Identification of diagnostic serum protein profiles of glioblastoma patients.

Diagnosis of a glioblastoma (GBM) is triggered by the onset of symptoms and is based on cerebral imaging and histological examination. Serum-based biomarkers may support detection of GBM. Here, we explored serum protein concentrations of GBM patients and used data mining to explore profiles of biomarkers and determine whether these are associated with the clinical status of the patients. Gene and protein expression data for astrocytoma and GBM were used to identify secreted proteins differently expressed in tumors and in normal brain tissues. Tumor expression and serum concentrations of 14 candidate proteins were analyzed for 23 GBM patients and nine healthy subjects. Data-mining methods involving all 14 proteins were used as an initial evaluation step to find clinically informative profiles. Data mining identified a serum protein profile formed by BMP2, HSP70, and CXCL10 that enabled correct assignment to the GBM group with specificity and sensitivity of 89 and 96%, respectively (p < 0.0001, Fischer's exact test). Survival for more than 15 months after tumor resection was associated with a profile formed by TSP1, HSP70, and IGFBP3, enabling correct assignment in all cases (p < 0.0001, Fischer's exact test). No correlation was found with tumor size or age of the patient. This study shows that robust serum profiles for GBM may be identified by data mining on the basis of a relatively small study cohort. Profiles of more than one biomarker enable more specific assignment to the GBM and survival group than those based on single proteins, confirming earlier attempts to correlate single markers with cancer. These conceptual findings will be a basis for validation in a larger sample size.

Simple tricks for improving pattern-based information extraction from the biomedical literature.

BACKGROUND: Pattern-based approaches to relation extraction have shown very good results in many areas of biomedical text mining. However, defining the right set of patterns is difficult; approaches are either manual, incurring high cost, or automatic, often resulting in large sets of noisy patterns. RESULTS: We propose several techniques for filtering sets of automatically generated patterns and analyze their effectiveness for different extraction tasks, as defined in the recent BioNLP 2009 shared task. We focus on simple methods that only take into account the complexity of the pattern and the complexity of the texts the patterns are applied to. We show that our techniques, despite their simplicity, yield large improvements in all tasks we analyzed. For instance, they raise the F-score for the task of extraction gene expression events from 24.8% to 51.9%. CONCLUSIONS: Already very simple filtering techniques may improve the F-score of an information extraction method based on automatically generated patterns significantly. Furthermore, the application of such methods yields a considerable speed-up, as fewer matches need to be analysed. Due to their simplicity, the proposed filtering techniques also should be applicable to other methods using linguistic patterns for information extraction.

Treatment of avascular necrosis of femoral head with vascular pedicle iliac crest grafting

20 patiens avascular necrosis of femoral hip had been treated with vascular pedicle iliac crest grafting same side from 11/2002 - 1/2004. Follow up, longest is 52 weeks, the earliest is 12 weeks. Average age was 38,5. Presumed risk factors were excessive use of alcohol (14/20), the use of corticosteroid prolong (3/20), no specific (3/20). 18 good result patients, 2 not good result patients (both in Ficat IV before operation). None infection, inguinal hernia and limit range of motion of hip. One patient had weak quadriceps (the complete recover after 6 weeks). 10 loss sensation lateral thigh (back normal after average 2 weeks). 2 loss sensation in thigh (back normal after average 2 weeks)