenomenon is also presented. The results reveal that the Lorentz force significantly reduces the Cu–TiO₂/blood nanofluid velocity, whereas the elevating Grashof number does the opposite. Compared with copper nanoparticles, hybrid nanoparticles have a higher wall shear stress. The increasing values of Reynolds numbers amplify the induced magnetic field on annular surfaces. In the axial direction, Lorentz force significantly decreases the current density distribution for hybrid nanofluid. Moreover, hybrid nanoparticles (Cu–TiO₂) exhibit superior heat transfer than Copper (Cu) nanoparticles in the blood-based fluid. According to the graphical outcomes, hybrid nanoparticles are comparatively more effective than unitary nanoparticles in the blood.

COVID-19 has become the world’s worst pandemic and has claimed over six million lives as of March 2022. The virus is now in alongside cancer as one of the most common causes of death. Likewise, there is no definitive or unique treatment for COVID-19 outside of a selected few drugs approved by the Food and Drug Administration (FDA). While Artificial Intelligence (AI) can be used to generate molecules that target Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, such molecules are novel and do not yet exist in the market. With the emergence and availability of several drug datasets related to COVID-19 (tests, images, graphs, and ChEMBLs), recent works based on Deep Learning (DL) techniques have been employed to generate molecules and check the effectiveness of existing molecules on COVID-19. In our study, we investigated the benefits of an Encoder–Decoder (ED) architecture based on Long Short-Term Memory (LSTM) cells. As a result, the molecules were converted into a vector during the encoding phase, which was then decoded back into SMILES molecules strings. We propose an approach to incorporate four features of Principal Components Analysis (PCA) with Encoder–Decoder Long Short-Term Memory (ED-LSTM) for regularization, which means that, instead of avoiding linear mapping, we assumed that the data could be linearly separable. We concluded that ED-LSTM with unit norm constraint has the best reconstruction accuracy in the context of generating molecules. The resulting dataset was used with the aid of virtual screening and convolutional neural networks to check the drugs that have the best binding affinity with SARS-CoV-2. We achieved an accuracy of 87.35% on the test set.

Keratoconus is an eye disease causing progressive corneal thinning. At an early stage (fruste keratoconus), the symptoms can overlap with those of other eye disorders, making the diagnosis difficult. This led us to propose a new image processing pipeline to automatically calculate the main descriptors used by Ophthalmologists to detect keratoconus, and then classify these data in order to propose an intelligent system able to help specialists in the early recognition of this pathology. To accomplish this, we elaborated a new benchmark database from a corneal topographic Orbscan II device. For keratoconus classification, five different machine learning methods are tested on our new locally collected database, which are: K-Nearest Neighbors (K-NN), Support Vector Machines (SVM), Decision Trees (DT), and two neural networks classifiers (the Radial Basis Function (RBF) and the Multi-Layer Perceptron (MLP)). Experimental results indicate that all classifiers achieved good precision when using all descriptors (the numerical parameters given by the ORBSCAN II topographer and the descriptors obtained after an image processing of the topography map). Furthermore, the SVM outperformed the other classifiers with an accuracy rate of 95.04%. These results were confirmed and validated by a group of experts in ophthalmology and prove the efficiency of our system and the coherence of our new database.

This study aims to determine the thermodynamic performance of the cardiovascular system for healthy and cardiomegaly conditions where work done by the heart and lung is considered as input expenses. Analysis based on the second law of thermodynamics was performed under three distinct physiological conditions: resting, moderate, and excessive activity levels. From this analysis, it is noted that, exergy transfer rate increases with the increase in the physiological activity level. The exergetic efficiency of the cardiovascular system is about 55% at resting and changes by about 4% with physical activity level. The human cardiovascular system efficiency is increased with an increase in breathing air inlet temperature and relative humidity irrespective of physiological conditions. The work also quantifies the efficiency of the heart and lung as individual systems. For a fixed breathing air inlet temperature and relative humidity, the cardiovascular system performance increases with a decrease in physical activity levels.

