Exosomes are stable nanovesicles secreted by cells into the circulation. Their reported sizes differ substantially, which likely reflects the difference in the isolation techniques used, the cells that secreted them, and the methods used in their characterization. We analyzed the influence of the last factor on the measured sizes and shapes of hydrated and desiccated exosomes isolated from the serum of a pancreatic cancer patient and a healthy control. We found that hydrated exosomes are close-to-spherical nanoparticles with a hydrodynamic radius that is substantially larger than the geometric size. For desiccated exosomes, we found that the desiccated shape and sizing are influenced by the manner in which drying occurred. Isotropic desiccation in aerosol preserves the near-spherical shape of the exosomes, whereas drying on a surface likely distorts their shapes and influences the sizing results obtained by techniques that require surface fixation prior to analysis.

SUMMARYIn this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman-Bucy filter.

Implantable flow and pressure sensors, used to control rotary blood pumps, are unreliable in the long term. It is, therefore, desirable to develop a physiological control system that depends only on readily available measurements of the intrinsic pump parameters, such as measurements of the pump current, voltage, and speed (in revolutions per minute). A previously proposed DeltaP control method of ventricular assist devices (VADs) requires the implantation of two pressure sensors to measure the pressure difference between the left ventricle and aorta. In this article, we propose a model-based method for estimating DeltaP, which eliminates the need for implantable pressure sensors. The developed estimator consists of the extended Kalman filter in conjunction with the Golay-Savitzky filter. The performance of the combined estimator-VAD controller system was evaluated in computer simulations for a broad range of physical activities and varying cardiac conditions. The results show that there was no appreciable performance degradation of the estimator-controller system compared to the case when DeltaP is measured directly. The proposed approach effectively utilizes a VAD as both a pump and a differential pressure sensor, thus eliminating the need for dedicated implantable pressure and flow sensors. The simulation results show that different pump designs may not be equally effective at playing a dual role of a flow actuator and DeltaP sensor.

Exosomes are membrane nanovesicles implicated in cell-to-cell signaling in which they transfer their molecular cargo from the parent to the recipient cells. This role essentially depends on the exosomes' small size, which is the prerequisite for their rapid migration through the crowded extracellular matrix and into and out of circulation. Here we report much lower exosome mobility than expected from the size of their vesicles, implicate membrane proteins in a substantially impeded rate of migration, and suggest an approach to quantifying the impact. The broadly distributed excess hydrodynamic resistance provided by surface proteins produces a highly heterogeneous and microenvironment-dependent hindrance to exosome mobility. The implications of the findings on exosome-mediated signaling are discussed.

The optimal filter for continuous, linear, stochastic, time-varying systems described by the Itô-Volterra equations with discontinuous measure is derived. With an appropriately selected measure, the result is applicable to a wide range of observation processes, including the hybrid case of observations formed by an arbitrary combination of continuous and discrete measurements, which may be sampled with a priori unknown, changing, and, possibly, random rates and delays. The simultaneous presence of continuous and sampled measurements causes impulsive discontinuity in the inputs of the optimal filter equations, which leads to a discontinuous change in state estimates every time a sampled measurement becomes available. Using the theory of vibrosolutions, the explicit and unique expressions for the jumps in state estimates and estimation error covariance are derived. Several examples illustrate the procedure of modeling hybrid measurement systems by selecting an appropriate discontinuous measure. We further show that the Itô-Volterra model and the main result of the paper can be specialized to several important cases, including state space systems, for which we recover several known state estimation results, and derive a novel optimal filter for continuous LTV systems with an arbitrary combination of continuous and delayed sampled measurements. This optimal filter updates the state estimates for incoming measurements as soon as they become available and does not require prior knowledge of sampling instants and delays, which makes it applicable when deterministic and random changes in sampling and delays are present. Several computational examples demonstrate the implementation of the developed filter and compare its performance to the traditional alternatives using MonteCarlo simulations.Index Terms-Continuous and sampled measurements, delayed measurements, Itô-Volterra systems, multirate and random sampling, optimal state estimation.

