Shadnaz Asgari

Synthesis and characterization of electrospun polyvinyl alcohol nanofibrous scaffolds modified by blending with chitosan for neural tissue engineering.

Among several attempts to integrate tissue engineering concepts into strategies to repair different parts of the human body, neuronal repair stands as a challenging area due to the complexity of the structure and function of the nervous system and the low efficiency of conventional repair approaches. Herein, electrospun polyvinyl alcohol (PVA)/chitosan nano-fibrous scaffolds have been synthesized with large pore sizes as potential matrices for nervous tissue engineering and repair. PVA fibers were modified through blending with chitosan and porosity of scaffolds was measured at various levels of their depth through an image analysis method. In addition, the structural, physicochemical, biodegradability, and swelling of the chitosan nanofibrous scaffolds were evaluated. The chitosan-containing scaffolds were used for in vitro cell culture in contact with PC12 nerve cells, and they were found to exhibit the most balanced properties to meet the basic required specifications for nerve cells. It could be concluded that addition of chitosan to the PVA scaffolds enhances viability and proliferation of nerve cells, which increases the biocompatibility of the scaffolds. In fact, addition of a small percentage of chitosan to the PVA scaffolds proved to be a promising approach for synthesis of a neural-friendly polymeric blend.

Collaborative sensor networking towards real-time acoustical beamforming in free-space and limited reverberance

Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.Wireless sensor networks have been attracting increasing research interest given the recent advances in microelectronics, array processing, and wireless networking. Consisting of a large collection of small, wireless, low-cost, integrated sensing, computing and communicating nodes capable of performing various demanding collaborative space-time processing tasks, wireless sensor network technology poses various unique design challenges, particularly for real-time operation. We review the approximate maximum-likelihood (AML) method for source localization and direction-of-arrival (DOA) estimation. Then, we consider the use of least-squares method (LS) method applied to DOA bearing crossings to perform source localization. A novel virtual array model applicable to the AML-DOA estimation method is proposed for reverberant scenarios. Details on the wireless acoustical testbed are given. We consider the use of Compaq iPAQ 3760s, which are handheld, battery-powered device normally meant to be used as personal organizers (PDAs), as sensor nodes. The iPAQ provide a reasonable balance of cost, availability, and functionality. It has a build in StrongARM processor, microphone, codec for acoustic acquisition and processing, and a PCMCIA bus for external IEEE 802.11b wireless cards for radio communication. The iPAQs form a distributed sensor network to perform real-time acoustical beamforming. Computational times and associated real-time processing tasks are described. Field measured results for linear, triangular, and square subarrays in free-space and reverberant scenarios are presented. These results show the effective and robust operation of the proposed algorithms and their implementations on a real-time acoustical wireless testbed.

Forecasting ICP Elevation Based on Prescient Changes of Intracranial Pressure Waveform Morphology

Interventions of intracranial pressure (ICP) elevation in neurocritical care is currently delivered only after healthcare professionals notice sustained and significant mean ICP elevation. This paper uses the morphological clustering and analysis of ICP (MOCAIP) algorithm to derive 24 metrics characterizing morphology of ICP pulses and test the hypothesis that preintracranial hypertension (Pre-IH) segments of ICP can be differentiated, using these morphological metrics, from control segments that were not associated with any ICP elevation or at least 1 h prior to ICP elevation. Furthermore, we investigate whether a global optimization algorithm could effectively find the optimal subset of these morphological metrics to achieve better classification performance as compared to using full set of MOCAIP metrics. The results showed that Pre-IH segments, using the optimal subset of metrics found by the differential evolution algorithm, can be differentiated from control segments at a specificity of 99% and sensitivity of 37% for these Pre-IH segments 5 min prior to the ICP elevation. While the sensitivity decreased to 21% for Pre-IH segments, 20 min prior to ICP elevation, the high specificity of 99% was retained. The performance using the full set of MOCAIP metrics was shown inferior to results achieved using the optimal subset of metrics. This paper demonstrated that advanced ICP pulse analysis combined with machine learning could potentially leads to the forecasting of ICP elevation so that a proactive ICP management could be realized based on these accurate forecasts.

Synthesis and characterization of poly(lactic-co-glycolic) acid nanoparticles-loaded chitosan/bioactive glass scaffolds as a localized delivery system in the bone defects.

