Real-time cell-impedance sensing assay as an alternative to clonogenic assay in evaluating cancer radiotherapy.

Intrinsic radiosensitivity of normal and tumour tissues has been shown to be an independent prognostic factor for patients' response to radiotherapy. This study compares the real-time cell-impedance sensing (RT-CES) assay with the conventional clonogenic assay in terms of in-vitro radiosensitivity. One objective in this study was to predict in-vivo response to gold nanoparticle (GNP) treatment on the basis of in-vitro RT-CES testing results. Four adenocarcinoma cancer cell lines were tested using both the RT-CES and the clonogenic assays. Cell-survival curves were plotted, and the mean SF2 values obtained by these two different assay methods were compared using ANOVA. Radiation sensitivities obtained in-vitro were then compared with the in-vivo results. On the basis of the measurement of cell colonies, the RT-CES assay has similar radiosensitivity to the clonogenic assay, but significantly shortens the testing time from 14-21 days to only 72 h. Intrinsic GNP enhanced radiation sensitivity using tumour volume (mm(3)) in vivo is comparable with that using RT-CES cell survival assay in vitro. Furthermore, the RT-CES system provides real-time information regarding the state of cell radiosensitivity that may give useful information towards personalizing radiotherapy. The RT-CES assay enables more reliable and time-efficient results in the evaluation of radiosensitivity.

A Variational Bayesian Approach to Robust Identification of Switched ARX Models.

A variational Bayesian approach to robust identification of switched auto-regressive exogenous models is developed in this paper. By formulating the problem of interest under a full Bayesian identification framework, the number of local-models can be determined automatically, while accounting for the uncertainty of parameter estimates in the overall identification procedure. A set of significance coefficients is used to assign proper importance weights to local-models. By maximizing the marginal likelihood of the identification data, insignificant local-models will be suppressed and the optimal number of local-models can be determined. Considering the fact that the identification data may be contaminated with outliers, t distributions with adjustable tails are utilized to model the contaminating noise so that the proposed identification algorithm is robust. The effectiveness of the proposed Bayesian approach is demonstrated through a simulated example as well as a detailed industrial application.