BMC biomedical engineering最新文献

Background: Currently there are no standard models with which to evaluate the biomechanical performance of calcified tissue adhesives, in vivo. We present, herein, a pre-clinical murine distal femoral bone model for evaluating tissue adhesives intended for use in both osseous and osteochondral tissue reconstruction.

Results: Cylindrical cores (diameter (Ø) 2 mm (mm) × 2 mm depth), containing both cancellous and cortical bone, were fractured out from the distal femur and then reattached using one of two tissue adhesives. The adhesiveness of fibrin glue (Tisseel^tm), and a novel, biocompatible, calcium phosphate-based tissue adhesive (OsStic^tm) were evaluated by pullout testing, in which glued cores were extracted and the peak force at failure recorded. The results show that Tisseel weakly bonded the metaphyseal bone cores, while OsStic produced > 30-fold higher mean peak forces at failure (7.64 Newtons (N) vs. 0.21 N). The failure modes were consistently disparate, with Tisseel failing gradually, while OsStic failed abruptly, as would be expected with a calcium-based material. Imaging of the bone/adhesive interface with microcomputed tomography revealed that, for OsStic, failure occurred more often within cancellous bone (75% of tested samples) rather than at the adhesive interface.

Conclusions: Despite the challenges associated with biomechanical testing in small rodent models the preclinical ex-vivo test model presented herein is both sensitive and accurate. It enabled differences in tissue adhesive strength to be quantified even for very small osseous fragments (<Ø4mm). Importantly, this model can easily be scaled to larger animals and adapted to fracture fragment fixation in human bone. The present model is also compatible with other long-term in vivo evaluation methods (i.e. in vivo imaging, histological analysis, etc.).

Background: Advancement in polymer technologies, facilitated predominantly through chemical engineering approaches or through the identification and utilization of novel renewable resources, has been a steady focus of biomaterials research for the past 50 years. Aliphatic polyesters have been exploited in numerous biomedical applications including the formulation of soft-tissue sutures, bone fixation devices, cardiovascular stents etc. Biomimetic 'soft' polymer formulations are of interest in the design of biological interfaces and specifically, in the development of implantable neuroelectrode systems intended to interface with neural tissues. Critically, soft polymer formulations have been shown to address the challenges associated with the disregulation of mechanotransductive processes and micro-motion induced inflammation at the electrode/tissue interface. In this study, a polyester-based poly(ε-decalactone)/silver nanowire (EDL:Ag) composite was investigated as a novel electrically active biomaterial with neural applications.Neural interfaces were formulated through spin coating of a polymer/nanowire formulation onto the surface of a Pt electrode to form a biocompatible EDL matrix supported by a percolated network of silver nanowires. As-formed EDL:Ag composites were characterized by means of infrared spectroscopy, scanning electron microscopy and electrochemical methods, with their cytocompatibility assessed using primary cultures of a mixed neural population obtained from the ventral mesencephalon of Sprague-Dawley rat embryos.

Results: Electrochemical characterization of various EDL:Ag composites indicated EDL:Ag 10:1 as the most favourable formulation, exhibiting high charge storage capacity (8.7 ± 1.0 mC/cm²), charge injection capacity (84.3 ± 1.4 μC/cm²) and low impedance at 1 kHz (194 ± 28 Ω), outperforming both pristine EDL and bare Pt electrodes. The in vitro biological evaluation showed that EDL:Ag supported significant neuron viability in culture and to promote neurite outgrowth, which had the average length of 2300 ± 6 μm following 14 days in culture, 60% longer than pristine EDL and 120% longer than bare Pt control substrates.

Conclusions: EDL:Ag nanocomposites are shown to serve as robust neural interface materials, possessing favourable electrochemical characteristics together with high neural cytocompatibility.

Background: In Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough, compared to the high resolution beamformers such as minimum variance (MV). In this paper, it is proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF.

Results: The higher performance of HRCF is proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91% and 15% and the signal-to-noise ratio about 40% and 14%, respectively.

