Irbm最新文献

Objectives

Satisfactory biomechanical compatibility of implants is important for obtaining desired tissue repair efficiency. Here, we investigated the combined effects of three important influencing factors, mesh orientation, defect location and size, on biomechanical compatibility of a typical anisotropic mesh by both computational simulation and animal experiment.

Methods

Numerical models of rabbits were developed based on CT images and material constitutive models obtained by uniaxial tests, during which two orientations, two defect locations and two defect sizes were investigated. Corresponding pneumoperitoneum tests on rabbits and non-invasive measurements on the displacement of abdominal wall surface were performed for validation.

Results

Numerical results showed that the displacement of abdominal wall was limited when the stiffest direction of mesh was parallel to the cranio-caudal direction, but the stress in suture area was greatly reduced. When the defect was located at the junction of different muscles, the strain distribution became uneven. In addition, for the defects with smaller size, difference between the results caused by different mesh orientations was smaller. Animal experimental results were in good agreement with the numerical results. Further simulations for a hypothetical mesh orientation showed that the meshes exhibited better biomechanical compatibility when their stiffest direction was consistent with that of oblique muscles for all four different defects.

Conclusion

The mesh orientation was the most influential factor and the proper orientation of the mesh was not necessarily consistent with the anisotropy of the defect tissue. In addition, the mesh design with asymmetric stiffness should be considered for defects at the junction of different tissues. Finally, it is possible to align the stiffest direction of the mesh with that of the defect tissue in repairing small defects to achieve better compliance. Our findings could provide some reliable and instructive guidelines for high-performance anisotropic meshes development and their appropriate selection and placement in surgery. And methods proposed in this study could be used to comprehensively and instructively evaluate the biomechanical compatibility of hernia meshes, predict their repair effect, and determine their appropriate positioning before they are put into clinical use.

Objectives

Breast cancer is a common but deadly disease among women. Medical imaging is an effective method to diagnose breast cancer, but manual image screening is time-consuming. In this study, a novel computer-aided diagnosis system for breast cancer detection called BCDNet is proposed.

Material and Methods

We leverage pre-trained convolutional neural networks (CNNs) for representation learning and propose an adaptive backbone selection algorithm to obtain the best CNN model. An extreme learning machine serves as the classifier in the BCDNet, and a bat algorithm with chaotic maps is put forward to further optimize the parameters in the classifiers. A public ultrasound image dataset is used in the experiments based on 5-fold cross-validation.

Results

Simulation results suggest that our BCDNet outperforms several state-of-the-art breast cancer detection methods in terms of accuracy.

Conclusion

The proposed BCDNet is a useful auxiliary tool that can be applied in clinical screening for breast cancer.

Objectives

Heart rate variability (HRV) is a valuable indicator of both physiological and psychological states. However, the accuracy of HRV measurements taken by wearable devices can be compromised by errors during transmission and acquisition. These errors can significantly affect HRV features and are not acceptable for precise HRV analysis used for medical diagnosis. This study aims to address this issue by investigating the effectiveness of four different interpolation methods (Nearest Neighbour - NN, Linear, Shape-preserving piecewise cubic Hermite - Pchip, and cubic spline) in tackling missing RR values in real-time HRV analysis.

Materials and Methods

In this study, HRV signals were obtained from Electrocardiograms (ECG) through automatic detection and manually corrected by a specialist, resulting in high-quality signals with no missing or ectopic peaks. To simulate low-quality data acquisition, values were iteratively deleted from each HRV analysis window. The deleted values were then replaced using four different interpolation methods. Time and frequency domain features were computed from both the original and reconstructed signals, and the Mean Absolute Percentage Error (MAPE) was used to compare these features.

Results

Results showed that as the percentage of missing values increased, some interpolation methods were more suitable for RR time-series with a greater number of missing data. Furthermore, the study suggests that the impact of interpolation on HRV features varied across different features and that SDNN is the least affected by interpolation. In the time domain, nearest neighbour interpolation gives the best results for up to 50% missing data. Beyond this threshold, it seems better not to use any interpolation for RMSSD. In the frequency domain however, the lowest errors of HRV feature estimation are obtained using linear or Pchip interpolation. To achieve maximum performance, it is recommended to adapt the interpolation method to both the percentage of missing values and the targeted HRV feature.

Conclusion

Results highlight the importance of choosing the appropriate interpolation method to accurately estimate HRV features in real-time analysis. Overall, the Pchip interpolation seems to yield the best results on most HRV features as it preserves the linear trend of the data while adding very light waves. The findings can be beneficial in the development of more precise and reliable wearable devices for real-time HRV monitoring.

