Biomedical subjects

Chunqi Chang

Publications and source records attributed to Chunqi Chang.

3 recordsLinked to original sources

PubMed2006

Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials.

Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP. The RBFN consists of an input layer of source nodes, a single hidden layer of non-linear processing units and an output layer of linear weights. It has built-in nonlinear activation functions that allow learning of function mappings. Moreover, it produces satisfactory estimates of signals against a background noise without a priori knowledge of the signal, provided that the signal and noise are independent. In clinical situations where EP responses change rapidly, the convergence rate of the algorithm becomes a critical factor. A carefully designed data-reusing RBFN can accelerate the convergence rate markedly and, thus, enhance its performance. Both theoretical analysis and simulation results support the improved performance of our new algorithm.

Adolescent↗

PubMed2005

Stability of infectious recombinant adeno-associated viral vector in gene delivery.

BACKGROUND: The aim of this study is to provide a basis for the design of appropriate protocols for the shipping and storage of rAAV vectors for experimental laboratory studies and clinical trials. MATERIAL/METHODS: rAAV stocks were generated by standard methods and then subjected to different environments. The transduction efficiency of viral vectors both in vitro and in vivo was determined by luciferase activity and immunohistochemistry. RESULTS: The virus stored at -80 degrees C remained completely stable and had high transduction efficiency. By contrast, the transduction efficiency of all other groups on 293 cells decreased continuously over time. The transduction efficiency of the -20 degrees C group remained relatively high for the first 5 days, but dropped sharply between days 5 and 7. The transduction efficiency for the 4 degrees C group dropped sharply on both days 1 and 7, and continued to decrease to 55% of maximum efficiency by the end of the first month. For both the room temperature (RT) and 37 degrees C groups, a sharp fall in efficiency was observed at day 1, and efficiency continued to decline throughout the experimental period. Data from the in vivo study also revealed that rAAV vector stored at -80 degrees C remained stable and retained its transduction efficiency. CONCLUSIONS: The virus stored at -80 degrees C remained completely stable and retained high transduction efficiency. The implications of these findings provide a basis for viral stock portioning and avoidance of freeze-thawing and storing at temperatures above -80 degrees C prior to clinical trials.

Animals↗

PubMed2005

Novel sparse component analysis approach to free radical EPR spectra decomposition.

Free radicals play important roles in many physiological and pathological pathways in biological systems. These free radicals can be detected and quantified by their EPR spectra. The measured EPR spectra are often mixtures of pure spectra of several different free radicals and other chemicals. Blind source separation can be applied to estimate the pure spectra of interested free radicals. However, since the pure EPR spectra are often not independent of each other, the approach based on independent component analysis (ICA) cannot accurately extract the required spectra. In this paper, a novel sparse component analysis method for blind source separation, which exploits the sparsity of the EPR spectra, is presented to reliably extract the pure source spectra from their mixtures with high accuracy. This method has been applied to the analysis of EPR spectra of superoxide, hydroxyl, and nitric oxide free radicals, for both simulated data and real world ex vivo experiment. Compared to the traditional self-modeling method and our previous ICA-based blind source separation method, the proposed sparse component analysis approach gives much better results and can give perfect separation for mixtures of superoxide spectrum and hydroxyl spectrum in the ideal noise-free case. This method can also be used in other similar applications of quantitative spectroscopy analysis.

Algorithms↗