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Fiorenza Ianzini

Publications and source records attributed to Fiorenza Ianzini.

2 recordsLinked to original sources

Lack of p53 function promotes radiation-induced mitotic catastrophe in mouse embryonic fibroblast cells.

BACKGROUND: We have demonstrated that in some human cancer cells both chronic mild heat and ionizing radiation exposures induce a transient block in S and G2 phases of the cell cycle. During this delay, cyclin B1 protein accumulates to supranormal levels, cyclin B1-dependent kinase is activated, and abrogation of the G2/M checkpoint control occurs resulting in mitotic catastrophe (MC). RESULTS: Using syngenic mouse embryonic fibroblasts (MEF) with wild-type or mutant p53, we now show that, while both cell lines exhibit delays in S/G2 phase post-irradiation, the mutant p53 cells show elevated levels of cyclin B1 followed by MC, while the wild-type p53 cells present both a lower accumulation of cyclin B1 and a lower frequency of MC. CONCLUSION: These results are in line with studies reporting the role of p53 as a post-transcriptional regulator of cyclin B1 protein and confirm that dysregulation of cyclin B1 promote radiation-induced MC. These findings might be exploited to design strategies to augment the yield of MC in tumor cells that are resistant to radiation-induced apoptosis.

Journal Article↗

Cell segmentation, tracking, and mitosis detection using temporal context.

The Large Scale Digital Cell Analysis System (LSDCAS) developed at the University of Iowa provides capabilities for extended-time live cell image acquisition. This paper presents a new approach to quantitative analysis of live cell image data. By using time as an extra dimension, level set methods are employed to determine cell trajectories from 2D + time data sets. When identifying the cell trajectories, cell cluster separation and mitotic cell detection steps are performed. Each of the trajectories corresponds to the motion pattern of an individual cell in the data set. At each time frame, number of cells, cell locations, cell borders, cell areas, and cell states are determined and recorded. The proposed method can help solving cell analysis problems of general importance including cell pedigree analysis and cell tracking. The developed method was tested on cancer cell image sequences and its performance compared with manually-defined ground truth. The similarity Kappa Index is 0.84 for segmentation area and the signed border positioning segmentation error is 1.6 +/- 2.1 microm.

Algorithms↗