Refine
Has Fulltext
- yes (22)
Is part of the Bibliography
- yes (22) (remove)
Year of publication
Document Type
- Journal article (18)
- Doctoral Thesis (4)
Keywords
- classification (22) (remove)
Institute
- Klinik und Poliklinik für Unfall-, Hand-, Plastische und Wiederherstellungschirurgie (Chirurgische Klinik II) (3)
- Theodor-Boveri-Institut für Biowissenschaften (3)
- Institut für Pharmakologie und Toxikologie (2)
- Klinik und Poliklinik für Mund-, Kiefer- und Plastische Gesichtschirurgie (2)
- Pathologisches Institut (2)
- Institut für Anatomie und Zellbiologie (1)
- Institut für Geographie und Geologie (1)
- Institut für Humangenetik (1)
- Institut für Psychologie (1)
- Institut für diagnostische und interventionelle Neuroradiologie (ehem. Abteilung für Neuroradiologie) (1)
Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.
Converging evidence from controlled experiments suggests that the mere processing of a number and its attributes such as value or parity might affect free choice decisions between different actions. For example the spatial numerical associations of response codes (SNARC) effect indicates the magnitude of a digit to be associated with a spatial representation and might therefore affect spatial response choices (i.e., decisions between a "left" and a "right" option). At the same time, other (linguistic) features of a number such as parity are embedded into space and might likewise prime left or right responses through feature words [odd or even, respectively; markedness association of response codes (MARC) effect]. In this experiment we aimed at documenting such influences in a natural setting. We therefore assessed number space and parity space association effects by exposing participants to a fair distribution task in a card playing scenario. Participants drew cards, read out loud their number values, and announced their response choice, i.e., dealing it to a left vs. right player, indicated by Playmobil characters. Not only did participants prefer to deal more cards to the right player, the card's digits also affected response choices and led to a slightly but systematically unfair distribution, supported by a regular SNARC effect and counteracted by a reversed MARC effect. The experiment demonstrates the impact of SNARC- and MARC-like biases in free choice behavior through verbal and visual numerical information processing even in a setting with high external validity.
Background:
Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Furthermore, it has been demonstrated that ratings and tests seem to assess somewhat different constructs. The use of objective measures might thus yield valuable information for diagnosing ADHD. This study aims at evaluating the role of objective measures when trying to distinguish between individuals with ADHD and controls. Our sample consisted of children (n = 60) and adults (n = 76) diagnosed with ADHD and matched controls who completed self- and observer ratings as well as objective tasks. Diagnosis was primarily based on clinical interviews. A popular pattern recognition approach, support vector machines, was used to predict the diagnosis.
Results:
We observed relatively high accuracy of 79% (adults) and 78% (children) applying solely objective measures. Predicting an ADHD diagnosis using both subjective and objective measures exceeded the accuracy of objective measures for both adults (89.5%) and children (86.7%), with the subjective variables proving to be the most relevant.
Conclusions:
We argue that objective measures are more robust against rater bias and errors inherent in subjective measures and may be more replicable. Considering the high accuracy of objective measures only, we found in our study, we think that they should be incorporated in diagnostic procedures for assessing ADHD.
Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics.
Primary involvement of skeletal muscle is a very rare event in ALK-1 positive anaplastic large cell lymphoma (ALCL). We describe a case of a 10-year old boy presenting with a three week history of pain and a palpable firm swelling at the dorsal aspect of the left thigh. Histological examination of the lesion revealed a tumoral and diffuse polymorphic infiltration of the muscle by large lymphoid cells. Tumor cells displayed eccentric, lobulated "horse shoe" or "kidney-shape" nuclei. The cells showed immunohistochemical positivity for CD30, ALK-1, CD2, CD3, CD7, CD8, and Perforin. Fluorescence in situ hybridization analysis revealed a characteristic rearrangement of the ALK-1 gene in 2p23 leading to the diagnosis of ALK-1 positive ALCL. Chemotherapy according to the ALCL-99-NHL-BFM protocol was initiated and resulted in a complete remission after two cycles. This case illustrates the unusual presentation of a pediatric ALCL in soft tissue with a good response to chemotherapy.
