Medical Engineering & Physics最新文献

In computational fluid dynamic studies related to blood flow, investigating the behavior of blood particles is crucial, especially red blood cells as they constitute a significant proportion of blood particles. Additionally, studying red blood cell movements is necessary, especially in stenotic artery geometries. A new multiphase scheme was utilized to demonstrate the effect of red blood cells on hemodynamics in complex coronary arteries and investigate the consequence of their motion. To investigate the effect of red blood cell movement on flow, the dense discrete phase model (DDPM) was used. This simulation was performed in 3D coronary arteries with different degrees of stenosis, utilizing blood pressure as inlet and outlet boundary conditions while assuming the arterial wall to be rigid. The model prediction shows good agreement with experimental data. Velocity values were comparable in both single-phase and two-phase flow simulations, but the shear stress in two-phase modeling had higher values. In the two-phase DDPM modeling, the recirculation areas indicated a higher probability of atherosclerosis plaque re-formation in the pre-stenosis area compared to the stenosis and post-stenosis areas. The DDPM model was found to be more effective in obtaining shear stress values in the artery. Additionally, this model provides good results compared to the single-phase model in investigating the movement of particles along the artery as well as recirculation areas that lead to the deposition of particles.

Laparoscopic instrument handles design and dimensions are crucial to determine the configuration of surgeons' hand grip and, therefore, can have a deleterious effect on overall surgical efficiency and surgeons' comfort. The aim of this study is to investigate the impact of laparoscopic handle size and hand surface area on surgical task performance. A single-blind, randomized crossover trial was carried out with 29 novice medical students. Participants performed three simulated tasks in "black box" simulators using two scissor-type handles of different sizes. Surgical performance was assessed by the number of errors and time required to complete each task. Hand anthropometric data were measured using a 3D scanner. Execution time was significantly higher when cutting and suturing tasks were performed with the smaller handle. In addition, hand surface area was positively correlated with peg transfer task time when performed with the standard handle and was correlated with cutting task time in small and standard handle groups. We also found positive correlations between execution time and the number of errors executed by larger-handed participants. Our findings indicate that laparoscopic handle size and hand area influence surgical performance, highlighting the importance of considering hand anthropometry variances in surgical instrument design.

Explosions in the battlefield can result in brain damage. Research on the effects of shock waves on brain tissue mainly focuses on the effects of single-orientation blast waves, while there have been few studies on the dynamic response of the human brain to directional explosions in different planes, multi-point explosions and repetitive explosions. Therefore, the brain tissue response and the intracranial pressure (ICP) caused by different blast loadings were numerically simulated using the CONWEP method. In the study of the blast in different directions, the lateral explosion blast wave was found to cause greater ICP than did blasts from other directions. When multi-point explosions occurred in the sagittal plane simultaneously, the ICP in the temporal lobe increased by 37.8 % and the ICP in the parietal lobe decreased by 17.6 %. When multi-point explosions occurred in the horizontal plane, the ICP in the frontal lobe increased by 61.8 % and the ICP in the temporal lobe increased by 12.2 %. In a study of repetitive explosions, the maximum ICP of the second blast increased by 40.6 % over that of the first blast, and that of the third blast increased by 61.2 % over that of the second blast. The ICP on the brain tissue from repetitive blasts can exceed 200 % of that of a single explosion blast wave.

Generation of polyethylene wear debris and peri‑prosthetic bone resorption have been identified as potential causes of acetabular component loosening in Total Hip Arthroplasty. This study was aimed at optimization of a functionally graded porous acetabular component to minimize peri‑prosthetic bone resorption and polyethylene liner wear. Porosity levels (porosity values at acetabular rim, and dome) and functional gradation exponents (radial and polar) were considered as the design parameters. The relationship between porosity and elastic properties were obtained from numerical homogenization. The multi-objective optimization was carried out using a non-dominated sorting genetic algorithm integrated with finite element analysis of the hemipelvises subject to various loading conditions of common daily activities. The optimal functionally graded porous designs (OFGPs −1, −2, −3, −4, −5) exhibited less strain-shielding in cancellous bone compared to solid metal-backing. Maximum bone-implant interfacial micromotions (63–68 μm) for OFGPs were found to be close to that of solid metal-backing (66 μm), which might facilitate bone ingrowth. However, OFGPs exhibited an increase in volumetric wear (3–10 %) compared to solid metal-backing. The objective functions were found to be more sensitive to changes in polar gradation exponent than radial gradation exponent, based on the Sobol’ method. Considering the common failure mechanisms, OFGP-1, having highly porous acetabular rim and less porous dome, appears to be a better alternative to the solid metal-backing.

The present study adopts a smartphone-based approach for the experimental characterization of coronary flows. Technically, Particle Tracking Velocimetry (PTV) measurements were performed using a smartphone camera and a low-power continuous wave laser in realistic healthy and stenosed phantoms of left anterior descending artery with inflow Reynolds numbers approximately ranging from 20 to 200. A Lagrangian–Eulerian mapping was performed to convert Lagrangian PTV velocity data to a Eulerian grid. Eulerian velocity and vorticity data obtained from smartphone-based PTV measurements were compared with Particle Image Velocimetry (PIV) measurements performed with a smartphone-based setup and with a conventional setup based on a high-power double-pulsed laser and a CMOS camera.

