Integrated sampling-and-sensing using microdialysis and biosensing by particle motion for continuous cortisol monitoring

Microdialysis catheters are small probes that allow sampling from biological systems and human subjects with minimal perturbation. Traditionally, microdialysis samples are collected in vials, transported to a laboratory, and analysed with typical turnaround times of hours to days. To realize a continuous sampling-and-sensing methodology with minimal time delay, we studied the integration of microdialysis sampling with a sensor for continuous biomolecular monitoring based on Biosensing by Particle Motion (BPM). A microfluidic flow cell was designed with a volume of 12 μl in order to be compatible with flowrates of microdialysis sampling. The analyte recovery and the time characteristics of the sampling-and-sensing system were studied using a food colorant in buffer and using cortisol in buffer and in blood plasma. Concentration step functions were applied, and the system response was measured using optical absorption and a continuous BPM cortisol sensor. The cortisol recovery was around 80% for a 30 mm microdialysis membrane with a 20 kDa molecular weight cut-off and a flowrate of 2 μl min−1. The concentration-time data could be fitted with a transport delay time and single-exponential relaxation curves. The total delay time of the sampling-and-sensing methodology was about 15 minutes. Continuous sampling-and-sensing was demonstrated over a period of 5 hours. These results represent an important step toward integrated sampling-and-sensing for the continuous monitoring of a wide variety of low-concentration biomolecular substances for applications in biological and biomedical research.</p

Integrated sampling-and-sensing using microdialysis and biosensing by particle motion for continuous cortisol monitoring

Microdialysis catheters are small probes that allow sampling from biological systems and human subjects with minimal perturbation. Traditionally, microdialysis samples are collected in vials, transported to a laboratory, and analysed with typical turnaround times of hours to days. To realize a continuous sampling-and-sensing methodology with minimal time delay, we studied the integration of microdialysis sampling with a sensor for continuous biomolecular monitoring based on Biosensing by Particle Motion (BPM). A microfluidic flow cell was designed with a volume of 12 μl in order to be compatible with flowrates of microdialysis sampling. The analyte recovery and the time characteristics of the sampling-and-sensing system were studied using a food colorant in buffer and using cortisol in buffer and in blood plasma. Concentration step functions were applied, and the system response was measured using optical absorption and a continuous BPM cortisol sensor. The cortisol recovery was around 80% for a 30 mm microdialysis membrane with a 20 kDa molecular weight cut-off and a flowrate of 2 μl min−1. The concentration-time data could be fitted with a transport delay time and single-exponential relaxation curves. The total delay time of the sampling-and-sensing methodology was about 15 minutes. Continuous sampling-and-sensing was demonstrated over a period of 5 hours. These results represent an important step toward integrated sampling-and-sensing for the continuous monitoring of a wide variety of low-concentration biomolecular substances for applications in biological and biomedical research.</p

The validity of family size preference measurements in rural Latin America

Includes bibliograph

Price Variations in a Stock Market With Many Agents

Large variations in stock prices happen with sufficient frequency to raise doubts about existing models, which all fail to account for non-Gaussian statistics. We construct simple models of a stock market, and argue that the large variations may be due to a crowd effect, where agents imitate each other's behavior. The variations over different time scales can be related to each other in a systematic way, similar to the Levy stable distribution proposed by Mandelbrot to describe real market indices. In the simplest, least realistic case, exact results for the statistics of the variations are derived by mapping onto a model of diffusing and annihilating particles, which has been solved by quantum field theory methods. When the agents imitate each other and respond to recent market volatility, different scaling behavior is obtained. In this case the statistics of price variations is consistent with empirical observations. The interplay between ``rational'' traders whose behavior is derived from fundamental analysis of the stock, including dividends, and ``noise traders'', whose behavior is governed solely by studying the market dynamics, is investigated. When the relative number of rational traders is small, ``bubbles'' often occur, where the market price moves outside the range justified by fundamental market analysis. When the number of rational traders is larger, the market price is generally locked within the price range they define.Comment: 39 pages (Latex) + 20 Figures and missing Figure 1 (sorry), submitted to J. Math. Eco

Towards continuous monitoring of TNF-α at picomolar concentrations using biosensing by particle motion

The ability to continuously monitor cytokines is desirable for fundamental research studies and healthcare applications. Cytokine release is characterized by picomolar circulating concentrations, short half-lives, and rapid peak times. Here, we describe the characteristics and feasibility of a particle-based biosensing technique for continuous monitoring of TNF-α at picomolar concentrations. The technique is based on the optical tracking of particle motion and uses an antibody sandwich configuration. Experimental results show how the analyte concentration influences the particle diffusivity and characteristic response time of the sensor, and how the sensitivity range depends on the antibody functionalization density. Furthermore, the data clarifies how antibodies supplemented in solution can shorten the characteristic response time. Finally, we demonstrate association rate-based sensing as a first step towards continuous monitoring of picomolar TNF-α concentrations, over a period of 2 h with delay times under 15 min. The insights from this research will enable the development of continuous monitoring sensors using high-affinity binders, providing the sensitivity and speed needed in applications like cytokine monitoring.</p

Sandwich Immunosensor Based on Particle Motion:How Do Reactant Concentrations and Reaction Pathways Determine the Time-Dependent Response of the Sensor?