The functionality of tissue engineering scaffolds can be enhanced by localized delivery of appropriate biological macromolecules incorporated within biodegradable nanoparticles. In this research, chitosan/58S-bioactive glass (58S-BG) containing poly(lactic-co-glycolic) acid (PLGA) nanoparticles has been prepared and then characterized. The effects of further addition of 58S-BG on the structure of scaffolds have been investigated to optimize the characteristics of the scaffolds for bone tissue engineering applications. The results showed that the scaffolds had high porosity with open pores. It was also shown that the porosity decreased with increasing 58S-BG content. Furthermore, the PLGA nanoparticles were homogenously distributed within the scaffolds. According to the obtained results, the nanocomposites could be considered as highly bioactive bone tissue engineering scaffolds with the potential of localized delivery of biological macromolecules.

Cerebral Hemodynamic and Metabolic Effects of Remote Ischemic Preconditioning in Patients with Subarachnoid Hemorrhage

BACKGROUND Remote ischemic preconditioning (RIPC) is a form of endogenous neuroprotection induced by transient, subcritical ischemia in a distant tissue. RIPC effects on cerebral hemodynamics and metabolism have not been explored in humans. This study evaluates hemodynamic and metabolic changes induced by RIPC in patients with aneurysmal subarachnoid hemorrhage (SAH). METHODS Patients underwent three or four RIPC sessions 2-12 days following SAH. Continuous vitals, intracranial pressure (ICP), and transcranial Doppler (TCD) data were collected. Brain microdialysis metabolic changes were monitored. ICP and TCD morphological clustering and analysis of intracranial pulse (MOCAIP) metrics were compared to positive and negative control groups for cerebral vasodilation. RESULTS Seven ICP and six TCD recordings from four patients demonstrated an increase in mean ICP (8-14.57 mmHg, p < 0.05). There was a reduction in middle cerebral artery (MCA) mean velocities (111-87 cm/s, p = 0.039). ICP and TCD MOCAIP metrics demonstrated variances consistent with vasodilation that returned to baseline following the RIPC. Over the duration of the RIPC, microdialysis showed reduction in the lactate/pyruvate (L/P) ratio (42.37-33.77, p = 0.005) and glycerol (174.04-126 μg/l, p < 0.005), which persisted for 25-54 h after the last RIPC. CONCLUSIONS This study demonstrated cerebrovascular effects induced by RIPC consistent with transient vasodilation. Cerebral metabolic effects suggest protection from ischemia and cell membrane preservation lasting up to 2 days following RIPC.

An Empirical Study of Collaborative Acoustic Source Localization

Field biologists use animal sounds to discover the presence of individuals and to study their behavior. Collecting bio-acoustic data has traditionally been a difficult and time-consuming process in which researchers use portable microphones to record sounds while taking notes of their own detailed observations. The recent development of new deployable acoustic sensor platforms presents opportunities to develop automated tools for bio-acoustic field research. In this work, we implement both two-dimensional (2D) and three-dimensional (3D) AML-based source localization algorithms. The 2D algorithm is used to localize marmot alarm-calls of marmots on the meadow ground. The 3D algorithm is used to localize the song of Acorn Woodpecker and Mexican Antthrush birds situated above the ground. We assess the performance of these techniques based on the results from four field experiments: two controlled test of direction-of-arrival (DOA) accuracy using a pre-recorded source signal for 2D and 3D analysis, an experiment to detect and localize actual animals in their habitat, with a comparison to ground truth gathered from human observations, and a controlled test of localization experiment using pre-recorded source to enable careful ground truth measurements. Although small arrays yield ambiguities from spatial aliasing of high frequency signals, we show that these ambiguities are readily eliminated by proper bearing crossings of the DOAs from several arrays. These results show that the AML source localization algorithm can be used to localize actual animals in their natural habitat using a platform that is practical to deploy.

Regression analysis for peak designation in pulsatile pressure signals

Following recent studies, the automatic analysis of intracranial pressure (ICP) pulses appears to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many disorders. A pulse analysis framework has been recently developed to automatically extract morphological features of ICP pulses. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to designate the locations of the three ICP sub-peaks in a pulse. This paper extends this algorithm by utilizing machine learning techniques to replace Gaussian priors used in the peak designation process with more versatile regression models. The experimental evaluations are conducted on a database of ICP signals built from 700 h of recordings from 64 neurosurgical patients. A comparative analysis of different state-of-the-art regression analysis methods is conducted and the best approach is then compared to the original pulse analysis algorithm. The results demonstrate a significant improvement in terms of accuracy in favor of our regression-based recognition framework. It reaches an average peak designation accuracy of 99% using a kernel spectral regression against 93% for the original algorithm.