Introduction
We characterized dermal innervation in patients with fibromyalgia syndrome (FMS) as potential contribution to small fiber pathology.
Methods
Skin biopsies of the calf were collected (86 FMS patients, 35 healthy controls). Skin was immunoreacted with antibodies against protein gene product 9.5, calcitonine gene-related peptide, substance P, CD31, and neurofilament 200 for small fiber subtypes. We assessed two skin sections per patient; on each skin section, two dermal areas (150 x 700 mu m each) were investigated for dermal nerve fiber length (DNFL).
Results
In FMS patients we found reduced DNFL of fibers with vessel contact compared to healthy controls (p<0.05). There were no differences for the other nerve fiber subtypes.
Discussion
We found less dermal nerve fibers in contact with blood vessels in FMS patients than in controls. The pathophysiological relevance of this finding is unclear, but we suggest the possibility of a relationship with impaired thermal tolerance commonly reported by FMS patients.
Background
The Goutallier Classification is a semi quantitative classification system to determine the amount of fatty degeneration in rotator cuff muscles. Although initially proposed for axial computer tomography scans it is currently applied to magnet-resonance-imaging-scans. The role for its clinical use is controversial, as the reliability of the classification has been shown to be inconsistent. The purpose of this study was to compare the semi quantitative MRI-based Goutallier Classification applied by 5 different raters to experimental MR spectroscopic quantitative fat measurement in order to determine the correlation between this classification system and the true extent of fatty degeneration shown by spectroscopy.
Methods
MRI-scans of 42 patients with rotator cuff tears were examined by 5 shoulder surgeons and were graduated according to the MRI-based Goutallier Classification proposed by Fuchs et al. Additionally the fat/water ratio was measured with MR spectroscopy using the experimental SPLASH technique. The semi quantitative grading according to the Goutallier Classification was statistically correlated with the quantitative measured fat/water ratio using Spearman’s rank correlation.
Results
Statistical analysis of the data revealed only fair correlation of the Goutallier Classification system and the quantitative fat/water ratio with R = 0.35 (p < 0.05). By dichotomizing the scale the correlation was 0.72. The interobserver and intraobserver reliabilities were substantial with R = 0.62 and R = 0.74 (p < 0.01).
Conclusion
The correlation between the semi quantitative MRI based Goutallier Classification system and MR spectroscopic fat measurement is weak. As an adequate estimation of fatty degeneration based on standard MRI may not be possible, quantitative methods need to be considered in order to increase diagnostic safety and thus provide patients with ideal care in regard to the amount of fatty degeneration. Spectroscopic MR measurement may increase the accuracy of the Goutallier classification and thus improve the prediction of clinical results after rotator cuff repair. However, these techniques are currently only available in an experimental setting.
Background and Objectives: Cycloid psychoses are characterized by polymorphic symptomatology with intraphasic bipolarity, a remitting and recurrent course and favourable prognosis. Perris and Brocicington (P&B) described the first set of operational criteria that were partly incorporated in ICD-10. The present study investigates psychopathological profiles according to the P&B criteria and the original descriptions by Leonhard, both against the background of the criteria from the prevailing international classification systems.
Methods: Eighty patients with psychotic disorders were recruited and assessed with various psychometric instruments at baseline and after six weeks of antipsychotic treatment in order to investigate the presence of cycloid psychoses according to Leonhard (LCP) and the effect of treatment with antipsychotics. The overlap between LCP and DSM-IV Brief Psychotic Disorder (BPD), ICD Acute Polymorphic Psychotic Disorder (APP) and P&B criteria was calculated.
Results: Using P&B criteria and a symptom checklist adapted from the original descriptions by Leonhard, 14 and 12 cases of cycloid psychosis were identified respectively reflecting a prevalence of 15-18%. Small though significant concordance rates were found between LCP and both DSM-BPD and ICD-APP. Concordance between LCP and P&B criteria was also significant, but modest.
