Swanand Khare

Research article: In vitro cytotoxicity assessment based on KC50 with real-time cell analyzer (RTCA) assay

Technological advances in cytotoxicity analysis have now made it possible to obtain real time data on changes in cell growth, morphology and cell death. This type of testing has a great potential for reducing and refining traditional in vivo toxicology tests. By monitoring the dynamic response profile of living cells via the xCELLigence real-time cell analyzer for high-throughput (RTCA HT) system, cellular changes including cell number (cell index, CI) are recorded and analyzed. A special scaled index defined as normalized cell index (NCI) is used in the analysis which reduces the influence of inter-experimental variations. To assess the extent of exposure of the tested chemicals, a two-exponent model is presented to describe rate of cell growth and death. This model is embodied in the time and concentration-dependent cellular response curves, and the parameters k1 and k2 in this model are used to describe the rate of cell growth and death. Based on calculated k2 values and the corresponding concentrations, a concentration-response curve is fitted. As a result, a cytotoxicity assessment named KC50 is calculated. The validation of the proposed method is demonstrated by exposing six cell lines to 14 chemical compounds. Our findings suggest that the proposed KC50-based toxicity assay can be an alternative to the traditional single time-point assay such as LC50 (the concentration at which 50% of the cells are killed). The proposed index has a potential for routine evaluation of cytotoxicities. Another advantage of the proposed index is that it extracts cytotoxicity information when CI fails to detect the low toxicity.

Mode of action classification of chemicals using multi-concentration time-dependent cellular response profiles

In this paper, we present a new statistical pattern recognition method for classifying cytotoxic cellular responses to toxic agents. The advantage of the proposed method is to quickly assess the toxicity level of an unclassified toxic agent on human health by bringing cytotoxic cellular responses with similar patterns (mode of action, MoOA) into the same class. The proposed method is a model-based hierarchical classification approach incorporating principal component analysis (PCA) and functional data analysis (FDA). The cytotoxic cell responses are represented by multi-concentration time-dependent cellular response profiles (TCRPs) which are dynamically recorded by using the xCELLigence real-time cell analysis high-throughput (RTCA HT) system. The classification results obtained using our algorithm show satisfactory discrimination and are validated using biological facts by examining common chemical mechanisms of actions with treatment on human hepatocellular carcinoma cells (HepG2).

Particle filtering without tears: A primer for beginners

Abstract The main purpose of this primer is to systematically introduce the theory of particle filters to readers with limited or no prior understanding of the subject. The primer is written for beginners and practitioners interested in learning about the theory and implementation of particle filtering methods. Throughout this primer we highlight the common mistakes that beginners and first-time researchers make in understanding and implementing the theory of particle filtering. We also discuss and demonstrate the use of particle filtering in nonlinear state estimation applications. We conclude the primer by providing an implementable version of MATLAB code for particle filters. The code not only aids in improving the understanding of particle filters, it also serves as a template for building and implementing advanced nonlinear state estimation routines.

High-throughput screening assay for the environmental water samples using cellular response profiles

Chemical and physical analyses are commonly used as screening methods for the environmental water. However, these methods can only look for the targeted substance but may miss unexpected toxicants. Furthermore, the synergistic effects of mixture cannot be detected. In order to set up the assay criteria for determining various biological activities at a cellular level that could potentially lead to toxicity of environmental water samples, a novel test based on cellular response by using Real-Time Cellular Analyzer (RTCA) is proposed in this study. First, the water sample is diluted to a series of strengths (80%, 60%, 40%, 30%, 20% and 10%) to get the multi-concentration cellular response profile. Then, the area under the cellular response profile (AUCRP) is calculated. Comparing to the normal cell growth of negative control, a new biological activity index named Percentage of Effect (PoE) has been presented which reflects the cumulative inhibitory activity of cell growth over the log-phase. Finally, a synthetical index PoE50 is proposed to evaluate the intensity of biological activities in water samples. The biological experiment demonstrates the effectiveness of the proposed method.