For the treatment of degenerative disc disease (DDD) and associated back pain and instability in the spinal segments, the success of pedicle screw-based spinal implant system has been found in the literature. Over the last couple of years, various nonfusion implants are made for the treatment of minor DDD. In this study, the biomechanical response of pedicle screw-based implant with semi-rigid rod material on the lumbar spine is evaluated. Computed tomography (CT) scan-based finite element (FE) model is used to evaluate the performance of pedicle screw fixation with flexible rods made of carbon fiber-reinforced polyetheretherketone (CFR PEEK). Typical physiological loadings are applied to the intact and implanted lumbar spine model. It is observed that the pediclescrew with the CFR-PEEK rod can restore the range of motion (ROM) on the surgical segment. The CFR-PEEK RODs carried out more loads and reduced the stress on the vertebral body. Using traditional rigid rods, ROM is generally almost restricted on the fusion zone, which can be improved by CFR-PEEK rod system to a certain degree. After successful experimental validation and clinical trial, the CFR-PEEK rod may be recommended for improved clinical outcomes.

Preface: Coronavirus has several mechanisms that facilitate its entry into the target cell such as cholesterol by acting concurrently with angiotensin-converting enzyme (ACE2) at entry site, where the virus uses it to increase its entry and spread. We conducted a practical research for measuring Cholesterol, Triglyceride (TG) and Lactate dehydrogenase concentrations in COVID-19 patients. Objectives: The aim of this research is to see the correlation between Cholesterol, TG and Lactate dehydrogenase and the severity of coronavirus infection. Methods: This study is made at Al-Zahra Teaching Hospital during the period from March 2021 to July 2021 and it was performed on two groups: patients group that involved 30 samples (15 male and 15 female), age ranging from 18 to 84 years old and another group consisting of 30 individuals who were not infected with SARS-CoV-2. Results: Our findings show that the high value for markers under this study associated with coronavirus was detected in lactate dehydrogenase ($505.07\pm 26.934$ IU/l), ($227.83\pm 24.660$ Mg/dl) for TG and ($227.53\pm 19.805$ Mg/dl) for cholesterol. High concentration of cholesterol is shown in the age category between 54 and 64 years is represented as (487 Mg/dl and 502 Mg/dl), TG at ages 42, 54 and 64 years is represented as 497, 509 and 521 Mg/dl, Lactate dehydrogenase at ages 80–84 years that represented is as 739, 809 and 812 IU/l. According to physiological parameters which were elevated, a high concentration was observed in almost both sexes, as males were more affected than females when determining cholesterol at (500 Mg/dl), while females had more than males with respect to lactate dehydrogenase and TGs at (520 Mg/dl and 800 IU/l), respectively. In the correlation test, we found the highest significant differences, according to the correlation test, between cholesterol and lactate dehydrogenase at (1) cholesterol and TG at (0.000), TG and lactate dehydrogenase at (0.739). Conclusions: We conclude that COVID-19 patients suffer from increased markers under the studies on cholesterol, TG and lactate dehydrogenase.

Few studies are interested in the noninvasive determination of propagation parameters. Reflected waves can be used as an intelligent tool for therapeutic measures. The amplitude and arrival time of reflected wave play an interesting role in investigating cardiovascular diseases. Therefore, this study aims to implement a method based on cepstral analysis for noninvasive decomposition of the incident and reflected waves and determination of local propagation parameters. The cepstral analysis is applied to the blood velocity, which is measured by using phase-contrast magnetic resonance imaging (PCMR) in 20 subjects. The determination of propagation parameters: arrival time, attenuation coefficient, reflection amplitude, and reflection coefficient is based on a mathematical model developed in the previous work. To evaluate the precision of the proposed approach, we focused on the effect of age. In addition, we intended to compare the obtained results to those obtained by the wave intensity analysis (WIA) method. A statistical test analysis was conducted to establish the relationship between the propagation parameters and the age. Our experimental results showed that there is no significant difference in terms of propagation parameters with age ($p>0.05$). Our experimental results correlated with reference values reported in previous studies conducted on the internal arterial carotid. The propagate parameters obtained by the proposed method varied slightly with age. Moreover, the reflection coefficient detected using our proposed method was close to that detected using WIA. We can conclude that the technique described in this paper offers a promising and efficient approach to separating measured velocity waveforms into their incident and reflected components noninvasively.

