High-efficiency transfer and expression of AdCMV-p53 in human cervix adenocarcinoma cells induced by subclinical-dose carbon beam radiation

Purpose The aim of this study is to evaluate the eVect of carbon-beam irradiation on adenovirus-mediated p53 transfer in human cervix adenocarcinoma.Materials and methods The HeLa cells pre-exposed to carbon-beam or -ray, were infected with replication-deficient adenovirus recombinant vectors, containing human wild-type p53 (AdCMV-p53) and green Xuorescent protein (GFP) (AdCMV–GFP), respectively. The GFP transfer and p53 expression were detected by Xow cytometric analysis.Results The GFP transfer frequency in C-beam with AdCMV-GFP groups was 38–50% more than that inγ-ray with AdCMV–GFP groups. The percentage of p53 positive cells in the C-beam with AdCMV–p53 groups was 34–55.6% more than that in γ-ray with AdCMV-p53 groups (p < 0.05), suggesting that subclinical-dose C-beam irradiation could signiWcantly promote exogenous p53 transfer and p53 expression, and extend the duration of p53 expression in the HeLa cells. The expression of p21 increased with p53 expression in HeLa cells. The survival fractions for the 0.5–1.0 Gy C-beam with AdCMV-p53 groups were 38–43% less than those for the isodose γ-ray with AdCMV-p53 groups, and 31–40% less than those for the C-beam only groups (p <0.05).Conclusions The subclinical-dose C-beam irradiation could signiWcantly promote the transfer and expression of exogenous p53, extend the duration of p53 expression, and enhance the suppression of p53 on cervix adenocarcinoma cells.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.

Diagnosis of colorectal cancer using Raman spectroscopy of laser-trapped single living epithelial cells

A single-cell diagnostic technique for epithelial cancers is developed by utilizing laser trapping and Raman spectroscopy to differentiate cancerous and normal epithelial cells. Single-cell suspensions were prepared from surgically removed human colorectal tissues following standard primary culture protocols and examined in a near-infrared laser-trapping Raman spectroscopy system, where living epithelial cells were investigated one by one. A diagnostic model was built on the spectral data obtained from 8 patients and validated by the data from 2 new patients. Our technique has potential applications from epithelial cancer diagnosis to the study of cell dynamics of carcinogenesis. (c) 2006 Optical Society of America.

Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer

Raman spectroscopy on single, living epithelial cells captured in a laser trap is shown to have diagnostic power over colorectal cancer. This new single-cell technology comprises three major components: primary culture processing of human tissue samples to produce single-cell suspensions, Raman detection on singly trapped cells, and diagnoses of the cells by artificial neural network classifications. it is compared with DNA flow cytometry for similarities and differences. Its advantages over tissue Raman spectroscopy are also discussed. In the actual construction of a diagnostic model for colorectal cancer, real patient data were taken to generate a training set of 320 Raman spectra and, a test set of 80. By incorporating outlier corrections to a conventional binary neural classifier, our network accomplished significantly better predictions than logistic regressions, with sensitivity improved from 77.5% to 86.3% and specificity improved from 81.3% to 86.3% for the training set and moderate improvements for the test set. Most important, the network approach enables a sensitivity map analysis to quantitate the relevance of each Raman band to the normal-to-cancer transform at the cell level. Our technique has direct clinic applications for diagnosing cancers and basic science potential in the study of cell dynamics of carcinogenesis. (C) 2007 Society of Photo-Optical Instrumentation Engineers.