Computer Methods in Biomechanics and Biomedical Engineering最新文献

Electroencephalography analysis is critical for brain computer interface research. The primary goal of brain-computer interface is to establish communication between impaired people and others via brain signals. The classification of multi-level mental activities using the brain-computer interface has recently become more difficult, which affects the accuracy of the classification. However, several deep learning-based techniques have attempted to identify mental tasks using multidimensional data. The hybrid capsule attention-based convolutional bidirectional gated recurrent unit model was introduced in this study as a hybrid deep learning technique for multi-class mental task categorization. Initially, the obtained electroencephalography data is pre-processed with a digital low-pass Butterworth filter and a discrete wavelet transform to remove disturbances. The spectrally adaptive common spatial pattern is used to extract characteristics from pre-processed electroencephalography data. The retrieved features were then loaded into the suggested classification model, which was used to extract the features deeply and classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using a dung beetle optimization approach. Finally, the proposed classifier is assessed for several types of mental task classification using the provided dataset. The simulation results are compared with the existing state-of-the-art techniques in terms of accuracy, precision, recall, etc. The accuracy obtained using the proposed approach is 97.87%, which is higher than that of the other existing methods.

To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.

This study aimed to identify important clotting associated genes (CRGs) associated with septic shock in children and explore possible important mechanisms of the disease. Five hub genes with diagnostic performance were identified using GEO database and data from literature. These hub genes have strong correlation with immune cells. ceRNA network was constructed to explore potential pathogenic mechanisms. Ten candidate small molecule compounds were identified. In summary, the hub genes may play an important role in the immunity and disease development of septic shock, providing new ideas and strategies for future diagnosis and mechanism evaluation of children with septic shock.

While injuries sustained from body armour backface deformation (BFD) have not been well-documented in military injury trauma registries, data from US law enforcement officers, animal tests and currently available data pertaining to military combatants has shown that BFD can not only cause minor injuries, but also result in serious trauma. However, the nature and severity of injuries sustained depends on a multitude of factors including the projectile type, the impact location and velocity, and the specific type of body armour worn. The difficulties involved in current measurement techniques for ballistic testing has led researchers to seek alternative techniques to evaluate the level of protection from body armour, such as the finite element (FE) method. In the current study, a systematic review of the open literature was undertaken using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology. The aim was to summarise the literature pertaining to the development and application of FE models to investigate body armour BFD and behind armour blunt trauma (BABT), and included FE models representing the projectile, clay-based mediums, ballistic gelatine and the human torso. Using the keywords 'behind armour*', 'ballistic blunt trauma', 'BABT', 'backface signature', 'backface deformation', 'BFS', 'BFD', 'wound ballistic', 'ballistic impact testing', 'body armour', 'bullet proof vest', 'ballistic vest', 'Finite Element*' and 'FE', an electronic database search of EBSCOhost, Google Scholar, ProQuest, Scopus, Standards, Web of Science and PubMed was conducted, and included peer-reviewed journal articles, review papers, research reports, conference papers, and MSc or PhD theses. While this research demonstrates the potential of FE analysis for recreating realistic blunt impact scenarios and enhancing the current understanding of BABT mechanisms, a common limitation in most studies is the lack of validation. Thus, in order to address this issue, it is proposed that injury predictions from FE models be correlated with trauma data from soldiers who have sustained BABT. Consequently, pressure and energy distributions within the organs can be used to interpret the effects of non-penetrating ballistic impacts on the human torso. Bridging the gap between simulation and real-world data is essential in order to validate FE models and enhance their utility in optimising body armour design and employing injury mitigation strategies.

Injuries arising from car crashes are ubiquitous across the globe and account for over 1.3 million fatalities annually. 93% of mortalities are observed in middle- and low-income countries owing to the lack of infrastructure in the safety assessment of car designs. It is therefore imperative to predict the extent of injuries to the occupants during car crashes, which would lead to safer vehicle design. To date, conventional computational testing methods use Hybrid III dummies, which fail to reproduce fracture and tear injuries. In this work, a full-frontal collision of a vehicle against a rigid wall with a highly biofidelic human body model of an occupant was simulated for the first time to investigate fractures and tears using a novel fracture modeling technique. Fractures were observed in ribs (5-7), which occurred at stresses of 120 MPa at the left lateral vertebrosternal region. In the lower extremity, tears in the ligaments at 70.80 MPa, and fractures in the tibia and femur at 236 MPa were quantified. Stresses in the skull were limited to 11 MPa, indicating a possibility of concussion rather than fractures. The developed computational model would be indispensable for car manufacturers to test the crash impact on the human body at all possible accident scenarios accurately, which will help design better solutions for automotive injury mitigation.

