Journal of Mechanics in Medicine and Biology

A form of dementia is Alzheimer’s syndrome, also known as a neurodegenerative syndrome, damages the neuron cells in a selective mode. The count of patients that is increased gradually here by the disease is considered a worldwide concern, which may cause death in more cases. The rapid and accurate detection and categorization of Alzheimer’s disease have obtained enormous awareness from researchers due to the studies of a deep model. However, the productive detection and categorization of Alzheimer’s disease with reliable biomarkers are more challenging tasks. In this paper, Manta crow search optimization (MCSO)-based Actor Critic Neural Network (ACNN) is devised for the Alzheimer classification process. The Region of Interest (ROI) is extracted based on the thresholding process, and the preprocessing is done using the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. Moreover, the Sparse Fuzzy C-means (Sparse FCM) model has been in employment to segment preprocessed images. The feature extraction is a productive process for classifying Alzheimer’s disease. The ACNN classifier is utilized for performing Alzheimer’s disease classification process. Furthermore, the ACNN classifier is skilled by a devised optimization algorithm named the MCSO model. The projected MCSO-based ACNN outperformed other existing techniques with a testing accuracy of 0.9295, sensitivity of 0.9378, and specificity of 0.9354.

The importance and reliability of implants, have brought biomedical engineers and material scientists together to aid orthopedic specialists to increase their durability and efficiency. Porous materials have been used diversely in biomedical applications namely, scaffold designs and orthopedic implants. However, satisfying all biological as well as mechanical conditions of the human body has never been achieved. Hence, in this paper, to mimic the hierarchical nature of human bone, a simple Hollow Cube unit cell was subjected to twelve different sets of micropores in its struts, leading to an increased internal surface area and overall porosity. Thus, 3D models of the unit cells with hybrid porosities were studied using finite elements method. It was observed that with adding micropores inside already porous unit cells, about 25% decrease was obtained in elastic modulus, ranging between 7.9GPa to 11GPa and, roughly 100% increase in internal surface area and surface-to-volume ratio, moreover, the porosity rose from 64% to 87%. Sequentially, proper hierarchical unit cells were evaluated according to elastic modulus and failure analyses. In the end, to validate, the numerical and experimental results were compared and 9% error was observed between them.

In view of the shortcomings of the existing reversible watermarking algorithms for encrypted images, such as low embedding rate and incomplete separation, and at the same time ensuring the embedding rate and the quality of the watermarked image, this paper proposes a reversible watermarking algorithm for encrypted images with high embedding rate and separable effect. This paper proposes a reversible watermarking algorithm for encrypted images with high embedding rate and separable effect. First, the plaintext image is encrypted by using the encryption series generated by chaotic system and Zu Chongzhi’s encryption algorithm; Second, analyze the PSNR value of the image under different interpolation conditions, generate the corresponding expected interpolation, and embed the watermark information into the image based on the expected interpolation; Finally, the original image can be recovered by using the encryption key, and the watermark information can be obtained by using the embedded key. The security and embedding ability of the algorithm are analyzed through simulation experiments. The results show that the introduction of ZUC stream cipher can expand the key space of the algorithm, have high security and achieve the separable effect. The embedding of watermark information has little impact on the ciphertext image, and the embedding rate is also higher than the contrast algorithm under the condition of ensuring the visual effect of the image.