Pattern Recognition of Overnight Intracranial Pressure Slow Waves using Morphological Features of Intracranial Pressure Pulse

This study aimed to develop a new approach to detect intracranial pressure (ICP) slow waves based on morphological changes of ICP pulse waveforms. A recently proposed Morphological Clustering and Analysis of ICP Pulse (MOCAIP) algorithm was utilized to calculate a set of metrics that characterize ICP pulse morphology. A regularized linear quadratic classifier was used to test the hypothesis that classification between ICP slow wave and flat ICP could be achieved using features composed of mean values and dispersion of 24 MOCAIP metrics. To optimize the classification performance, three feature selection techniques (differential evolution, discriminant analysis and analysis of variance) were applied to find an optimal set of MOCAIP metrics under different criteria. In addition, we selected three sets of metrics common to those found by combination of two selection methods, to be used as classification features (differential evolution and analysis of variance, discriminant analysis and analysis of variance, and combination of differential evolution and discriminant analysis). To test the approach, a total of 276 selections of ICP recordings corresponding to two patterns without waves and containing slow waves were obtained from overnight ICP studies of 44 hydrocephalus patients performed at the UCLA Adult Hydrocephalus Center. Our results showed that the best classification performance of differentiation of slow waves from the ICP recording without slow waves was obtained using the combination of metrics common to both differential evolution and analysis of variance methods; achieving an accuracy of 89%, specificity 96%, and sensitivity 83%.

Consistent Changes in Intracranial Pressure Waveform Morphology Induced by Acute Hypercapnic Cerebral Vasodilatation

BackgroundIntracranial pressure (ICP) remains a pivotal physiological signal for managing brain injury and subarachnoid hemorrhage (SAH) patients in neurocritical care units. Given the vascular origin of the ICP, changes in ICP waveform morphology could be used to infer cerebrovascular changes. Clinical validation of this association in the setting of brain trauma, and SAH is challenging due to the multi-factorial influences on, and uncertainty of, the state of the cerebral vasculature.MethodsTo gain a more controlled setting, in this articel, we study ICP signals recorded in four uninjured patients undergoing a CO2 inhalation challenge in which hypercapnia induced acute cerebral vasodilatation. We apply our morphological clustering and analysis of intracranial pressure (MOCAIP) algorithm to identify six landmarks on individual ICP pulses (based on the three established ICP sub-peaks; P1, P2, and P3) and extract 128 ICP morphological metrics. Then by comparing baseline, test, and post-test data, we assess the consistency and rate of change for each individual metric.ResultsAcute vasodilatation causes consistent changes in a total of 72 ICP pulse morphological metrics and the P2 sub-region responds to cerebral vascular changes in the most consistent way with the greatest change as compared to P1 and P3 sub-regions.ConclusionsSince the dilation/constriction of the cerebral vasculature resulted in detectable consistent changes in ICP MOCIAP metrics, by an extended monitoring practice of ICP that includes characterizing ICP pulse morphology, one can potentially detect cerebrovascular changes, continuously, for patients under neurocritical care.

Robust Peak Recognition in Intracranial Pressure Signals

BackgroundThe waveform morphology of intracranial pressure pulses (ICP) is an essential indicator for monitoring, and forecasting critical intracranial and cerebrovascular pathophysiological variations. While current ICP pulse analysis frameworks offer satisfying results on most of the pulses, we observed that the performance of several of them deteriorates significantly on abnormal, or simply more challenging pulses.MethodsThis paper provides two contributions to this problem. First, it introduces MOCAIP++, a generic ICP pulse processing framework that generalizes MOCAIP (Morphological Clustering and Analysis of ICP Pulse). Its strength is to integrate several peak recognition methods to describe ICP morphology, and to exploit different ICP features to improve peak recognition. Second, it investigates the effect of incorporating, automatically identified, challenging pulses into the training set of peak recognition models.ResultsExperiments on a large dataset of ICP signals, as well as on a representative collection of sampled challenging ICP pulses, demonstrate that both contributions are complementary and significantly improve peak recognition performance in clinical conditions.ConclusionThe proposed framework allows to extract more reliable statistics about the ICP waveform morphology on challenging pulses to investigate the predictive power of these pulses on the condition of the patient.