Conclusion: Proposed method provides a high resolution along with a low level of sidelobes for PAI.

Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires multi-dimensional k-space data through 1-D free induction decay or echo signals. This often limits the use of MRI, especially for high resolution or dynamic imaging. Accordingly, many investigators has developed various acceleration techniques to allow fast MR imaging. For the last two decades, one of the most important breakthroughs in this direction is the introduction of compressed sensing (CS) that allows accurate reconstruction from sparsely sampled k-space data. The recent FDA approval of compressed sensing products for clinical scans clearly reflect the maturity of this technology. Therefore, this paper reviews the basic idea of CS and how this technology have been evolved for various MR imaging problems.

Background: Frailty assessment is a critical approach in assessing the health status of older people. The clinical tools deployed by geriatricians to assess frailty can be grouped into two categories; using a questionnaire-based method or analyzing the physical performance of the subject. In performance analysis, the time taken by a subject to complete a physical task such as walking over a specific distance, typically three meters, is measured. The questionnaire-based method is subjective, and the time-based performance analysis does not necessarily identify the kinematic characteristics of motion and their root causes. However, kinematic characteristics are crucial in measuring the degree of frailty.

Results: The studies reviewed in this paper indicate that the quantitative analysis of activity of daily living, balance and gait are significant methods for assessing frailty in older people. Kinematic parameters (such as gait speed) and sensor-derived parameters are also strong markers of frailty. Seventeen gait parameters are found to be sensitive for discriminating various frailty levels. Gait velocity is the most significant parameter. Short term monitoring of daily activities is a more significant method for frailty assessment than is long term monitoring and can be implemented easily using clinical tests such as sit to stand or stand to sit. The risk of fall can be considered an outcome of frailty.

Conclusion: Frailty is a multi-dimensional phenomenon that is defined by various domains; physical, social, psychological and environmental. The physical domain has proven to be essential in the objective determination of the degree of frailty in older people. The deployment of inertial sensor in clinical tests is an effective method for the objective assessment of frailty.

Background: Long double-stranded RNAs (dsRNAs) are duplex RNAs that can induce immune response when present in mammalian cells. These RNAs are historically associated with viral replication, but recent evidence suggests that human cells naturally encode endogenous dsRNAs that can regulate antiviral machineries in cellular contexts beyond immune response.

Results: In this study, we use photochromic organic compound spiropyran to profile and quantitate dsRNA expression. We show that the open form of spiropyran, merocyanine, can intercalate between RNA base pairs, which leads to protonation and alteration in the spectral property of the compound. By quantifying the spectral change, we can detect and quantify dsRNA expression level, both synthetic and cellular. We further demonstrate that spiropyrans can be used as a molecular diagnostic tool to profile endogenously expressed dsRNAs. Particularly, we show that spiropyrans can robustly detect elevated dsRNA levels when colorectal cancer cells are treated with 5-aza-2'-deoxycytidine, an FDA-approved DNA-demethylating agent used for chemotherapy, thus demonstrating the use of spiropyran for predicting responsiveness to the drug treatment.

Conclusion: As dsRNAs are signature of virus and accumulation of dsRNAs is implicated in various degenerative disease, our work establishes potential application of spiropyrans as a simple spectral tool to diagnose human disease based on dsRNA expression.

Adhesions represent a major burden in clinical practice, particularly following abdominal, intrauterine, pericardial and tendon surgical procedures. Adhesions are initiated by a disruption in the epithelial or mesothelial layer of tissue, which leads to fibrin adhesion sites due to the downregulation of fibrinolytic activity and an increase in fibrin deposition. Hence, the metabolic events involved in tissue healing, coagulation, inflammation, fibrinolysis and angiogenesis play a pivotal role in adhesion formation. Understanding these events, their interactions and their influence on the development of post-surgical adhesion is crucial for the development of effective therapies to prevent them. Mechanical barriers, antiadhesive agents and combination thereof are customarily used in the battle against adhesions. Although these systems seem to be effective at reducing adhesions in clinical procedures, their prevention remains still elusive, imposing the need for new antiadhesive strategies.

