Secretome-based proteomics reveals sulindac-modulated proteins released from colon cancer cells

Although experiments in rodents and human population-based studies have demonstrated the efficacy of nonsteroidal anti-inflammatory drugs (NSAIDs) such as sulindac in colorectal cancer (CRC) prevention, a detailed knowledge of the underlying mechanism of action of this drug is limited. To better understand the chemopreventitive effects of sulindac, especially early sulindac-induced apoptotic events, we used the CRC cell line LIM1215 as an experimental model, focusing on proteins secreted into the LIM1215 culture medium - i.e., the secretome. This subproteome comprises both soluble-secreted proteins and exosomes (30-100?nm diameter membrane vesicles released by several cell types). Selected secretome proteins whose expression levels were dysregulated by 1?mM sulindac treatment over 16?h were analyzed using 2-D DIGE, cytokine array, Western blotting, and MS. Overall, 150 secreted proteins were identified, many of which are implicated in molecular and cellular functions such as cell proliferation, differentiation, adhesion, invasion, angiogenesis, metastasis, and apoptosis. Our secretome-based proteomic studies have identified several secreted modulators of sulindac-induced apoptosis action (e.g., Mac-2 binding protein, Alix, 14-3-3 isoforms, profilin-1, calumenin/Cab45 precursors, and the angiogenic/tumor growth factors interleukin 8 (IL-8) and growth related oncogene (GRO-α)) that are likely to improve our understanding of the chemopreventitive action of this NSAID in CRC.     

Analog and digital circuits using organic thin-film transistors onpolyester substrates

We have fabricated and characterized analog and digital circuits using organic thin-film transistors on polyester film substrates. These are the first reported dynamic results for organic circuits fabricated on polyester substrates. The high-performance pentacene transistors yield circuits with the highest reported clock frequencies for organic circuits     

Identifying axial load patterns using space frame FEMs and measured vibration data

The axial loading of a space frame may need to be quantified, perhaps for improvement of a finite element model (FEM) to better represent the structural dynamics or to ascertain how close the structure is to buckling. The coexistence of compressive and tensile forces in a space frame causes certain frequencies to increase with respect to load while others decrease. This intricate behaviour has been modelled in the FEM of a bi-tetrahedral space frame through consideration of the geometric stiffness, which accounts for stiffness changes in the loaded members. Updating the load pattern in the FEM using Newton's method (traditional sensitivity-based model updating) brings the model frequencies closer to those physically measured from the real bi-tetrahedral frame and thus provides identification of the axial loads. This load pattern is a predetermined set of frame axial forces in equilibrium. Such a constraint means that the extent of loading can be described by just one scalar updating parameter, an improvement upon former methods that updated member forces as independent parameters. When compared to the loads measured using strain gauges, the loads identified by model updating are seen to offer approximations of the actual loading. Difficulties such as modelling joint behaviour are discussed. The present work extends a series of numerical studies on load updating published by the authors by offering a demonstration of load pattern identification using physically measured vibration data from a real space frame.     

Nitrite levels in breath condensate of patients with cystic fibrosis is elevated in contrast to exhaled nitric oxide

BACKGROUND: Nitric oxide (NO) is released by activated macrophages, neutrophils, and stimulated bronchial epithelial cells. Exhaled NO has been shown to be increased in patients with asthma and has been put forward as a marker of airways inflammation. However, we have found that exhaled NO is not raised in patients with cystic fibrosis, even during infective pulmonary exacerbation. One reason for this may be that excess airway secretions may prevent diffusion of gaseous NO into the airway lumen. We hypothesised that exhaled NO may not reflect total NO production in chronically suppurative airways and investigated nitrite as another marker of NO production. METHODS: Breath condensate nitrite concentration and exhaled NO levels were measured in 21 clinically stable patients with cystic fibrosis of mean age 26 years and mean FEV1 57% and 12 healthy normal volunteers of mean age 31 years. Breath condensate was collected with a validated method which excluded saliva and nasal air contamination and nitrite levels were measured using the Griess reaction. Exhaled NO was measured using a sensitive chemiluminescence analyser (LR2000) at an exhalation rate of 250 ml/s. Fourteen patients with cystic fibrosis had circulating plasma leucocyte levels and differential analysis performed on the day of breath collection. RESULTS: Nitrite levels were significantly higher in patients with cystic fibrosis than in normal subjects (median 1.93 microM compared with 0.33 microM). This correlated positively with circulating plasma leucocytes and neutrophils (r = 0.6). In contrast, exhaled NO values were not significantly different from the normal range (median 3.8 ppb vs 4.4 ppb). There was no correlation between breath condensate nitrite and lung function and between breath condensate nitrite and exhaled NO. CONCLUSIONS: Nitrite levels in breath condensate were raised in stable patients with cystic fibrosis in contrast to exhaled NO. This suggests that nitrite levels may be a more useful measure of NO production and possibly airways inflammation in suppurative airways and that exhaled NO may not reflect total NO production.     