Conclusions: This study demonstrates that LCP can be identified in a substantial number of patients with psychotic disorders. Cycloid psychoses are not adequately covered in current classification systems and criteria. Since they are demonstrated to have a specific psychopathological profile, relapsing course and favourable prognosis, it is advocated to include these psychoses in daily differential diagnostic procedures.
Knöcherne Verletzungen am Ellenbogen stehen bei Kindern und Jugendlichen nach Unterarm-, Unterschenkel- und Schlüsselbeinbrüchen an vierter Stelle. Von diesen ist die suprakondyläre Humerusfraktur mit ca. 60 Prozent (50 Prozent - 70 Prozent) die häufigste Fraktur. Bedeutend ist sie, weil es sich um eine gelenknahe Fraktur handelt, deren exakte Reposition und Fixation schwierig ist und Wachstumsfugen nicht tangiert werden dürfen. Es treten auch relativ häufig Nerven- und Gefäßläsionen, Gelenkfehlstellungen und Bewegungseinschränkungen sowie der Cubitus varus auf, die immer wieder erneut Anlaß zu Diskussionen über neue, verbesserte Therapiemaßnahmen geben. Das Bestreben, Komplikationen zu vermindern, hat in der Vergangenheit zu einer Vielzahl von Therapiemaßnahmen geführt. Erst 1998 einigte sich die Arbeitsgemeinschaft Kindertraumatologie der Deutschen Gesellschaft für Unfallchirurgie auf eine einheitliche Klassifikation der Frakturen, die im Vergleich zu den früher gebräuchlichen Klassifikationen, die Rotationsstellung, den wichtigsten Grund für die Entstehung für Fehlstellungen, mit berücksichtigt. Es wurden auch, nach der Auswertung einer retrospektiven deutschlandweiten Sammelstudie, Therapieempfehlungen nach Dislokations- und Rotationsgrad der neuen Klassifikation herausgegeben. Leider konnte man sich immer noch nicht auf einheitliche Bewertungskriterien einigen. In der Universitätsklinik Würzburg wurde bereits in den Jahren 1986 bis 1996 im weitesten Sinne nach diesen Richtlinien therapiert, da man frühzeitig die Bedeutung des Rotationsfehlers erkannt hatte. Im Allgemeinen Teil wird auf die speziellen Grundlagen eingegangen, die Besonderheiten der Ellenbogenregion und des wachsenden Skeletts erläutert, um das Entstehen der verschiedenen Komplikationen zu verdeutlichen. Der Spezielle Teil stellt die Auswertung der nachuntersuchten 80 von 136 Patienten, die von 1986 bis 1996 in der kinderchirurgischen Abteilung der Universität Würzburg behandelt wurden, von den allgemeinen Daten über die Klassifikationen, Therapiemethoden und Komplikationen detailliert dar. An Behandlungsmethoden kamen zwei konservative (Blount und Gips), die perkutane gekreuzte Kirschner-Draht-Osteosynsthese und die offene Reposition als Therapiemethoden zum Einsatz. Die perkutane Kirschner-Draht-Osteosynthese erzielte mit 94 Prozent Ideale und Gute Ergebnisse in der Bewertung nach Morger. Bei den konservativen Therapien wurden 80 Prozent mit ideal und gut bewertet. Das Ergebnis der offenen Repositionen lag mit 83 Prozent auch noch weit über dem deutschlandweiten Durchschnitt von 56 Prozent der Idealen Ergebnissen. Die größere Anzahl an schwierigen Fällen führten auch zu dem Auftreten einer relativ hohen Anzahl primärer Komplikationen wie Nerven- (22,5 Prozent) und Gefäßläsionen (5 Prozent), die jedoch fast alle innerhalb kurzer Zeit folgenlos ausheilten. In unserem Patientengut hatten fünf Patienten (6,25 Prozent) einen Cubitus varus. Schwerwiegende Komplikationen wie die Volkmann´sche Kontraktur traten nicht auf. In der Diskussion werden die eigenen Ergebnisse in Bezug zur deutschland-weiten Sammelstudie, zu Vorgängerarbeiten (Fälle von 1975 – 1985 und 1964 – 1974) und weiteren aktuellen Veröffentlichungen gebracht.