Background: Corneal endothelium (CE) images provide valuable clinical information regarding the health state of the cornea. Computation of the clinical morphometric parameters requires the segmentation of endothelial cell images. Current techniques to image the endothelium in vivo deliver low quality images, which makes automatic segmentation a complicated task. Here, we present two convolutional neural networks (CNN) to segment CE images: a global fully convolutional approach based on U-net, and a local sliding-window network (SW-net). We propose to use probabilistic labels instead of binary, we evaluate a preprocessing method to enhance the contrast of images, and we introduce a postprocessing method based on Fourier analysis and watershed to convert the CNN output images into the final cell segmentation. Both methods are applied to 50 images acquired with an SP-1P Topcon specular microscope. Estimates are compared against a manual delineation made by a trained observer.

Results: U-net (AUC=0.9938) yields slightly sharper, clearer images than SW-net (AUC=0.9921). After postprocessing, U-net obtains a DICE=0.981 and a MHD=0.22 (modified Hausdorff distance), whereas SW-net yields a DICE=0.978 and a MHD=0.30. U-net generates a wrong cell segmentation in only 0.48% of the cells, versus 0.92% for the SW-net. U-net achieves statistically significant better precision and accuracy than both, Topcon and SW-net, for the estimates of three clinical parameters: cell density (ECD), polymegethism (CV), and pleomorphism (HEX). The mean relative error in U-net for the parameters is 0.4% in ECD, 2.8% in CV, and 1.3% in HEX. The computation time to segment an image and estimate the parameters is barely a few seconds.

Conclusions: Both methods presented here provide a statistically significant improvement over the state of the art. U-net has reached the smallest error rate. We suggest a segmentation refinement based on our previous work to further improve the performance.

This editorial accompanies the launch of BMC Biomedical Engineering, a new open access, peer-reviewed journal within the BMC series, which seeks to publish articles on all aspects of biomedical engineering. As one of the first engineering journals within the BMC series portfolio, it will support and complement existing biomedical communities, but at the same time, it will provide an open access home for engineering research. By publishing original research, methodology, database, software and review articles, BMC Biomedical Engineering will disseminate quality research, with a focus on studies that further the understanding of human disease and that contribute towards the improvement of human health.

Background: In neurology and rehabilitation the primary interest for using wearables is to supplement traditional patient assessment and monitoring in hospital settings with continuous data collection at home and in community settings. The aim of this project was to develop a novel wearable garment with integrated sensors designed for continuous monitoring of physiological and movement related variables to evaluate progression, tailor treatments and improve diagnosis in epilepsy, Parkinson's disease and stroke. In this paper the early development and evaluation of a prototype designed to monitor movements and heart rate is described. An iterative development process and evaluation of an upper body garment with integrated sensors included: identification of user needs, specification of technical and garment requirements, garment development and production as well as evaluation of garment design, functionality and usability. The project is a multidisciplinary collaboration with experts from medical, engineering, textile, and material science within the wearITmed consortium. The work was organized in regular meetings, task groups and hands-on workshops. User needs were identified using results from a mixed-methods systematic review, a focus group study and expert groups. Usability was evaluated in 19 individuals (13 controls, 6 patients with Parkinson's disease) using semi-structured interviews and qualitative content analysis.

Results: The garment was well accepted by the users regarding design and comfort, although the users were cautious about the technology and suggested improvements. All electronic components passed a washability test. The most robust data was obtained from accelerometer and gyroscope sensors while the electrodes for heart rate registration were sensitive to motion artefacts. The algorithm development within the wearITmed consortium has shown promising results.

Conclusions: The prototype was accepted by the users. Technical improvements are needed, but preliminary data indicate that the garment has potential to be used as a tool for diagnosis and treatment selection and could provide added value for monitoring seizures in epilepsy, fluctuations in PD and activity levels in stroke. Future work aims to improve the prototype further, develop algorithms, and evaluate the functionality and usability in targeted patient groups. The potential of incorporating blood pressure and heart-rate variability monitoring will also be explored.