Robust online algorithm for adaptive linear regression parameter estimation and prediction

In this article, we focus on adaptive linear regression methods and propose a new technique. The article begins with a review of the online passive aggressive algorithm (OPAA), an adaptive linear regression algorithm from the machine learning literature. We highlight the strengths and weaknesses of OPAA and compare it with other popular adaptive regression techniques such as moving window and recursive least squares, recursive partial least squares, and just‐in‐time or locally weighted regression. Modifications to OPAA are proposed to make it more robust and better suited for industrial soft‐sensor applications. The new algorithm is called smoothed passive aggressive algorithm (SPAA), and like OPAA, it follows a cautious parameter update strategy but is more robust. The trade‐off between SPAAs computation complexity and accuracy can be easily controlled by manipulating just two tuning parameters. We also demonstrate that the SPAA framework is quite flexible and a number of variants are easily formulated. Application of SPAA to estimate the time‐varying parameters of a numerically simulated autoregressive with exogenous terms (ARX) model and to predict the Reid vapor pressure of the bottoms flow from an industrial column demonstrates its superior performance over OPAA and comparable performance with the other popular algorithms. Copyright

Analysis of inter-/intra-E-plate repeatability in the real-time cell analyzer

Over the past decade, the real-time cell analyzer (RTCA) has provided a good tool to the cell-based in vitro assay. Unlike the traditional systems that label the target cells with luminescence, fluorescence, or light absorption, RTCA monitors cell properties using noninvasive and label-free impedance measuring. However, realization of the maximum value of RTCA for applications will require assurance of within-experiment repeatability, day-to-day repeatability, and robustness to variations in conditions that might occur from different experiments. In this article, the performance and variability of RTCA is evaluated and a novel repeatability index (RI) is proposed to analyze the intra-/inter-E-plate repeatability of RTCA. The repeatability assay involves six cell lines and two media (water [H2O] and dimethyl sulfoxide [DMSO]). First, six cell lines are exposed to the media individually, and time-dependent cellular response curves characterized as a cell index (CI) are recorded by RTCA. Then, the variations along sampling time and among repeated tests are calculated and RI values are obtained. Finally, a discriminating standard is set up to evaluate the degree of repeatability. As opposed to the standardized methodologies, it is shown that the presented index can give the quantitative evaluation for repeatability of RTCA within E-plate and variation on different days.

Bayesian identification of non-linear state-space models: Part I- Input design

Abstract We propose an algorithm for designing optimal inputs for on-line Bayesian identification of stochastic non-linear state-space models. The proposed method relies on minimization of the posterior Cramer Rao lower bound derived for the model parameters, with respect to the input sequence. To render the optimization problem computationally tractable, the inputs are parametrized as a multi-dimensional Markov chain in the input space. The proposed approach is illustrated through a simulation example.

Real-Time Performance Assessment of Inferential Sensors

Abstract A data-driven Bayesian framework for real-time performance assessment of inferential sensors is proposed. The application of the proposed Bayesian solution does not depend on the identification techniques employed for inferential model development. The effectiveness of the proposed method is demonstrated through a simulation case study.

A switching strategy for adaptive state estimation

Abstract This paper develops a switching strategy for adaptive state estimation in systems represented by nonlinear, stochastic, discrete-time state space models (SSMs). The developed strategy is motivated by the fact that there is no single Bayesian estimator that is guaranteed to perform optimally for a given nonlinear system and under all operating conditions. The proposed strategy considers a bank of plausible Bayesian estimators for adaptive state estimation, and then switches between them based on their performance. The performance of a Bayesian estimator is assessed using a performance measure derived from the posterior Cramer-Rao lower bound (PCRLB). It is shown that the switching strategy is stable, and yields estimates that are at least as good as any individual estimator in the bank. The efficacy of the switching strategy is illustrated on a practical simulation example.

Development of Toxicity Index to Evaluate the Level of Water Contamination by Using Cellular Responses

Abstract Conventional tests in environmental monitoring have been performed by quantifying levels of toxicity of specific substances such as pesticides or drugs, and then comparing with known toxicity thresholds. These tests are conducted using analytical chemistry methods and can only be used for targeted substances, often missing unexpected toxicants. This shortcoming underlines the need for novel tests for rapid assessment of toxicity of environmental sample mixtures, followed by more detailed and expensive laboratory analysis as required. In order to evaluate the level of toxicity in water contamination, a mathematical model to predict the toxicity index is developed based on time-dependent cellular response curves (TCRCs). First, the water sample is diluted to a series of strength (80%, 60%, 40%, 30% 20% and 10%) to get the multiple concentrations. Then, the living cells are exposed to those water samples and the corresponding dynamic cytotoxicity response curves are collected via xCELLigence real-time cellular analyzer for high throughput (RTCA HT) system. A synthetical index, based on the calculation of area under curve (AUC) of the negative control, is proposed to evaluate the level of toxicity in water contamination. The proposed index also takes the variation of biological experiment into consideration. The biological experiment demonstrates the effectiveness of the proposed toxicity index to measure the level of toxicity in water contamination.