Objective: To reduce the shielding effect caused by the large elastic modulus of metal fusion devices after osteotomy for the treatment of scoliosis. Methods: A personalized fusion device was designed using reverse engineering techniques, three-dimensional modeling, topology optimization, and finite element analysis. A finite element model of the lumbar spine after orthopedic surgery was established from an actual case. A fusion device was implanted into the lumbar spine before and after the optimized design for simulation calculations, respectively. The similarities and differences in the mechanical properties of the different fusion devices and vertebrae were compared. Based on the topological optimization of the mechanical properties of the fusion system, a lightweight fusion device was designed and the stress-shielding effect on the lumbar vertebrae was improved. Results: The topologically optimized fusion device described in the current case was 60% lighter while maintaining the strength of the fusion. After optimization, the average strain of the fusion was increased by up to 200% and the stiffness was significantly decreased. The average equivalent stress of cancellous bone was increased by up to 9.55% for the PEEK fusion device. In contrast, the topologically optimized fusion can increase the average equivalent stress of cortical bone by 5.63% and reduce the stress-masking effect of vertebrae. Conclusion: Topologically optimized titanium alloy fusion devices can significantly reduce the mass of the implant as well as reduce fusion stiffness. Additionally, a topology-optimized titanium alloy fusion device can effectively improve the stress-shielding effect of cortical bone.

In this paper, an attempt has been made to analyze the effect of different physiological conditions i.e., rest, moderate and extremely active on the branch Reynolds number, pressure drop, lost work, alveolar partial O₂ pressure, airway resistance and surface tension. From the analysis, it has been observed that local Reynolds number and tracheal pressure drop along with the generations of branch. The alveolar O₂ pressure and surface tension decrease with the increased activity level. Work lost due to different major and minor losses in the respiratory tract increases with the increase in activity level. A similar trend was also found for the airway resistance i.e., increases with the increase in activity level. A new parameter, Breathing Work Number (BWN) in respiratory mechanics is introduced. BWN is the ratio of the work of breathing to the sum of work of breathing and work loss, which might find a significant application to denote the respiratory health. From the analysis, it is found that BWN decreases with the increase in activity level.

Many of the used features in sudden cardiac death (SCD) classification algorithms are based on features present in the autonomic system. However, changes in the autonomic system occur in both SCD subjects and patients with congestive heart failure (CHF). Therefore, many overlaps are observed in the features extracted from the cardiac signals of these two groups. To solve this challenge, this paper studies the changes in the multifractal dimension in patients with SCD and compares it with the subjects with CHF using the heart rate variability (HRV) signal processing. For this purpose, HRV signals are initially extracted, and their four sub-signals are determined using the empirical mode decomposition (EMD) method. Afterward, the instant amplitude of each sub-signal obtained in the previous step is calculated using the Teager energy method; thus, new signals are generated through the utilization of these instant amplitudes. Subsequently, modifications in each new signal’s fractal dimensions are obtained using the multifractal detrended fluctuation analysis (MF-DFA) method. The appropriate features are selected using the t-test method and are applied to the support vector machine algorithm as input data. The proposed algorithm can differentiate the signal of SCD subjects with an average accuracy of 84.08% in 26min prior to the event.

The phonocardiogram (PCG) signal is sometimes affected by added parameters that reflect the presence of a specific pathology. The intensity or the energy of the signal is one of the most reliable parameters when studying cardiac severity. Yet, in a pathological electrophysiological and audio signal, the severity information does not fully remain in the intensity or energy, but in other variables. In this paper, we will discuss the ability of a time-frequency parameter to discriminate, separate, and monitor the pathological cardiac severity levels. We studied 14 PCG signal from eight pathologies, six of them contain clicks (reduce murmurs), and eight murmur PCG signals with four different cardiac severity levels. We then calculated the entropy of approximation coefficients (EAC) from a discrete wavelet transform (DWT) analysis, to differentiate the PCG signals with clicks from those with murmurs and to assess the cardiac severity evolution. Since the entropy EAC is also related to the signal’s intensity (energy), we compared it to the energetic ratio (ER) evolution, a parameter widely used for PCG signals discrimination and classification, which revealed that the EAC provied better results for the paper' purposes.