Renal artery stenosis (RAS) hypertension is a common type of secondary hypertension. This paper aimed to explore how unilateral renal artery stenosis (Uni-RAS) and bilateral renal artery stenosis (Bi-RAS) caused renovascular hypertension with the fluid-structure interaction (FSI) method. Based on a real RAS model, 20 ideal models with different stenosis degrees were established by modifying the stenosis segment. The hemodynamic parameters at different degrees of stenosis, mass flow rate (MFR), pressure drop (PD), fractional flow reserve (FFR), oscillatory shear index (OSI), and relative residence time (RRT), were numerically calculated by the computational fluid dynamics (CFD) method. The numerical results showed that RAS caused the decrease of MFR, and the increase of PD and the proportion of high OSI and high RRT. In the case of RAS, it could not be regarded as a reference indicator for causing renovascular hypertension that the value of FFR was greater than 0.9. In addition, the results of the statistical analysis indicated that Uni-RAS and Bi-RAS were statistically different for MFR, PD and the proportion of high RRT.

The controller is important for the artificial pancreas to guide insulin infusion in diabetic therapy. However, the inter- and intra-individual variability and time delay of glucose metabolism bring challenges to control glucose within a normal range. In this study, a multivariable identification based model predictive control (mi-MPC) is developed to overcome the above challenges. Firstly, an integrated glucose-insulin model is established to describe insulin absorption, glucose-insulin interaction under meal disturbance, and glucose transport. On this basis, an observable glucose-insulin dynamic model is formed, in which the individual parameters and disturbances can be identified by designing a particle filtering estimator. Next, embedded with the identified glucose-insulin dynamic model, a mi-MPC method is proposed. In this controller, plasma glucose concentration (PGC), an important variable and indicator of glucose regulation, is estimated and controlled directly. Finally, the method was tested on 30 in-silico subjects produced by the UVa/Padova simulator. The results show that the mi-MPC method including the model, individual identification, and the controller can regulate glucose with the mean value of 7.45 mmol/L without meal announcement.

Modern medicine has taken energy loss during cilia beating in the human stomach, which under some circumstances causes blood flow to become acidic, very seriously. In current report covering a whole advancement and results for the impact of Rabinowitsch model with cilia-driven flow analysis with the help of ciliary beating in a cylindrical tube. The fluid is incompressible, and layers of fluid do not mix. The fluid flow with heat and mass transfer is firstly modeled in wave and then transformed into fixed frame. Exact solutions for stresses, temperature velocity, and concentration profiles whereas numerical pressure rise is obtained subject to relevant boundary conditions. The behavior of incipient parameters is shown graphically (plotted in MATHEMATICA 13.0) in the results section. The key findings obtained from graphical results show that maximum magnitude for velocity and temperature is achieved in middle layer of fluid whereas in the outer layer concentration profile is maximum. The current study may help researchers to develop new treatments for diseases such as cystic fibrosis, in which impaired ciliary function leads to mucus accumulation in the lungs. The attained exact and numerical outcomes are novel and offered here for first time in literature.

This study proposes a rapid method for determining pregnancy status based on fingertip pulse signals. A finger pulse sensor collects data, which is processed into unified multimodal signals. The Bamboo-Net model, combining ResNet, LSTM, and 1D-CNN, extracts key features from time, frequency, and time-frequency domains. Tested on 346 training and 138 testing samples, the model achieves 91% accuracy with 6 s input, outperforming mainstream methods. Recognition rates for mid and late pregnancy are higher than for early pregnancy, highlighting its potential for practical applications.

Background: The prognosis of cancers is strongly connected with nitrogen metabolism (NM), which plays a critical role in the microenvironment and growth of tumors. It is unsubstantiated, however, how important NM-related genes are for the prognosis of hepatocellular carcinoma (HCC).

Methods: Using publicly available data, we examined potential mechanisms of NM-related genes in HCC, created a predictive model, and assessed immune infiltration and medication sensitivity.

Results: A prognostic model, which included 12 NM genes (COLQ, GNE, ISCU, MSRA, SARS2, SPHK1, CBS, GOT2, CHST1, EXTL2, GCLM, YARS1), was constructed based on regression analysis. The robustness of the model was validated using multiple methods. The high-risk (HR) and low-risk (LR) groups had varying degrees of immune infiltration, according to an immunology-related study. Of these, B cells and Type_II_IFN_Response were greatly infiltrated in the LR group, whereas aCDs, Macrophages, and Treg were heavily infiltrated in the HR group (p < 0.05). Because of higher immunophenoscore, the low-risk group could benefit from immunotherapy more. Drug sensitivity predictions indicated that people with high CBS expression and low GOT2 and ISCU expression may benefit more from treatment with SCH-772984, Pimasertib, Cobimetinib (isomer1), TAK-733, LY-3214996, ARRY-162, Cladribine, Fludarabine, and Hydroxyurea.

Conclusion: This work created a 12-gene signature based on NM, preliminary investigated immune infiltration in two risk categories, and discovered some possible anti-tumor medications. To sum up, our study findings offer fresh perspectives on the roles played by NM-associated genes in HCC development, prognosis, immunological response, and medication screening.