Ascites and hydrothorax in association with struma ovarii

The occurrence of first cousin couples was 2.90/00 in Hungary involving three different population samples (about 2,000, 1,000 and 2,000 women after delivery in the capital, two cities, and a city with agricultural characteristics, respectively).     

Feedback for the Test Weary.

Comments on an article by L. Cronbach (see record 2005-11080-002) which noted that repetitive, lengthy, and exhausting psychological testing with no feedback produces discouragement and hostility in a job applicant. As one step toward dealing with this problem, many consulting firms regularly include a provision for "feedback" to the applicant being assessed. The goal is to make the assessment experience valuable to the applicant as well as to the employer. The atmosphere is changed from one of testing on a pass-fail basis to one of exploring the possible matches of man and position in terms of the needs of both. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

James F. T. Bugental (1915-2008).

James F. T. Bugental died peacefully at age 92 at his Petaluma, California, home on September 18, 2008. Jim was a leading psychotherapist and a founding father, with Abraham Maslow and others, of humanistic psychology, or the "third force" (in contrast to psychoanalysis and behaviorism). Jim was also the creator, along with Rollo May, of existential-humanistic psychotherapy. Jim was born in Fort Wayne, Indiana, on Christmas Day in 1915. Jim earned his doctorate in 1948 from Ohio State University, where he was influenced by Victor Raimy and George Kelly. After a brief time on the University of California, Los Angeles (UCLA) faculty in psychology, Jim resigned in 1953 to found the first group practice of psychotherapy, Psychological Service Associates, with Alvin Lasko. With Abraham Maslow and others, Jim was a cofounder of the Journal of Humanistic Psychology (JHP) and the Association for Humanistic Psychology in 1961. Jim also wrote many books on the topic of psychotherapy during his lifetime. Jim was a great and bold spirit--his many writings and teachings are cherished today widely, and the field of psychology is much richer for his efforts. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Depression detection from sMRI and rs-fMRI images using machine learning

Major Depression Disorder (MDD) is a common mental disorder that negatively affects many people’s lives worldwide. Developing an automated method to find useful diagnostic biomarkers from brain imaging data would help clinicians to detect MDD in its early stages. Depression is known to be a brain connectivity disorder problem. In this paper, we present a brain connectivity-based machine learning (ML) workflow that utilizes similarity/dissimilarity of spatial cubes in brain MRI images as features for depression detection. The proposed workflow provides a unified framework applicable to both structural MRI images and resting-state functional MRI images. Several cube similarity measures have been explored, including Pearson or Spearman correlations, Minimum Distance Covariance, or inverse of Minimum Distance Covariance. Discriminative features from the cube similarity matrix are chosen with the Wilcoxon rank-sum test. The extracted features are fed into machine learning classifiers to train MDD prediction models. To address the challenge of data imbalance in MDD detection, oversampling is performed to balance the training data. The proposed workflow is evaluated through experiments on three independent public datasets, all imbalanced, of structural MRI and resting-state fMRI images with depression labels. Experimental results show good performance on all three datasets in terms of prediction accuracy, specificity, sensitivity, and area under the Receiver Operating Characteristic (ROC) curve. The use of features from both structured MRI and resting state functional MRI is also investigated.