A prospective, randomized, double-blind, placebo-controlled multi-centre study on the efficacy and safety of sublingual immunotherapy (SLIT) in children with seasonal allergic rhinoconjunctivitis to grass pollen.

BACKGROUND: Especially in childhood, sublingual immunotherapy (SLIT) could offer advantages over subcutaneous therapy. However, limited data on its efficacy is available. METHODS: In four German centres 97 children (age 3-14 years) with allergic rhinoconjunctivitis to grass pollen were enrolled in a prospective, double-blind trial comparing SLIT (Pangramin SLIT; ALK-SCHERAX, 0.5 microg major allergens, three times per week, 32 months) with placebo. Primary endpoint was a multiple symptom-medication score for changes in seasonal diary entries between the first and third year of the study (SLIT n=39; placebo n=38). RESULTS: The multiple symptom-medication score was significantly reduced by SLIT to 77.3% of the placebo group (P=0.0498). The subsequent analysis of the single endpoints did not reveal significant differences for symptom scores in favour of SLIT (85.1% of placebo group; P=0.22). However, the medication score improved significantly (67.1% of placebo group; P=0.0025). Furthermore, secondary endpoints assessing in vivo immune responses did not differ significantly between the groups. However, retrospective analysis showed some inhomogeneity for clinical and in vitro parameters at the beginning of the study. Allergic side effects with possible relation to the study drug were reported in both groups (SLIT 49%, placebo 27%, P=0.026). CONCLUSION: Our study indicates that SLIT had a positive effect on the reduction of a multiple symptom-medication score, mainly by significantly reducing rescue medication use, but had no significant effect on symptoms alone in children with rhinoconjunctivitis to grass pollen compared with a placebo.

Effect of immunotherapy on eosinophil activation in pollen sensitive children.

Immunotherapy is a well documented method for treatment of children with allergic airway diseases and allergic rhinitis. The aim of this investigation was to establish the effect of immunotherapy on the presence and activation state of eosinophils in pollen sensitive patients, measured by eosinophil counts and their degranulation product in serum, eosinophil cationic protein in comparison with the (specific) IgE concentration in pollen sensitive patients. In both groups intra-individual variations in (specific) IgE concentrations, due to increase of pollen exposure, were not observed. Blood eosinophil counts and serum ECP concentrations showed consistent results after starting immunotherapy. Overall release of ECP per eosinophil remained also unaffected after the start of immunotherapy. Results of ECP concentrations and eosinophil counts in the subjects' group did not show any deviation when compared with the control subjects' group. It is concluded that the laboratory parameters are not helpful in monitoring the efficacy of immunotherapy in patients with seasonal rhinitis.

Automated histometry in quantitative prostate pathology.

OBJECTIVE: To review progress on the development of machine vision and image understanding in prostate tissue histology and to discuss the problems and opportunities afforded to pathology through the use of these techniques. STUDY DESIGN: A variety of concepts in machine vision are explored, and methodologies are described that have been developed to deal with the complexities of histologic imagery. The theory of human vision and its impact on machine vision are discussed. Software has been specifically developed for the analysis of prostate histology, allowing accurate gland segmentation, basal cell identification and measurement of vascularization within lesions. RESULTS: Image interpretation can be achieved using knowledge-based image analysis and the application of local object-oriented processing. This successfully allows an automated quantitative analysis of histologic morphology in the diagnosis of prostate intraepithelial neoplasia and invasive prostatic cancer. The use of low-power image scanning, based on textural or n-gram mapping, permits the development of fully automated devices for the rapid detection of tissue abnormalities. High-power, knowledge-guided scene segmentation can be carried out for the quantitative analysis of cellular features and the objective grading of the lesion. CONCLUSION: Automated tissue section scanning and image interpretation is now possible and holds much promise in prostate pathology and other diagnostically demanding areas. Issues of standardization still need to be addressed, but the development of such systems will undoubtedly enhance our diagnostic capabilities through the automation of time-consuming procedures and the quantitative evaluation of disease processes.

Knowledge-based image analysis in the precursors of prostatic adenocarcinoma.

OBJECTIVES: It is the objective of this study to present the use of knowledge-guided procedures in quantitative image analysis and interpretation in histopathology. METHODS: The knowledge-guided procedures were implemented in the form of N-gram encoding methods for the search and detection of areas of atypicality or abnormality in histopathologic sections; they were implemented as expert system for automated scene segmentation based on an associative network with frames at each node. The extraction of histometric features from the basal cell layer of prostatic lesions is presented as an example of automated image interpretation. RESULTS: Rapid search algorithms for lesion detection were able to identify approximately 90% of areas labelled as atypical or abnormal by visual assessment, in lesions of colon, prostate and breast. Automated segmentation of very complex histopathologic imagery was possible with a success rate of approximately 80-90%, in sections of prostatic and colonic lesions. Histometry of the deterioration of the basal cell layer in prostatic lesions provided a monotonic trend curve suitable for the measurement of progression or regression. CONCLUSIONS: Knowledge-guided procedures bring external information, not offered by the imagery itself, to bear on image processing and image analytic methods. This has enabled automated analysis and interpretation of very complex imagery, such as from cribriform glands, resulting in quantitative diagnostic information.

Knowledge-guided segmentation of colorectal histopathologic imagery.

Scene segmentation in images of sections of colorectal glands is guided by a knowledge-based system. The construction of the knowledge file, processing sequences and computation of a crowding index for adenomatous glands are described. The histologic sections were stained by hematoxylin and eosin, and the image segmentation was difficult. The knowledge-based system has to determine, on the basis of context and logic, which detected object groups belong to which histologic components. Image segmentation was followed by a reconstruction process. Acceptable segmentation was attained in approximately 85% of the glands processed.