Smartphone-based PTV and PIV velocity flow fields substantially agreed with conventional PIV measurements, with the former characterized by lower average percentage differences than the latter. Discrepancies emerged at high flow regimes, especially at the stenosis throat, due to particle image blur generated by smartphone camera shutter speed and image acquisition frequency. In conclusion, the present findings demonstrate the feasibility of PTV measurements using a smartphone camera and a low-power light source for the in vitro characterization of cardiovascular flows for research, industrial and educational purposes, with advantages in terms of costs, safety and usability.

In this study, amino-functionalized mesoporous silica/hydroxyapatite nanoparticles (MSN_S/HAP) with the property of acid dissociation have been prepared as a traditional Chinese medicine monomer carriers to improve the drug loading rate and antibacterial properties of antimicrobial quercetin (QUE) in vitro. The experimental results confirm that the drug loading rate of MSNs/HAP is 28.94 %, which is about 3.6 times higher than that of aminated mesoporous sililca nanoparticles (MSNs). The drug release of QUE on MSNs/HAP is pH-sensitive in phosphate buffered saline (pH=4.0–7.4). The above fabricated traditional Chinese medicine monomer modified nanocomposites (QUE@MSNs/HAP) displays concentration-dependent inhibitory effect, which shows better antibacterial effect than free QUE. The minimum inhibitory concentration for two tested bacteria, Staphylococcus aureus (S.aureus) and Escherichia coli (E.coli), is 256 mg·L ^{− 1}. In summary, QUE@MSNs/HAP have successfully prepared, which not only improves the bio-availability of QUE, but also has acid-sensitive drug release properties. Compared with free QUE, its antibacterial performance significantly enhances, which provides a theoretical basis for the application of Chinese medicine molecules in bacterial treatment.

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD.

Objective

Early detection of cardiovascular diseases is based on accurate quantification of the left ventricle (LV) function parameters. In this paper, we propose a fully automatic framework for LV volume and mass quantification from 2D-cine MR images already segmented using U-Net.

Methods

The general framework consists of three main steps: Data preparation including automatic LV localization using a convolution neural network (CNN) and application of morphological operations to exclude papillary muscles from the LV cavity. The second step consists in automatically extracting the LV contours using U-Net architecture. Finally, by integrating temporal information which is manifested by a spatial motion of myocytes as a third dimension, we calculated LV volume, LV ejection fraction (LVEF) and left ventricle mass (LVM). Based on these parameters, we detected and quantified cardiac contraction abnormalities using Python software.

Results

CNN was trained with 35 patients and tested on 15 patients from the ACDC database with an accuracy of 99,15 %. U-Net architecture was trained using ACDC database and evaluated using local dataset with a Dice similarity coefficient (DSC) of 99,78 % and a Hausdorff Distance (HD) of 4.468 mm (p < 0,001). Quantification results showed a strong correlation with physiological measures with a Pearson correlation coefficient (PCC) of 0,991 for LV volume, 0.962 for LVEF, 0.98 for stroke volume (SV) and 0.923 for LVM after pillars’ elimination. Clinically, our method allows regional and accurate identification of pathological myocardial segments and can serve as a diagnostic aid tool of cardiac contraction abnormalities.

Conclusion

Experimental results prove the usefulness of the proposed method for LV volume and function quantification and verify its potential clinical applicability.

Background

The intervertebral disc exhibits not only strain rate dependence (viscoelasticity), but also significant asymmetry under tensile and compressive loads, which is of great significance for understanding the mechanism of lumbar disc injury under physiological loads.

Objective

In this study, the strain rate sensitive and tension-compression asymmetry of the intervertebral disc were analyzed by experiments and constitutive equation.

Method

The Sheep intervertebral disc samples were divided into three groups, in order to test the strain rate sensitive mechanical behavior, and the internal displacement as well as pressure distribution.

Results

The tensile stiffness is one order of magnitude smaller than the compression stiffness, and the logarithm of the elastic modulus is approximately linear with the logarithm of the strain rate, showing obvious tension-compression asymmetry and rate-related characteristics. In addition, the sensitivity to the strain rate is the same under these two loading conditions. The stress–strain curves of unloading and loading usually do not coincide, and form a Mullins effect hysteresis loop. The radial displacement distribution is opposite between the anterior and posterior region, which is consistent with the stress distribution. By introducing the damage factor into ZWT constitutive equation, the rate-dependent viscoelastic and weakening behavior of the intervertebral disc can be well described.

Brain-computer interfaces (BCIs) are used to understand brain functioning and develop therapies for neurological and neurodegenerative disorders. Therefore, BCIs are crucial in rehabilitating motor dysfunction and advancing motor imagery applications. For motor imagery, electroencephalogram (EEG) signals are used to classify the subject's intention of moving a body part without actually moving it. This paper presents a two-stage transformer-based architecture that employs handcrafted features and deep learning techniques to enhance the classification performance on benchmarked EEG signals. Stage-1 is built on parallel convolution based EEGNet, multi-head attention, and separable temporal convolution networks for spatiotemporal feature extraction. Further, for enhanced classification, in stage-2, additional features and embeddings extracted from stage-1 are used to train TabNet. In addition, a novel channel cluster swapping data augmentation technique is also developed to handle the issue of limited samples for training deep learning architectures. The developed two-stage architecture offered an average classification accuracy of 88.5 % and 88.3 % on the BCI Competition IV-2a and IV-2b datasets, respectively, which is approximately 3.0 % superior over similar recent reported works.