We present arguments and simulation results in favor of a novel strategy for control of rotary blood pumps. We suggest that physiological perfusion is achieved when the blood pump is controlled to maintain an average reference differential pressure. In the case of rotary left ventricular assist devices, our simulations show that maintaining a constant average pressure difference between the left ventricle and aorta results in physiological perfusion over a wide range of physical activities and clinical cardiac conditions. We simulated rest, light, and strenuous exercise conditions, corresponding to cardiac demands of 4.92, 7.98, and 14.62 L/min, respectively. For different exercise levels, the clinical conditions ranged from normal to failing to asystolic heart. By maintaining a constant pressure difference of 75 mm Hg between the left ventricle and aorta, with either an axial or a centrifugal blood pump, a total cardiac output close to the physiological cardiac demand was achieved, irrespective of the heart condition. The simulations of the transitions between different levels of exercise indicate that with the same reference differential pressure, the proposed approach leads to rapid adaptation of the total cardiac output to physiological levels, while avoiding suction. Comparison with the traditional control strategy of maintaining a reference rotational speed (rpm) of the pump indicates that though the traditional approach has some degree of adaptability, it is only adequate over a narrow range of cardiac demand and clinical conditions of the patient. Our results indicate that the proposed approach is superior to the alternatives in providing an adequate and autonomous adaptation of the total cardiac output over a broad range of exercise conditions (expected when an assist device is used as a destination therapy) and clinical statuses of the native heart (such as further deterioration or recovery of cardiac function), while having the potential to improve the quality of life of patients by reducing the need for monitoring and frequent human intervention. The proposed approach can be clinically implemented using simple controllers, and requires the implantation of two pressure sensors, or estimation of the pressure difference based on other available measurements.

This article presents an integrated model of the human circulatory system that incorporates circulatory support by a brushless DC axial flow ventricular assist device (VAD), and a feedback VAD controller designed to maintain physiologically sufficient perfusion. The developed integrated model combines a network type model of the circulatory system with a nonlinear dynamic model of the brushless DC pump We show that maintaining a reference differential pressure between the left ventricle and aorta leads to adequate perfusion for different pathologic cases, ranging from normal heart to left heart asystole, and widely varying physical activity scenarios from rest to exercise.

In this paper, the optimal filtering problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the observation equation is introduced to reduce the original problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman-Bucy filter. 483closed system of filtering equations for a certain number of lower conditional moments. The most famous result, the Kalman-Bucy filter [2], is related to the case of linear state and observation equations, where only two moments, the estimate itself and its variance, form a closed system of filtering equations. However, the optimal nonlinear finite-dimensional filter can be obtained in some other cases, if, for example, the state vector can take only a finite number of admissible states [3] or if the observation equation is linear and the drift term in the state equation satisfies the Riccati equation d f /dx + f 2 = x 2 (see [4]). The complete classification of the 'general situation' cases (this means that there are no special assumptions on the structure of state and observation equations and the initial conditions), where the optimal nonlinear finite-dimensional filter exists, is given in [5]. The last two papers actually deal with specific types of polynomial filtering systems. There also exists a considerable bibliography on robust filtering for the 'general situation' systems (see, for example, [6-11]). Apart from the 'general situation', the optimal finite-dimensional filters have recently been designed [12][13][14] for certain classes of polynomial system states with Gaussian initial conditions over linear observations with an invertible observation matrix.This paper presents the optimal finite-dimensional filter for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix, thus generalizing the results of [12][13][14]. Designing the optimal filter for polynomial systems with a non-invertible observation matrix presents a significant advantage in the filtering theory and practice, since it enables one to address the joint state and parameter optimal identification problems for polynomial systems. The optimal filtering problem is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance [15]. As the first ...