Search Results
Local delivery to malignant brain tumors: potential biomaterial-based therapeutic/adjuvant strategies
Glioblastoma (GBM) is the most aggressive malignant brain tumor and is associated with a very poor prognosis. The standard treatment for newly diagnosed patients involves total tumor surgical resection (if possible), plus irradiation and adjuvant chemotherapy. Despite treatment, the prognosis is still poor, and the tumor often recurs within two centimeters of the original tumor. A promising approach to improving the efficacy of GBM therapeutics is to utilize biomaterials to deliver them locally at the tumor site. Local delivery to GBM offers several advantages over systemic administration, such as bypassing the blood-brain barrier and increasing the bioavailability of the therapeutic at the tumor site without causing systemic toxicity. Local delivery may also combat tumor recurrence by maintaining sufficient drug concentrations at and surrounding the original tumor area. Herein, we critically appraised the literature on local delivery systems based within the following categories: polymer-based implantable devices, polymeric injectable systems, and hydrogel drug delivery systems. We also discussed the negative effect of hypoxia on treatment strategies and how one might utilize local implantation of oxygen-generating biomaterials as an adjuvant to enhance current therapeutic strategies. © 2021 The Royal Society of Chemistry.
The synergistic effect of chlorotoxin-mApoE in boosting drug-loaded liposomes across the BBB
We designed liposomes dually functionalized with ApoE-derived peptide (mApoE) and chlorotoxin (ClTx) to improve their blood-brain barrier (BBB) crossing. Our results demonstrated the synergistic activity of ClTx-mApoE in boosting doxorubicin-loaded liposomes across the BBB, keeping the anti-tumour activity of the drug loaded: mApoE acts promoting cellular uptake, while ClTx promotes exocytosis of liposomes. © 2019 The Author(s).
More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses
Signal detection in functional magnetic resonance imaging (fMRI) inherently involves the problem of testing a large number of hypotheses. A popular strategy to address this multiplicity is the control of the false discovery rate (FDR). In this work we consider the case where prior knowledge is available to partition the set of all hypotheses into disjoint subsets or families, e. g., by a-priori knowledge on the functionality of certain regions of interest. If the proportion of true null hypotheses differs between families, this structural information can be used to increase statistical power. We propose a two-stage multiple test procedure which first excludes those families from the analysis for which there is no strong evidence for containing true alternatives. We show control of the family-wise error rate at this first stage of testing. Then, at the second stage, we proceed to test the hypotheses within each non-excluded family and obtain asymptotic control of the FDR within each family at this second stage. Our main mathematical result is that this two-stage strategy implies asymptotic control of the FDR with respect to all hypotheses. In simulations we demonstrate the increased power of this new procedure in comparison with established procedures in situations with highly unbalanced families. Finally, we apply the proposed method to simulated and to real fMRI data.
Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis
Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
Order patterns networks (orpan) - A method to estimate time-evolving functional connectivity from multivariate time series
Complex networks provide an excellent framework for studying the function of the human brain activity. Yet estimating functional networks from measured signals is not trivial, especially if the data is non-stationary and noisy as it is often the case with physiological recordings. In this article we propose a method that uses the local rank structure of the data to define functional links in terms of identical rank structures. The method yields temporal sequences of networks which permits to trace the evolution of the functional connectivity during the time course of the observation. We demonstrate the potentials of this approach with model data as well as with experimental data from an electrophysiological study on language processing.
Frequency spectrum recurrence analysis
In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.
Identifying controlling nodes in neuronal networks in different scales
Recent studies have detected hubs in neuronal networks using degree, betweenness centrality, motif and synchronization and revealed the importance of hubs in their structural and functional roles. In addition, the analysis of complex networks in different scales are widely used in physics community. This can provide detailed insights into the intrinsic properties of networks. In this study, we focus on the identification of controlling regions in cortical networks of cats' brain in microscopic, mesoscopic and macroscopic scales, based on single-objective evolutionary computation methods. The problem is investigated by considering two measures of controllability separately. The impact of the number of driver nodes on controllability is revealed and the properties of controlling nodes are shown in a statistical way. Our results show that the statistical properties of the controlling nodes display a concave or convex shape with an increase of the allowed number of controlling nodes, revealing a transition in choosing driver nodes from the areas with a large degree to the areas with a low degree. Interestingly, the community Auditory in cats' brain, which has sparse connections with other communities, plays an important role in controlling the neuronal networks.
The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control
In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results.
Modified wavelet analysis of ECoG-pattern as promising tool for detection of the blood–brain barrier leakage
A new approach for detection oscillatory patterns and estimation of their dynamics based by a modified CWT skeleton method is presented. The method opens up additional perspectives for the analysis of subtle changes in the oscillatory activity of complex nonstationary signals. The method was applied to analyze unique experimental signals obtained in usual conditions and after the non-invasive increase in the blood–brain barrier (BBB) permeability in 10 male Wistar rats. The results of the wavelet-analysis of electrocorticography (ECoG) recorded in a normal physiological state and after an increase in the BBB permeability of animals demonstrate significant changes between these states during wakefulness of animals and an essential smoothing of these differences during sleep. Sleep is closely related to the processes of observed changes in the BBB permeability.