A prosthetic knee is predominantly subjected to cyclic action leading to fatigue failure during its operation. However, cyclic failure is more severe as it occurs below the yield strength and may happen even without prior indication. This study presents a cyclic structural analysis of an existing polycentric mechanical prosthetic knee by finite element simulation and its experimental validation as per the ISO 10328:2016 standard. The three-dimensional (3D) model is imported to the ANSYS 20.1 workbench to study stress distribution and fatigue life in the knee for ensuring its safety performance. The maximum von-Mises stresses developed at the front and back joint bars made of A390.0-T5 and A390.0-T6 cast aluminium are 100 and 89MPa, respectively, in both loading conditions compared to their mechanical fatigue strength of 89MPa. The maximum deformation and average number of cycles for fatigue failure are 2.66 and 2.53mm and $2.95\times 10^6$ and $2.81\times 10^6$ for simulation and experimental results, respectively. These results suggest that this polycentric knee unit has poor fatigue strength and thereby does not meet the ISO structural standard. A validation test has been performed with an average error of 4.75% between the simulation and experiments results showing higher reliability.

A two-dimensional (2D) steady boundary layer flow along with heat transfer of a self-similar biomagnetic fluid over a permeable moving flat plate has been taken into consideration in this work. The flow is contemplated to be embedded by a magnetic dipole of sufficient magnetic strength. Transpiration as well as movement along the wall is also regarded. By imposing the appropriate similarity technique, the governing equations are converted into a system of coupled nondimensional equations. An efficient numerical technique has been incorporated to solve these dimensionless coupled nonlinear ordinary differential equations. The existence of dual solutions along with their stability has been established with the consideration of stability analysis. We discovered from our analysis that two solutions exist (one stable and another unstable) for the arbitrary values of transpiration, movement velocity and biomagnetic interaction parameters on flow and physical parameters. The attained results are demonstrated graphically and in tabular form. For the validity of our numerical scheme, we compared our findings with others previously published and found significant agreement.

In this study, we develop a novel multi-posture lower limb rehabilitation robot with three postures, which can provide different amplitudes and frequencies of rehabilitation training for hip, knee and ankle joints, respectively. The kinematic and dynamic analyses of the robot are carried out to solve the kinematic forward and backward solutions and the Lagrangian dynamics equations of the lower limbs. The angle, angular velocity and angular acceleration ideal trajectory curves of the rehabilitation motion are derived by using a quintic polynomial trajectory planning scheme. An improved ions motion optimization (IIMO) algorithm is proposed and applied to optimize the initial weight of back propagation (BP) neural network, and algorithm is used to adjust five parameters of fractional order ${\mathrm{\text{PI}}}^{\lambda }{\mathrm{\text{D}}}^{\mu }$ (${\mathrm{\text{FOPI}}}^{\lambda }{\mathrm{\text{D}}}^{\mu }$) control in controller design. The passive training experiment results of prototype show that the designed controller has the largest average error of angle and angular velocity of hip, knee and ankle joints in high amplitude and high frequency movement mode, which are 1.091^∘, 0.716^∘, 0.412^∘, 1.551^∘/s, 1.394^∘/s, 1.498^∘/s, respectively. At low amplitude and low frequency, the maximum average errors are the smallest, which are 0.351^∘, 0.341^∘, 0.167^∘; 0.833^∘/s, 0.842^∘/s, 0.398^∘/s, respectively. The actual trajectory curve fits well with the designed one. The highest accuracy of angle and angular velocity can reach 99.165% and 99.116% through comprehensive comparison of all motion modes. Therefore, the overall error is small. The stability of rehabilitation training process is ensured, and the rationality and effectiveness of trajectory planning and control design are verified.