Critical Reviews in Biomedical Engineering最新文献

It is hypothesized that bone cells can sense mechanical force in the extracellular network via an electrical signal. This has led to the use of electrical stimulation (ES) to improve fracture repair and mitigate bone loss. Although overlap exists in bone maintenance and fracture healing mechanics, the processes involved in both are very different, resulting in dissimilar behaviors from the cells. Osteocytes are the most abundant cell type in bone tissue, and their basic structure and lineage are fairly well understood, but much debate is present regarding their behavior, with even less known about their behavior in electrical environments. A wide range of research exists on cell behavior under different types of ES, but it is difficult to draw conclusions due to the large variance in stimulation parameters, cell types, and origins (locations and species). By exploring behavior of multiple bone-cell types under different forms of ES, as well as mechanical stimulation through fluid flow, we can determine more about cell reactions to stimuli. In turn, a better understanding of cell response has the potential to improve and broaden therapeutic applications of ES for bone healing and bone loss mitigation, and enhance outcomes for osseointegration into implantable medical devices. These require greater understanding of the bone cellular environment from an electrical perspective as well as cellular responses to ES.

Altered cellular mechano-transduction and biochemistry lead to degeneration of articular cartilage in people with knee osteoarthritis. However, the influence of low-moderate exposure to weight-bearing activity such as squatting on cartilage metabolism has not been adequately studied. The current study explored associations between knee adduction moment (KAM) during walking, biochemical markers and daily squat exposure. 3D gait analysis was used to determine external loads acting on the knee as indicators of joint compressive forces whereas biomarkers-Urine type-II-collagen-telopeptide (uCTxII), antioxidant and phospholipase A2 (PLA2) activity reflected on articular cartilage status. Following ethical approval, 66 participants with varying daily squat exposure (non-squatters [n = 21, exposure = 0 min]; activity of daily living [ADL] squatters [n = 16, exposure = 34 min]; occupational squatters [n = 13, exposure = 102 min]) and people with grade 2-3 knee osteoarthritis (n = 16, exposure = 28 min) were evaluated using 3D gait and biomarker analysis. The PLA2 activity was lowest in ADL squatters while occupational squatters demonstrated highest activity (p < 0.05). KAM and urine biomarker were similar among the groups. Moderate-strong positive association was observed between sweat PLA2 activity and age (r = 0.819, p = 0.004), daily squat exposure and biomarker uCTxII (r = 0.604, p = 0.013), antioxidant activity and Right-KAM (r = -0.917, p = 0.001), and Left-KAM (r = -0.767, p = 0.016), in people with knee OA. Healthy people demonstrated weak positive associations between KAM, uCTxII, and BMI. Associations between non-invasive biomechanical and biochemical markers indicate their potential use to identify early knee osteoarthritis. Studies with larger sample size are necessary to support prescription of body weight joint loading activities such as squatting in moderation, to delay functional decline caused by knee OA.

Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally high-grade tumors such as gliomas require quick and precise diagnosis and medical intervention due to their infiltrative and fast-spreading nature. Therefore, they require computer assistance instead of manual methods. Deep learning (DL) methods are currently on the rise and have become an active field of research in several domains varying from stock market analysis to deep space object detection. They have very promising potential in brain tumor feature extraction from MRIs. Convolutional neural network (CNN) architectures, one of the most influential families of DL algorithms, have undergone a profound transformation since their first successes. This has led to increasing feature extraction quality and algorithm generalizability over various brain tumor types and grades. This review paper presents an explanatory and comparative survey on MRI-based brain tumor image segmentation. First, it provides the survey background and the typical process chain for brain MRI segmentation using CNNs. Second, it details the typical CNN architecture structure and its advantages over other machine learning algorithms. CNN architectures proposed for this purpose are enumerated and classified corresponding to their complexity, and then compared using specific metrics that consider the datasets they use.

Titanium dioxide nanotubes combine the geometrical properties of a tubular structure with the physico-chemical properties of TiO2. The nanotubes improve the surface characteristics of a material such as titanium, which possesses high mechanical resistance, and low density, enhancing its use for biomedical devices. The nanotubular layer increases the device's interaction with cells. In this paper, we discuss various aspects of the anodizing technique to obtain ordered nanotubes and careful control of the process parameters to obtain highly ordered TiO2 nanotubes. Also, we review the biological activity of TiO2 nanotubes, the effect of nanotube size on bioactivity, and the antibacterial effect of TiO2 nanotubes without doping. Finally, novel applications of TiO2 nanotubes employed as a biomaterial are discussed.

One aspect of special concern in endodontics is the effect of irrigating solutions on the biomechanical properties of dentine. A systematic review of in vitro studies was conducted to analyze and systematize the effect of endodontic irrigating solutions on biomechanical properties in noninstrumented dentine, according to published in vitro studies. A literature review was conducted on different databases including papers from 2009 to 2019. Two researchers identified in vitro studies on permanent teeth root dentine that reported control group, featured nonmechanical preparation, and sample size ≥ 10. An instrument was designed for bias assessment in three categories (High-Uncertain-Low), applying 17 criteria. Using the PRISMA tool, an electronic search found 9,026 titles. From these, 28 were subjected to full-text analysis and 9 were chosen for qualitative analysis. It was identified that chelates decrease microhardness and stiffness. It was also inferred that the proteolytic effect of NaOCl reduces the elasticity modulus and flexural strength. The heterogeneity analysis, with a value I2:92% for microhardness and I2:81% for roughness, revealed high heterogeneity among the included studies. The random effect model identified with 95% confidence that NaOCl and EDTA significantly decrease microhardness: -3.00[-4.22, -1.78]; EDTA 17% at 15 min being on average the lowest value: -6.66[-8.32, -5.00]. For roughness, all the proposed solutions increased significantly: 2.37[1.67, 3.08]; the highest, 3.94[2.84, 5.04], was recorded by NaOCl2.5%-15 min. In contrast, CLX registered a high roughness value: 3.33[1.88, 4.77]. Dentinal microhardness reduction associated to chelates is a concentration and time- dependent variable. Collagen degradation by NaOCl is a time and concentration-dependent variable.