To control and optimize the speed of a molecular biosensor, it is crucial to quantify and understand the mechanisms that underlie the time-dependent response of the sensor. Here, we study how the kinetic properties of a particle-based sandwich immunosensor depend on underlying parameters, such as reactant concentrations and the size of the reaction chamber. The data of the measured sensor responses could be fitted with single-exponential curves, with characteristic response times that depend on the analyte concentration and the binder concentrations on the particle and substrate. By comparing characteristic response times at different incubation configurations, the data clarifies how two distinct reaction pathways play a role in the sandwich immunosensor, namely, analyte binding first to particles and thereafter to the substrate, and analyte binding first to the substrate and thereafter to a particle. For a concrete biosensor design, we found that the biosensor is dominated by the reaction pathway where analyte molecules bind first to the substrate and thereafter to a particle. Within this pathway, the binding of a particle to the substrate-bound analyte dominates the sensor response time. Thus, the probability of a particle interacting with the substrate was identified as the main direction to improve the speed of the biosensor while maintaining good sensitivity. We expect that the developed immunosensor and research methodology can be generally applied to understand the reaction mechanisms and optimize the kinetic properties of sandwich immunosensors with particle labels.</p

Mass Models for Spiral Galaxies from 2-D Velocity Maps

We model the mass distributions of 40 high surface brightness spiral galaxies inside their optical radii, deriving parameters of mass models by matching the predicted velocities to observed velocity maps. We use constant mass-to-light disk and bulge models, and we have tried fits with no halo and with three different halo density profiles. The data require a halo in most, but not all, cases, while in others the best fit occurs with negligible mass in the luminous component, which we regard as unphysical. All three adopted halo profiles lead to fits of about the same quality, and our data therefore do not constrain the functional form of the halo profile. The halo parameters display large degeneracies for two of the three adopted halo functions, but the separate luminous and dark masses are better constrained. However, the fitted disk and halo masses vary substantially between the adopted halo models, indicating that even high quality 2-D optical velocity maps do not provide significant constraints on the dark matter content of a galaxy. We demonstrate that data from longslit observations are likely to provide still weaker constraints. We conclude that additional information is needed in order to constrain the separate disk and halo masses in a galaxy.Comment: 41 pages, 13 figures, accepted for publication in A

String Thermalization at a Black Hole Horizon

Susskind has recently shown that a relativistic string approaching the event horizon of a black hole spreads in both the transverse and longitudinal directions in the reference frame of an outside observer. The transverse spreading can be described as a branching diffusion of wee string bits. This stochastic process provides a mechanism for thermalizing the quantum state of the string as it spreads across the stretched horizon.Comment: 14 pages, latex, SU-ITP-94-4, NSF-ITP-94-1

Reaction-diffusion model for the preparation of polymer gratings by patterned ultraviolet illumination

A model is developed to describe the migration mechanism of monomers during the lithographic preparation of polymer gratings by ultraviolet polymerization. The model is based on the Flory–Huggins theory: a thermodynamic theory that deals with monomer/polymer solutions. During the photoinduced polymerization process, monomer migration is assumed to be driven by a gradient in the chemical potential rather than the concentration. If the chemical potential is used as the driving force, monomer migration is not only driven by a difference in concentration, or volume fraction, but also by other entropic effects such as monomer size and the degree of crosslinking of the polymer network, which is related to the ability of a polymer to swell. Interaction of the monomers with each other or the polymer is an additional energetic term in the chemical potential. The theoretical background of the model is explained and results of simulations are compared with those of nuclear microprobe measurements. A nuclear microprobe is used to determine the spatial monomer distribution in the polymer gratings. It is shown that two-way diffusion is expected if the monomers are both difunctional and have the same size. In some cases, if one monomer is considerably smaller than the other, it can eventually have a higher concentration in the illuminated regions, even when it has a lower reactivity. The model is used to simulate the grating formation process. This results in a calculated distribution of the monomer volume fractions as a function of position in polymer gratings. An excellent agreement with the nuclear microprobe measurements is obtained. ©2004 American Institute of Physics