Irbm最新文献

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.

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.

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.

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.

Objectives

The accurate preoperative segmentation of the uterus and uterine fibroids from magnetic resonance images (MRI) is an essential step for diagnosis and real-time ultrasound guidance during high-intensity focused ultrasound (HIFU) surgery. Conventional supervised methods are effective techniques for image segmentation. Recently, semi-supervised segmentation approaches have been reported in the literature. One popular technique for semi-supervised methods is to use pseudo-labels to artificially annotate unlabeled data. However, many existing pseudo-label generations rely on a fixed threshold used to generate a confidence map, regardless of the proportion of unlabeled and labeled data.

Materials and Methods

To address this issue, we propose a novel semi-supervised framework called Confidence-based Threshold Adaptation Network (CTANet) to improve the quality of pseudo-labels. Specifically, we propose an online pseudo-labels method to automatically adjust the threshold, producing high-confident unlabeled annotations and boosting segmentation accuracy. To further improve the network's generalization to fit the diversity of different patients, we design a novel mixup strategy by regularizing the network on each layer in the decoder part and introducing a consistency regularization loss between the outputs of two sub-networks in CTANet.

Results

We compare our method with several state-of-the-art semi-supervised segmentation methods on the same uterine fibroids dataset containing 297 patients. The performance is evaluated by the Dice similarity coefficient, the precision, and the recall. The results show that our method outperforms other semi-supervised learning methods. Moreover, for the same training set, our method approaches the segmentation performance of a fully supervised U-Net (100% annotated data) but using 4 times less annotated data (25% annotated data, 75% unannotated data).

Conclusion

Experimental results are provided to illustrate the effectiveness of the proposed semi-supervised approach. The proposed method can contribute to multi-class segmentation of uterine regions from MRI for HIFU treatment.

Purpose

Three main non-invasive imaging methods are routinely used for the assessment of liver fibrosis and steatosis in patients with nonalcoholic fatty liver disease (NAFLD): the vibration-controlled transient elastography (VCTE) using the FibroScan device, the magnetic resonance imaging (MRI) based on proton density fat fraction (PDFF), and the magnetic resonance elastography (MRE). The purpose of our study is to evaluate the efficiency of the VCTE findings compared to the two others methods, and to analyze the impact of hepatic iron overload on these comparisons.

Methods

A clinical study was performed on 94 patients with NAFLD in the radiology department of ACRIM-Polyclinic Saint-Côme (France). The study also included 17 patients with hemochromatosis, measured from $T{2}^{⁎}$ MRI. The liver tissues of all the patients were evaluated with 1) VCTE (including the controlled attenuation (CAP) and stiffness parameters), 2) MRI (fat fraction parameter), and 3) MRE (stiffness parameter) techniques. The performance of VCTE was assessed by estimating the area under the ROC curve (AUC) for patients without or with hemochromatosis. Spearman's correlation was used for the comparison of VCTE measurements to MRI and MRE.

Results

VCTE-based stiffness and CAP were significantly correlated with PDFF and MRE measurements ($P<0.01$) for the subgroup without hemochromatosis. The correlations failed for the subgroup with hemochromatosis.

Conclusion

VCTE and CAP measurements were not correlated with those from MR PDFF and MRE for patients with hemochromatosis. VCTE, PDFF and MRE modalities don't give concordant results for patients with hemochromatosis.

Objectives

Esophageal cancer is a high occult malignant tumor. Even with good diagnosis and treatment, the 5-year survival rate of esophageal cancer patients is still less than 30%. Considering the influence of clinical characteristics on postoperative esophageal cancer patients, the construction of a neural network model will help improve the poor prognosis of patients in the five years.

Material and methods

In this study, genetic algorithm optimized deep neural network is exploited to the clinical dataset of esophageal cancer. The independent prognostic factors are screened by Relief algorithm and Cox proportional risk regression. FTD prognostic staging system is established to assess the risk level of esophageal cancer patients.

Results

FTD staging system and independent prognostic factors are integrated into the genetic algorithm optimized deep neural network. The Area Under Curve (AUC) of FTD staging system is 0.802. FTD staging system is verified by the Kaplan-Meier survival curve, and the median survival time is divided for different risk grades. The FTD staging system is superior to the TNM stages in the prognosis effect. The AUC of deep neural network optimized by genetic algorithm is 0.91.

Conclusion

The deep neural network optimized by genetic algorithm has good performance in predicting the 5-year survival status of esophageal cancer patients. The FTD staging system has a significant prognostic effect. The FTD staging system and genetic algorithm optimized deep neural network can be successfully availed in clinical diagnosis and treatment.

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.

Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.

Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.

Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.