Bedürfnisfragebogen für die pädiatrische Onkologie

Monatsschrift Kinderheilkunde - Bedürfnisse onkologisch erkrankter Kinder im Kontext der Versorgung sind noch wenig erforscht, was u.?a. in einem Mangel an entsprechenden...     

The use of the monoclonal antibody Ki-67 in the identification of proliferating cells: application to surgical neuropathology

In order to test its potential application to surgical neuropathology, the monoclonal antibody Ki-67 was used to demonstrate immunohistochemically the proliferating cells in 40 neoplasms of the nervous system. The antibody, which reacts with a nuclear protein expressed in the G1, G2, S, and M phases of the cell cycle, was demonstrated in frozen sections of all lesions. The highest incidence of stained nuclei was found in a metastatic carcinoma (57%). The percentage of stained cells in gliomas was in general agreement with the histologic grade and known biologic behavior of the lesions, ranging from 0.6% in a pilocytic astrocytoma to 12.4% in a glioblastoma multiforme. In the fibrillary astrocytic neoplasms of low cellularity, there were good correlations between the percentages of stained cells and the degrees of nuclear pleomorphism and chromatin density. In meningiomas, schwannomas, and a cerebellar hemangioblastoma, the fractions of labeled nuclei were less than 1%. The percentage of stained cells in pituitary adenomas showed considerable variation among the four cases (0.2-1.5%), the biologic significance of which is unknown. In four of the above cases, Ki-67 staining was performed on air-dried squash preparations with excellent visualization of immunoreactive nuclei. In one case, a hemangioblastoma, no stained nuclei were seen. The results confirm that Ki-67 staining is technically suitable as a diagnostic method, with good correlations between frozen sections and smear preparations. Determination of the replicating cell fraction could become an important additional criterion to predict the biologic behavior of nervous system neoplasms.     

Three-Dimensional Soft Tissue Prediction Using Finite Elements

Abstract Background and Aim: The prediction of soft tissue esthetics is important for achieving an optimal esthetic outcome in orthodontic treatment planning. Applicable procedures have so far been restricted to two-dimensional profile predictions that have not proven to be very reliable. The goal of this investigation was therefore to develop a novel finite element-based procedure that allows a three-dimensional, easily visualized, quantitative analysis and prediction of soft tissue behavior for the clinician. The procedure to be developed should be easy to handle and not entail any additional radiation exposure for the patient. Material and Methods: Using a three-dimensional scanner, the facial surfaces of 20 probands were digitalized and individual FEM models were generated. Results: After reduction of data redundancy via several conversion steps, a patient-specific simulation model was prepared consisting of 20,000 to 40,000 individual elements to which specific physical properties could be assigned. The average time required for generating a virtual model was 50 minutes. Problems occurring during model generation were rare (mainly shadowing phenomena and movement artifacts). Conclusion: The procedure outlined herein makes the reliable generation of patient-specific simulation models possible for facial soft tissue prediction in orthodontics.     

Three-Dimensional Soft Tissue Prediction Using Finite Elements

BACKGROUND AND AIM: The goal of this study was to analyze the validity and prediction accuracy of a newly-developed procedure for three-dimensional soft tissue prediction based on Finite Element Method, and to compare the results with prediction produced using an existing two-dimensional prediction program (Dentofacial Planner Plus). PATIENTS AND METHODS: In twelve patients who underwent combined surgical-orthodontic treatment, profile prediction was generated using both procedures preoperatively and then compared at predefined measurement points with the patient's actual postoperative soft tissue status. RESULTS: The deviations observed depended on the facial region, whereby the prediction errors for both procedures were much greater in the lower facial third than in the midfacial third. Calculating in all the measurement points, the mean horizontal prediction error was 0.32 mm for the Finite Element Method and 0.75 mm for the Dentofacial Planner Plus. Overall, we were able to demonstrate the new procedure's superior validity and quality of visualization. In addition to profile prediction, the procedure allows a differentiated three-dimensional assessment of esthetically important regions such as the cheeks, nasolabial folds and the nasal wings. Additional X-radiation is not necessary in this risk-free and stress-free procedure. CONCLUSION: Three-dimensional soft tissue prediction employing finite element modeling is a useful aid for implementing esthetically-optimized treatment planning.