Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images.

BACKGROUND: Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted. METHODS: We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space. RESULTS: MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA. CONCLUSIONS: MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.

Characterization of Human, Ovine and Porcine Mesenchymal Stem Cells from Bone Marrow: Critical In Vitro Comparison with Regard to Humans.

For research and clinical use of stem cells, a suitable animal model is necessary. Hence, the aim of this study was to compare human-bone-marrow-derived mesenchymal stem cells (hBMSCs) with those from sheep (oBMSCs) and pigs (pBMSCs). The cells from these three species were examined for their self-renewal potential; proliferation potential; adhesion and migration capacity; adipogenic, osteogenic and chondrogenic differentiation potential; and cell morphology. There was no significant difference between hBMSCs and pBMSCs in terms of self-renewal potential or growth potential. The oBMSCs exhibited a significantly higher doubling time than hBMSCs from passage 7. The migration assay showed significant differences between hBMSCs and pBMSCs and oBMSCs-up to 30 min, hBMSCs were faster than both types and after 60 min faster than pBMSCs. In the adhesion assay, hBMSCs were significantly better than oBMSCs and pBMSCs. When differentiating in the direction of osteogenesis, oBMSCs and pBMSCs have shown a clearer osteogenic potential. In all three species, adipogenesis could only be evaluated qualitatively. The chondrogenic differentiation was successful in hBMSCs and pBMSCs in contrast to oBMSCs. It is also important to note that the cell size of pBMSCs was significantly smaller compared to hBMSCs. Finally, it can be concluded that further comparative studies are needed to draw a clear comparison between hBMSCs and pBMSCs/oBMSCs.

Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier.

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier's performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses.

Identifying biomarkers deciphering sepsis from trauma-induced sterile inflammation and trauma-induced sepsis.

Objective: The purpose of this study was to identify a panel of biomarkers for distinguishing early stage sepsis patients from non-infected trauma patients. Background: Accurate differentiation between trauma-induced sterile inflammation and real infective sepsis poses a complex life-threatening medical challenge because of their common symptoms albeit diverging clinical implications, namely different therapies. The timely and accurate identification of sepsis in trauma patients is therefore vital to ensure prompt and tailored medical interventions (provision of adequate antimicrobial agents and if possible eradication of infective foci) that can ultimately lead to improved therapeutic management and patient outcome. The adequate withholding of antimicrobials in trauma patients without sepsis is also important in aspects of both patient and environmental perspective. Methods: In this proof-of-concept study, we employed advanced technologies, including Matrix-Assisted Laser Desorption/Ionization (MALDI) and multiplex antibody arrays (MAA) to identify a panel of biomarkers distinguishing actual sepsis from trauma-induced sterile inflammation. Results: By comparing patient groups (controls, infected and non-infected trauma and septic shock patients under mechanical ventilation) at different time points, we uncovered distinct protein patterns associated with early trauma-induced sterile inflammation on the one hand and sepsis on the other hand. SYT13 and IL1F10 emerged as potential early sepsis biomarkers, while reduced levels of A2M were indicative of both trauma-induced inflammation and sepsis conditions. Additionally, higher levels of TREM1 were associated at a later stage in trauma patients. Furthermore, enrichment analyses revealed differences in the inflammatory response between trauma-induced inflammation and sepsis, with proteins related to complement and coagulation cascades being elevated whereas proteins relevant to focal adhesion were diminished in sepsis. Conclusions: Our findings, therefore, suggest that a combination of biomarkers is needed for the development of novel diagnostic approaches deciphering trauma-induced sterile inflammation from actual infective sepsis.

Influence of age on stem cells depends on the sex of the bone marrow donor.

Ageing is often accompanied by an increase in bone marrow fat together with reduced bone volume and diseases of the bone such as osteoporosis. As mesenchymal stem cells (MSCs) are capable of forming bone, cartilage and fat tissue, studying these cells is of great importance to understand the underlying mechanisms behind age-related bone diseases. However, inter-donor variation has been found when handling MSCs. Therefore, the aim of this study was to investigate the effects of donor age and sex by comparing in vitro characteristics of human bone marrow-derived MSCs (hBMSCs) from a large donor cohort (n = 175). For this, hBMSCs were analysed for CFU-F capacity, proliferation, differentiation capacity and surface antigen expression under standardized culture conditions. The results demonstrated a significantly reduced CFU-F number for hBMSCs of female compared to male donors. Furthermore, there was a significant decrease in the proliferation rate, adipogenic differentiation potential and cell surface expression of SSEA-4, CD146 and CD274 of hBMSCs with an increase in donor age. Interestingly, all these findings were exclusive to hBMSCs from female donors. Further research should focus on postmenopausal-related effects on hBMSCs, as the results imply a functional loss and immunophenotypic change of hBMSCs particularly in aged women.

Biotic and abiotic transformations of methyl tertiary butyl ether (MTBE).

BACKGROUND: Methyl tertiary butyl ether (MTBE) is a fuel additive which is used all over the world. In recent years it has often been found in groundwater, mainly in the USA, but also in Europe. Although MTBE seems to be a minor toxic, it affects the taste and odour of water at concentrations of < 30 microg/L. Although MTBE is often a recalcitrant compound, it is known that many ethers can be degraded by abiotic means. The aim of this study was to examine biotic and abiotic transformations of MTBE with respect to the particular conditions of a contaminated site (former refinery) in Leuna, Germany. METHODS: Groundwater samples from wells of a contaminated site were used for aerobic and anaerobic degradation experiments. The abiotic degradation experiment (hydrolysis) was conducted employing an ion-exchange resin and MTBE solutions in distilled water. MTBE, tertiary butyl formate (TBF) and tertiary butyl alcohol (TBA) were measured by a gas chromatograph with flame ionisation detector (FID). Aldehydes and organic acids were respectively analysed by a gas chromatograph with electron capture detector (ECD) and high-performance ion chromatography (HPIC). RESULTS AND DISCUSSION: Under aerobic conditions, MTBE was degraded in laboratory experiments. Only 4 of a total of 30 anaerobic experiments exhibited degradation, and the process was very slow. In no cases were metabolites detected, but a few degradation products (TBF, TBA and formic acid) were found on the site, possibly due to the lower temperatures in groundwater. The abiotic degradation of MTBE with an ion-exchange resin as a catalyst at pH 3.5 was much faster than hydrolysis in diluted hydrochloric acid (pH 1.0). CONCLUSION: Although the aerobic degradation of MTBE in the environment seems to be possible, the specific conditions responsible are widely unknown. Successful aerobic degradation only seems to take place if there is a lack of other utilisable compounds. However, MTBE is often accompanied by other fuel compounds on contaminated sites and anaerobic conditions prevail. MTBE is often recalcitrant under anaerobic conditions, at least in the presence of other carbon sources. The abiotic hydrolysis of MTBE seems to be of secondary importance (on site), but it might be possible to enhance it with catalysts. RECOMMENDATION AND OUTLOOK: MTBE only seems to be recalcitrant under particular conditions. In some cases, the degradation of MTBE on contaminated sites could be supported by oxygen. Enhanced hydrolysis could also be an alternative.