Objectives

Accurate automatic liver segmentation has important value for subsequent tumor segmentation, diagnosis, and treatment. In this paper, a Multiscale Cascaded Feature Attention U-Net (MCFA-UNet) neural network model was proposed to solve the problem of edge detail feature loss caused by insufficient feature extraction in existing segmentation methods.

Material and methods

MCFA-UNet is a 3D segmentation network based on U-Net encoding and decoding structure. First, this paper proposes a multiscale feature cascaded attention (MCFA) module, which extracts multiscale feature information through multiple continuous convolution paths, and uses double attention to realize multiscale feature information fusion of different paths. Second, the attention-gate mechanism is used to fuse different levels of feature information, which reduces the semantic difference between coding and decoding paths. Finally, the deep supervision learning method was employed to optimize the network segmentation effect through the feature information of each hidden layer in the decoding path.

Results

MCFA-UNet was evaluated on LiTS and 3DIRCADb datasets. The Dice scores of 0.955 and 0.981 are obtained respectively. Compared with the baseline network, the segmentation accuracy is improved by 5% and 3.5%.

Conclusion

Experimental results show that MCFA-UNet has more accurate segmentation performance than baseline model and other advanced methods.

Objective

The purpose of this research article is to present the functionalization of a new titanium alloy of the system TiNbZr, by the grafting of a bioactive polymer (poly(sodium styrene sulfonate), PNaSS) using the “grafting from” technique to improve the osseointegration. The resulting grafted polymer is covalently bonded to the substrate in this procedure thanks to surface-induced polymerization.

Material and Method

Colorimetric assay, Fourier-transform infrared spectra recorded in attenuated total reflection mode (ATR-FTIR), Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and water contact angle measurements (WCA) were applied to characterize the surfaces. In addition, the effect of the grafting on the biological response was assessed using MC3T3-E1 pre-osteoblast cells line.

Results

This study showed that grafting rates obtained on these new alloy are as good (around 4.5 μg/cm²) as on classical alloys. In parallel, in vitro biological response study was carried out to assess toxicity, cell viability, and morphology on titanium alloys TiNbZr functionalized. Moreover, results showed superior alkaline phosphatase activity and higher calcium deposition on grafted samples, implying a beneficial effect of the PNaSS in osteoinduction activity.

Conclusions

Grafted TiNbZr improves the cell response, in particular, the osseointegration.

Objectives Background

Social isolation is probably one of the most affected health outcomes in the elderly people, particularly those living alone, due to the COVID-19 pandemic. Therefore, we try to identify it by detecting changes in the elderly such as malnutrition and lack of mobility.

Material and methods

The system consists of two types of sensors installed at various locations in the user's home: Passive infrared (PIR) sensors and reed switch sensors. It was implemented for 15 days in the home of a 26-year-old student living alone, as a first step to later be deployed in the home of elderly people.

Results

Our study showed strong similarities between the activities detected by the algorithm and the real activity pattern of the interviewed individual. In addition, the system was able to identify two daily patterns (weekday and weekend) of the person as he is a student and is present in class during the week.

Conclusion

A system composed of low-cost, unobtrusive, non-intrusive and miniaturized sensors is able to detect meal-taking activity and mobility. These results are an intermediate step in assessing the potential risk of social isolation in older people living alone based on these ADLs.

Background

The recognition of lower limb movement has a wide range of applications in rehabilitation training, wearable exoskeleton control, and human activity monitoring. Surface electromyography (sEMG) signals can directly reflect the intention of human movement and can be used as the source of lower limb movement recognition. Literature reports have shown that extracting features from sEMG signals is the core of human movement recognition based on sEMG signals. However, how to effectively extract features from the sEMG signal of the lower limbs affected by body gravity is a difficult problem for the recognition of lower limb movement based on the sEMG signal.

Objectives

The main objective of this paper is to propose an efficient lower limb movement recognition model based on sEMG signals to accurately recognize the four lower limb movements.

Methods and results

We proposed a novel method of lower limbs movements recognition based on tunable Q-factor wavelet transform (TQWT) and Kraskov entropy (KrEn). Firstly, the sEMG signals of four different lower limb movements from twenty subjects were recorded by seven wearable sEMG signal sensors, and the recorded sEMG signals were denoised by multi-scale principal component analysis (MSPCA). Then, the denoised sEMG signal is decomposed into multiple sub-band signals by TQWT and the KrEn feature is extracted from each sub-band signal. Next, the representative features are selected from the extracted KrEn features by the minimum redundancy maximum relevance (mRMR) feature selection method. Finally, the four lower limb movements are recognized by three machine learning classifiers. Besides, to improve the recognition performance, a majority voting (MV) technology is proposed for the post-processing of decision flow. Experimental results show that the combination of TQWT, KrEn, and MV technology achieved the average recognition accuracy of 98.42% using the linear discriminant analysis (LDA) classifier.

