We sought to biologically characterize and identify a subpopulation of urine-derived stem cells (USCs) with the capacity for multipotent differentiation. We demonstrated that single USCs can expand to a large population with 60-70 population doublings. Nine of 15 individual USC clones expressed detectable levels of telomerase and have long telomeres. These cells expressed pericyte and mesenchymal stem cell markers. Upon induction with appropriate media in vitro, USCs differentiated into bladder-associated cell types, including functional urothelial and smooth muscle cell lineages.When the differentiated USCs were seeded onto a scaffold and subcutaneously implanted into nude mice, multilayered tissue-like structures formed consisting of urothelium and smooth muscle. Additionally, USCs were able to differentiate into endothelial, osteogenic, chondrogenic, adipogenic, skeletal myogenic, and neurogenic lineages but did not form teratomas during the 1-month study despite telomerase activity. USCs may be useful in cell-based therapies and tissue engineering applications, including urogenital reconstruction.

Purpose The purpose of this prospective multicenter study was to assess the accuracy of a computer-aided surgical simulation (CASS) protocol for orthognathic surgery. Materials and Methods The accuracy of the CASS protocol was assessed by comparing planned and postoperative outcomes of 65 consecutive patients enrolled from 3 centers. Computer-generated surgical splints were used for all patients. For the genioplasty, one center utilized computer-generated chin templates to reposition the chin segment only for patients with asymmetry. Standard intraoperative measurements were utilized without the chin templates for the remaining patients. The primary outcome measurements were linear and angular differences for the maxilla, mandible and chin when the planned and postoperative models were registered at the cranium. The secondary outcome measurements were: maxillary dental midline difference between the planned and postoperative positions; and linear and angular differences of the chin segment between the groups with and without the use of the template. The latter was measured when the planned and postoperative models were registered at mandibular body. Statistical analyses were performed, and the accuracy was reported using root mean square deviation (RMSD) and Bland and Altman's method for assessing measurement agreement. Results In the primary outcome measurements, there was no statistically significant difference among the 3 centers for the maxilla and mandible. The largest RMSD was 1.0mm and 1.5° for the maxilla, and 1.1mm and 1.8° for the mandible. For the chin, there was a statistically significant difference between the groups with and without the use of the chin template. The chin template group showed excellent accuracy with largest positional RMSD of 1.0mm and the largest orientational RSMD of 2.2°. However, larger variances were observed in the group not using the chin template. This was significant in anteroposterior and superoinferior directions, as in pitch and yaw orientations. In the secondary outcome measurements, the RMSD of maxillary dental midline positions was 0.9mm. When registered at the body of the mandible, the linear and angular differences of the chin segment between the groups with and without the use of the chin template were consistent with the results found in the primary outcome measurements. Conclusion Using the CASS protocol, the computerized plan can be accurately and consistently transferred to the patient to position the maxilla and mandible at the time of surgery. The computer-generated chin template provides more accuracy in repositioning the chin segment than the intraoperative measurements.

It is important to observe and study cancer cells' cycle progression in order to better understand drug effects on cancer cells. Time-lapse microscopy imaging serves as an important method to measure the cycle progression of individual cells in a large population. Since manual analysis is unreasonably time consuming for the large volumes of time-lapse image data, automated image analysis is proposed. Existing approaches dealing with time-lapse image data are rather limited and often give inaccurate analysis results, especially in segmenting and tracking individual cells in a cell population. In this paper, we present a new approach to segment and track cell nuclei in time-lapse fluorescence image sequence. First, we propose a novel marker-controlled watershed based on mathematical morphology, which can effectively segment clustered cells with less oversegmentation. To further segment undersegmented cells or to merge oversegmented cells, context information among neighboring frames is employed, which is proved to be an effective strategy. Then, we design a tracking method based on modified mean shift algorithm, in which several kernels with adaptive scale, shape, and direction are designed. Finally, we combine mean-shift and Kalman filter to achieve a more robust cell nuclei tracking method than existing ones. Experimental results show that our method can obtain 98.8% segmentation accuracy, 97.4% cell division tracking accuracy, and 97.6% cell tracking accuracy.