CXCR4-targeted and MMP-responsive iron oxide nanoparticles for enhanced magnetic resonance imaging

MRI offers high spatial resolution with excellent tissue penetration but it has limited sensitivity and the commonly administered contrast agents lack specificity. In this study, two sets of iron oxide nanoparticles (IONPs) were synthesized that were designed to selectively undergo copper-free click conjugation upon sensing of matrix metalloproteinase (MMP) enzymes, thereby leading to a self-assembled superparamagnetic nanocluster network with T2 signal enhancement properties. For this purpose, IONPs with bioorthogonal azide and alkyne surfaces masked by polyethylene glycol (PEG) layers tethered to CXCR4-targeted peptide ligands were synthesized and characterized. The IONPs were tested in vitro and T2 signal enhancements of around 160 % were measured when the IONPs were incubated with cells expressing MMP2/9 and CXCR4. Simultaneous systemic administration of the bioorthogonal IONPs in tumor-bearing mice demonstrated the signal-enhancing ability of these ‘smart’ self-assembling nanomaterials

Arachnoid cysts: the role of the BLADE technique

Background: This study aims at demonstrating the ability of BLADE sequences to reduce or even eliminate all the image artifacts as well as verifying the significance of using this technique in certain pathological conditions

Reverse classification accuracy: predicting segmentation performance in the absence of ground truth

When integrating computational tools such as au- tomatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross-validation because validation data is often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared to a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as part of large-scale image analysis studies

Analysis of a transmission mode scanning microwave microscope for subsurface imaging at the nanoscale

We present a comprehensive analysis of the imaging characteristics of a scanning microwave microscopy (SMM) system operated in the transmission mode. In particular, we use rigorous three-dimensional finite-element simulations to investigate the effect of varying the permittivity and depth of sub-surface constituents of samples, on the scattering parameters of probes made of a metallic nano-tip attached to a cantilever. Our results prove that one can achieve enhanced imaging sensitivity in the transmission mode SMM (TM-SMM) configuration, from twofold to as much as 5× increase, as compared to that attainable in the widely used reflection mode SMM operation. In addition, we demonstrate that the phase of the S-parameter is much more sensitive to changes of the system parameters as compared to its magnitude, the scattering parameters being affected the most by variations in the conductivity of the substrate. Our analysis is validated by a good qualitative agreement between our modeling results and experimental data. These results suggest that TM-SMM systems can be used as highly efficient imaging tools with new functionalities, findings which could have important implications to the development of improved experimental imaging techniques

Nine Months of Hybrid Intradialytic Exercise Training Improves Ejection Fraction and Cardiac Autonomic Nervous System Activity.

Cardiovascular disease is the most common cause of death in hemodialysis (HD) patients. Intradialytic aerobic exercise training has a beneficial effect on cardiovascular system function and reduces mortality in HD patients. However, the impact of other forms of exercise on the cardiovascular system, such as hybrid exercise, is not clear. Briefly, hybrid exercise combines aerobic and strength training in the same session. The present study examined whether hybrid intradialytic exercise has long-term benefits on left ventricular function and structure and the autonomous nervous system in HD patients. In this single-group design, efficacy-based intervention, twelve stable HD patients (10M/2F, 56 ± 19 years) participated in a nine-month-long hybrid intradialytic training program. Both echocardiographic assessments of left ventricular function and structure and heart rate variability (HRV) were assessed pre, during and after the end of the HD session at baseline and after the nine-month intervention. Ejection Fraction (EF), both assessed before and at the end of the HD session, appeared to be significantly improved after the intervention period compared to the baseline values (48.7 ± 11.1 vs. 58.8 ± 6.5, p = 0.046 and 50.0 ± 13.4 vs. 56.1 ± 3.4, p = 0.054 respectively). Regarding HRV assessment, hybrid exercise training increased LF and decreased HF (p p > 0.05). In conclusion, long-term intradialytic hybrid exercise training was an effective non-pharmacological approach to improving EF and the cardiac autonomous nervous system in HD patients. Such exercise training programs could be incorporated into HD units to improve the patients' cardiovascular health

Evidence of Increased Muscle Atrophy and Impaired Quality of Life Parameters in Patients with Uremic Restless Legs Syndrome

BACKGROUND: Restless Legs Syndrome is a very common disorder in hemodialysis patients. Restless Legs Syndrome negatively affects quality of life; however it is not clear whether this is due to mental or physical parameters and whether an association exists between the syndrome and parameters affecting survival. METHOD#ENTITYSTARTX003BF;LOGY/PRINCIPAL FINDINGS: Using the Restless Legs Syndrome criteria and the presence of Periodic Limb Movements in Sleep (PLMS/h >15), 70 clinically stable hemodialysis patients were assessed and divided into the RLS (n = 30) and non-RLS (n = 40) groups. Physical performance was evaluated by a battery of tests: body composition by dual energy X ray absorptiometry, muscle size and composition by computer tomography, while depression symptoms, perception of sleep quality and quality of life were assessed through validated questionnaires. In this cross sectional analysis, the RLS group showed evidence of thigh muscle atrophy compared to the non-RLS group. Sleep quality and depression score were found to be significantly impaired in the RLS group. The mental component of the quality of life questionnaire appeared significantly diminished in the RLS group, reducing thus the overall quality of life score. In contrast, there were no significant differences between groups in any of the physical performance tests, body and muscle composition. CONCLUSIONS: The low level of quality of life reported by the HD patients with Restless Legs Syndrome seems to be due mainly to mental health and sleep related aspects. Increased evidence of muscle atrophy is also observed in the RLS group and possibly can be attributed to the lack of restorative sleep

Mathematical Modeling of Cortical Neurogenesis Reveals that the Founder Population does not Necessarily Scale with Neurogenic Output

The mammalian cerebral neocortex has a unique structure, composed of layers of different neuron types, interconnected in a stereotyped fashion. While the overall developmental program seems to be conserved, there are divergent developmental factors generating cortical diversity amongst species. In terms of cortical neuronal numbers some of the determining factors are the size of the founder population, the duration of cortical neurogenesis, the proportion of different progenitor types, and the fine-tuned balance between self-renewing and differentiative divisions. We develop a mathematical model of neurogenesis that, accounting for these factors, aims at explaining the high diversity in neuronal numbers found across species. By framing our hypotheses in rigorous mathematical terms, we are able to identify paths of neurogenesis that match experimentally observed patterns in mouse, macaque and human. Additionally, we use our model to identify key parameters that would particularly benefit from accurate experimental investigation. We find that the timing of a switch in favor of symmetric neurogenic divisions produces the highest variation in cortical neuronal numbers. Surprisingly, assuming similar cell cycle lengths in primate 3 progenitors, the increase in cortical neuronal numbers does not reflect a larger size of founder population, a prediction that identified a specific need for experimental quantifications