Conclusion

The method proposed in this paper can recognize lower limb movements with high accuracy. Compared with existing methods, this method is more advanced and accurate, indicating that it has great application potential in rehabilitation training, wearable exoskeleton control, and daily activity monitoring.

Background

Spinal degeneration with age is commonly accompanied by excessive kyphosis and low-back pain, however, little is known about the connection between lumbar sagittal morphology and its degenerative biomechanics. This study investigates the biomechanical response of four Roussouly's sagittal alignment lumbar to degeneration of various parts of the intervertebral disc (IVD) based on threedimensional finite element (FE) models.

Methods

Using Roussouly's type parametric FE models, material properties of the degenerate nucleus populous (NP), annulus fibrosis matrix (AFM), and collagen fibers were assumed to be half of the intact IVD. A follower preload and vertical force were applied to simulate physical standing posture.

Results

the reduced strength of the NP and AFM led to the increase of lumbar anteflexion, while the fiber mechanical properties have little effect on it. When facing IVD degeneration, Type 1 lumbar showed increased intradiscal pressures (IDPs) and fiber stress at the L1-2 and L4-S1 segments. Type 2 lumbar exhibited the highest lumbar anteflexion and pelvic rearward rotation, as well as increased IDPs among the models. Type 3 lumbar had the best biomechanical stability. Type 4 lumbar showed the higher AFM stress but the lower IDPs among the four types.

Conclusions

IVD degeneration generated sagittal imbalance by increasing lumbar anteflexion movement (i.e., loss lordosis) and pelvic rearback rotation. The biomechanical response of the four Roussouly's lumbar types differed in intervertebral rotation and stress distribution.

Objective

Interjoint coordination after stroke is affected, which limits the use of the upper limb. Current methods to determine interjoint coordination lack the ability to visualize and quantify the movement. Therefore we investigated if the coupling angle can be used to visualize and interpret upper limb interjoint coordination following a stroke.

Methods

Seven chronic stroke patients trained six weeks with an assistive home-training system (MERLIN). Kinematic outcomes, i.e. elbow and shoulder range of motion, movement duration, and angle-angle plots were determined in a retrieving task. Interjoint coordination between elbow flexion and shoulder abduction angles was expressed as the coupling angle phases and the number of phase transitions: proximal/distal joint leading phase, in-phase and anti-phase. Comparisons were made within sides: pre-test versus post-test, and between sides: most-affected (MA) versus least-affected (LA).

Results

Smaller elbow flexion angles were found PreMA versus PreLA, and smaller shoulder abduction angles in PostMA versus PostLA. A general coordination pattern was revealed on the LA side, but not on the MA side. A trend showed less phase transitions at the MA side after training, suggesting a smoother movement. Quantification of the movement phases indicated more involvement of the shoulder joint involvement in the MA side during pre-test. After training, these differences were not apparent, which might reveal an increased independent control of the elbow joint.

Conclusions

The coupling angle and the movement phases provide a promising tool to investigate post-stroke interjoint coordination patterns.

Significance

A new visualisation of the interjoint coordination may benefit rehabilitation of stroke survivors.

Registration

This trial was registered at the Netherlands Trial Register (NL7535) https://www.trialregister.nl/trial/7535.

Objectives

Diabetes is a serious, long-term disease and the use of continuous glucose monitoring sensors can reduce reliance on other painful invasive blood testing methods such as the finger blood glucose test. According to our work, a low-cost continuous glucose sensor has been developed based on electrochemical measurement techniques.

Materials

The sensor is based on a two needles system; a gold and a silver electrode are integrated into a circular shaped electronic printed circuit board (PCB). The sensing part is based on biological electrochemical measurements. Glucose oxidase (Gox) was used as the active sensing element and ferrocene (Fc) as a mediator. Simple and low-cost coating methods were used; these methods are self-assembled monolayers and deep coating. This will reduce the final cost of the sensor as no expensive technique was used. The electrical subsystem contains a low-noise and low-power trans-impedance front-end as well as a single-chip low-power Bluetooth microcontroller with a 12-bit Analog-to-Digital Converter (ADC).

Results

The sensor was tested in various concentrations of glucose. As a result of initial in vitro experiments, detailed analytical performance metrics are presented. The device has consistently shown a sensitivity of 3.059 mV/(mg/dl) reading with a linear range of 0-400 mg/dl.

Conclusion

The proposed study shows promising results for glucose detection. Thus, this type of sensor can be used for different analyzes targeting biological applications after further investigations and analysis.