The objective of this study is to show how important a compliant wall technique is in simulating non-Newtonian and pulsatile blood flows in arteriovenous fistula (AVF). The three-dimensional idealized geometry is used to investigate the local hemodynamics in the end-to-side, radio-cephalic AVF using computational fluid-stricture interaction (FSI) simulation. The third-order Yeoh law is used to model the behavior of the hyperelatic vessel walls. Hemodynamic parameters such as velocity, wall shear stress (WSS), oscillatory shear index (OSI), vorticity, and venous outflow rate are calculated. The results extracted for WSS on comparison of rigid and compliance wall, rigid wall WSS are 45–48.5% larger values than the compliance wall. The difference between compliance wall and rigid wall OSI is 11.5% and the rigid wall is a 10.86% decrease in compliance wall. The difference between the rigid wall AVF vorticity and the compliance wall vorticity is 18.34%, and the vorticity in the compliance wall is a 20.2% increase over the rigid wall. Maximum principle stress occurs at anastomosis (1335Pa) and 507Pa, 93Pa on vein and artery, respectively. Finally, we conclude that the artery bed and heel of AVF are prone areas of Intimal Hyperplasia. The structural result shows the tendency of the inflammation pattern of the vein required for AVF maturation. ANSYS is a highly effective commercial tool for modeling real-world problems and performing multi-physics numerical simulations.

The high rate of knee osteoarthritis has raised the need for accurate diagnostic methods. In this study, we propose a precise detection method using the center of pressure data obtained from the patients. The introduced automatic detection pipeline is based on the two modern algorithms of grey wolf and BAT. The extracted statistical features and the obtained data from healthy individuals and patients are processed with the grey wolf binary algorithm. The results are fed into the binary bat algorithm to select important features and increase the pipeline accuracy. Then the groups are classified using a four-layer neural network. We show that the proposed method with a simple four-layer neural network offers fantastic accuracy in high-speed processing large data and classifies the high-dimensional knee osteoarthritis center of pressure data with appropriate precision, recall, specificity, and F1 values. The proposed method has direct applications in knee osteoarthritis diagnostics in clinics.

The cooperative system of calcium (Ca${}^{2+}$) and inositol 1,4,5-trisphosphate (IP₃) plays a crucial role in maintaining structure and functions of T lymphocyte cells including the production of nuclear factor of activated T cells (NFAT) as an immunological response. NFAT regulates several mechanisms and dysregulation in this process can lead to various disorders like inflammatory bowel diseases, etc. In this study, the two-way feedback model of Ca${}^{2+}$ and IP₃ dynamics regulating NFAT production in T lymphocytes has been constructed. A system of nonlinear reaction-diffusion equations for calcium and IP₃ for a one-dimensional case has been employed in this proposed model. The numerical simulation is performed using finite element and the Crank–Nicholson method. The effects of the parameters like Ryanodine Receptor (RyR), source amplitude and buffers on active NFAT production have been analyzed. The novel insights about regulatory and dysregulation events and mechanisms of Ca${}^{2+}$, IP₃ and NFAT are concluded from these results.

In this paper, the effects of both geometric parameters and mineral density on the evolution of stresses generated in a cementless hip implant are investigated to enable orthopedic surgeons to make an appropriate selection. A design of experiment was used, followed by the analysis of variance (ANOVA) to identify the effects of geometric and physical parameters in terms of bone density on a femoral implant. Twenty-seven implants of different geometric dimensions were considered, without modification of the topology. Multiple regression analysis was chosen to replace the finite element method to predict the level of stresses generated in the stem, in terms of life expectancy, allowing for optimal geometric parameter selection. Three parameters of the stem were selected, namely, stem length (SL), neck length (NL), and neck shaft angle (NSA), where each parameter is apprehended by three levels. Based on a design (3${}^3)$, 27 femur–implant CAD models were created, to analyze the effect of three limiting densities (low, medium, and high) for the estimation of maximum stresses by finite elements. The results obtained by ANOVA for the three densities, respectively, for the low density the SL had an impact of the order of 58.5% on the level of von Mises constraints followed by the NL and NSA. For the medium density, it was observed that the SL remains the most influential parameter, with a rate of 41.2%, while for the high density the NL becomes the most influential (37.5%) followed by the SL and the NSA. The regression model used showed very good accuracy in terms of predicting the level of von Mises stress, compared to the results obtained by the finite element method.