Advances in nano-scaled biosensors for biomedical applications

Recently, a growing amount of attention has been focused on the utility of biosensors for biomedical applications. Combined with nanomaterials and nanostructures, nano-scaled biosensors are installed for biomedical applications, such as pathogenic bacteria monitoring, virus recognition, disease biomarker detection, among others. These nano-biosensors offer a number of advantages and in many respects are ideally suited to biomedical applications, which could be made as extremely flexible devices, allowing biomedical analysis with speediness, excellent selectivity and high sensitivity. This minireview discusses the literature published in the latest years on the advances in biomedical applications of nano-scaled biosensors for disease bio-marking and detection, especially in bio-imaging and the diagnosis of pathological cells and viruses, monitoring pathogenic bacteria, thus providing insight into the future prospects of biosensors in relevant clinical applications.

Normalized wall index specific and MRI-based stress analysis of atherosclerotic carotid plaques: A study comparing acutely symptomatic and asymptomatic patients

Background: Biomechanical stresses play an important role in determining plaque stability. Quantification of these simulated stresses can be potentially used to assess plaque vulnerability and differentiate different patient groups. Methods and Results: 54 asymptomatic and 45 acutely symptomatic patients underwent in vivo multicontrast magnetic resonance imaging (MRI) of the carotid arteries. Plaque geometry used for finite element analysis was derived from in vivo MRI at the sites of maximum and minimum plaque burden. In total, 198 slices were used for the computational simulations. A pre-shrink technique was used to refine the simulation. Maximum principle stress at the vulnerable plaque sites (ie, critical stress) was extracted for the selected slices and a comparison was performed between the 2 groups. Critical stress in the slice with maximum plaque burden is significantly higher in acutely symptomatic patients as compared to asymptomatic patients (median, inter quartile range: 198.0 kPa (119.8-359.0 kPa) vs 138.4 kPa (83.8-242.6 kPa), P=0.04). No significant difference was found in the slice with minimum plaque burden between the 2 groups (196.7 kPa (133.3-282.7 kPa) vs 182.4 kPa (117.2-310.6 kPa), P=0.82). Conclusions: Acutely symptomatic carotid plaques have significantly high biomechanical stresses than asymptomatic plaques. This might be potentially useful for establishing a biomechanical risk stratification criteria based on plaque burden in future studies.

Stress analysis of carotid plaque based on in vivo MRI of acute symptomatic and asymptomatic patients

Stress analysis within carotid plaques based on in vivo MR imaging has shown to be useful for the identification of vulnerable atheroma. This study is to investigate whether magnetic resonance imaging (MRI) based-biomechanical stress analysis of carotid plaques can differentiate acute symptomatic and asymptomatic patients. 54 asymptomatic and 45 acute symptomatic patients underwent in vivo multi-contrast MRI of the carotid arteries. Plaque geometry used for finite element analysis was derived from in vivo MR images at the site of maximum and minimum plaque burden. In total 198 slices were used for the computational simulations. A pre shrink technique was used to refine the simulation. Maximum principle stress at the vulnerable plaque sites (i.e. critical stress) was extracted for the selected slices and a comparison was performed between the two groups. Critical stress at the site of maximum plaque burden is significantly higher in acute symptomatic patients as compared to asymptomatic patients [median: 198.0kPa (inter quartile range (IQR) = (119.8 - 359.0) vs. 138.4kPa (83.8, 242.6), p=0.04]. No significant difference was found at the minimum plaque burden site between the two groups [196.7kPa (133.3- 282.7) vs. 182.4kPa (117.2 - 310. 6), p=0.82). Stress analysis at the site of maximal plaque burden can be effectively used for differentiating acute symptomatic carotid plaques from asymptomatic plaques. This maybe potentially used for development of biomechanical risk stratification criteria based on plaque burden in future studies.

The hemodynamic effects of in-tandem carotid artery stenosis: Implications for carotid endarterectomy

Objectives: It remains controversial whether patients with severe disease of the internal carotid artery and a coexisting stenotic lesion downstream would benefit from a carotid endarterectomy (CEA) of the proximal lesion. The aim of this study was to simulate the hemodynamic and wall shear effects of in-tandem internal carotid artery stenosis using a computational fluid dynamic (CFD) idealized model to give insight into the possible consequences of CEA on these lesions. Methods: A CFD model of steady viscous flow in a rigid tube with two asymmetric stenoses was introduced to simulate blood flow in arteries with multiple constrictions. The effect of varying the distance between the two stenoses, and the severity of the upstream stenosis on the pressure and wall shear stress (WSS) distributions on the second plaque, was investigated. The influence of the relative positions of the two stenoses was also assessed. Results: The distance between the plaques was found to have minimal influence on the overall hemodynamic effect except for the presence of a zone of low WSS (range -20 to 30 dyne/cm2) adjacent to both lesions when the two stenoses were sufficiently close (<4 times the arterial diameter). The upstream stenosis was protective if it was larger than the downstream stenosis. The relative positions of the stenoses were found to influence the WSS but not the pressure distribution. Conclusions: The geometry and positions of the lesions need to be considered when considering the hemodynamic effects of an in-tandem stenosis. Low WSS is thought to cause endothelial dysfunction and initiate atheroma formation. The fact that there was a flow recirculation zone with low WSS in between the two stenoses may demonstrate how two closely positioned plaques may merge into one larger lesion. Decision making for CEA may need to take into account the hemodynamic situation when an in-tandem stenosis is found. CFD may aid in the risk stratification of patients with this problem.

Does the size of subsamples taken from multispecies trawl catches affect estimates of catch composition and abundance?

Although subsampling is a common method for describing the composition of large and diverse trawl catches, the accuracy of these techniques is often unknown. We determined the sampling errors generated from estimating the percentage of the total number of species recorded in catches, as well as the abundance of each species, at each increase in the proportion of the sorted catch. We completely partitioned twenty prawn trawl catches from tropical northern Australia into subsamples of about 10 kg each. All subsamples were then sorted, and species numbers recorded. Catch weights ranged from 71 to 445 kg, and the number of fish species in trawls ranged from 60 to 138, and invertebrate species from 18 to 63. Almost 70% of the species recorded in catches were "rare" in subsamples (less than one individual per 10 kg subsample or less than one in every 389 individuals). A matrix was used to show the increase in the total number of species that were recorded in each catch as the percentage of the sorted catch increased. Simulation modelling showed that sorting small subsamples (about 10% of catch weights) identified about 50% of the total number of species caught in a trawl. Larger subsamples (50% of catch weight on average) identified about 80% of the total species caught in a trawl. The accuracy of estimating the abundance of each species also increased with increasing subsample size. For the "rare" species, sampling error was around 80% after sorting 10% of catch weight and was just less than 50% after 40% of catch weight had been sorted. For the "abundant" species (five or more individuals per 10 kg subsample or five or more in every 389 individuals), sampling error was around 25% after sorting 10% of catch weight, but was reduced to around 10% after 40% of catch weight had been sorted.