In this thesis, the development of a phylogenetic DNA microarray, the analysis of several gene expression microarray datasets and new approaches for improved data analysis and interpretation are described. In the first publication, the development and analysis of a phylogenetic microarray is presented. I could show that species detection with phylogenetic DNA microarrays can be significantly improved when the microarray data is analyzed with a linear regression modeling approach. Standard methods have so far relied on pure signal intensities of the array spots and a simple cutoff criterion was applied to call a species present or absent. This procedure is not applicable to very closely related species with high sequence similarity because cross-hybridization of non-target DNA renders species detection impossible based on signal intensities alone. By modeling hybridization and cross-hybridization with linear regression, as I have presented in this thesis, even species with a sequence similarity of 97% in the marker gene can be detected and distinguished from related species. Another advantage of the modeling approach over existing methods is that the model also performs well on mixtures of different species. In principle, also quantitative predictions can be made. To make better use of the large amounts of microarray data stored in public databases, meta-analysis approaches need to be developed. In the second publication, an explorative meta-analysis exemplified on Arabidopsis thaliana gene expression datasets is presented. Integrating datasets studying effects such as the influence of plant hormones, pathogens and different mutations on gene expression levels, clusters of similarly treated datasets could be found. From the clusters of pathogen-treated and indole-3-acetic acid (IAA) treated datasets, representative genes were selected which pointed to functions which had been associated with pathogen attack or IAA effects previously. Additionally, hypotheses about the functions of so far uncharacterized genes could be set up. Thus, this kind of meta-analysis could be used to propose gene functions and their regulation under different conditions. In this work, also primary data analysis of Arabidopsis thaliana datasets is presented. In the third publication, an experiment which was conducted to find out if microwave irradiation has an effect on the gene expression of a plant cell culture is described. During the first steps, the data analysis was carried out blinded and exploratory analysis methods were applied to find out if the irradiation had an effect on gene expression of plant cells. Small but statistically significant changes in a few genes were found and could be experimentally confirmed. From the functions of the regulated genes and a meta-analysis with publicly available microarray data, it could be suspected that the plant cell culture somehow perceived the irradiation as energy, similar to perceiving light rays. The fourth publication describes the functional analysis of another Arabidopsis thaliana gene expression dataset. The gene expression data of the plant tumor dataset pointed to a switch from a mainly aerobic, auxotrophic to an anaerobic and heterotrophic metabolism in the plant tumor. Genes involved in photosynthesis were found to be repressed in tumors; genes of amino acid and lipid metabolism, cell wall and solute transporters were regulated in a way that sustains tumor growth and development. Furthermore, in the fifth publication, GEPAT (Genome Expression Pathway Analysis Tool), a tool for the analysis and integration of microarray data with other data types, is described. It consists of a web application and database which allows comfortable data upload and data analysis. In later chapters of this thesis (publication 6 and publication 7), GEPAT is used to analyze human microarray datasets and to integrate results from gene expression analysis with other datatypes. Gene expression and comparative genomic hybridization data from 71 Mantle Cell Lymphoma (MCL) patients was analyzed and allowed proposing a seven gene predictor which facilitates survival predictions for patients compared to existing predictors. In this study, it was shown that CGH data can be used for survival predictions. For the dataset of Diffuse Large B-cell lymphoma (DLBCL) patients, an improved survival predictor could be found based on the gene expression data. From the genes differentially expressed between long and short surviving MCL patients as well as for regulated genes of DLBCL patients, interaction networks could be set up. They point to differences in regulation for cell cycle and proliferation genes between patients with good and bad prognosis.