Carboxypeptidase O is a Lipid Droplet-associated Enzyme Able to Cleave both Acidic and Polar C-terminal Amino Acids

Carboxypeptidase O (CPO) is a member of the M14 family of metallocarboxypeptidases with a preference for the cleavage of C-terminal acidic amino acids. CPO is largely expressed in the small intestine, although it has been detected in other tissues such as the brain and ovaries. CPO does not contain a prodomain, nor is it strongly regulated by pH, and hence appears to exist as a constitutively active enzyme. The goal of this study was to investigate the intracellular distribution and activity of CPO in order to predict physiological substrates and function. The distribution of CPO, when expressed in MDCK cells, was analyzed by immunofluorescence microscopy. Soon after addition of nutrient-rich media, CPO was found to associate with lipid droplets, causing an increase in lipid droplet quantity. As media became depleted, CPO moved to a broader ER distribution, no longer impacting lipid droplet numbers. Membrane cholesterol levels played a role in the distribution and in vitro enzymatic activity of CPO, with cholesterol enrichment leading to decreased lipid droplet association and enzymatic activity. The ability of CPO to cleave C-terminal amino acids within the early secretory pathway (in vivo) was examined using Gaussia luciferase as a substrate, C-terminally tagged with variants of an ER retention signal. While no effect of cholesterol was observed, these data show that CPO does function as an active enzyme within the ER where it removes C-terminal glutamates and aspartates, as well as a number of polar amino acids

Propagation Failure in Excitable Media

We study a mechanism of pulse propagation failure in excitable media where stable traveling pulse solutions appear via a subcritical pitchfork bifurcation. The bifurcation plays a key role in that mechanism. Small perturbations, externally applied or from internal instabilities, may cause pulse propagation failure (wave breakup) provided the system is close enough to the bifurcation point. We derive relations showing how the pitchfork bifurcation is unfolded by weak curvature or advective field perturbations and use them to demonstrate wave breakup. We suggest that the recent observations of wave breakup in the Belousov-Zhabotinsky reaction induced either by an electric field or a transverse instability are manifestations of this mechanism.Comment: 8 pages. Aric Hagberg: http://cnls.lanl.gov/~aric; Ehud Meron:http://www.bgu.ac.il/BIDR/research/staff/meron.htm

Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients

Objective: Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. Methods: For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. Results: The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. Conclusion: This study provides meaningful information for the development of radiomics based image markers in NACT response prediction

Evaluating the Effectiveness of 2D and 3D Features for Predicting Tumor Response to Chemotherapy

2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1595 and 1403 features for the 3D and 2D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM) based prediction models were developed and optimized for each feature set. 5-fold cross-validation was used to assess the performance of each individual model. The results show that the 2D feature-based model achieved an AUC (area under the ROC curve [receiver operating characteristic]) of 0.84+-0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86+-0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91+-0.01. This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future

Designing a complex intervention for dementia case management in primary care

Background: Community-based support will become increasingly important for people with dementia, but currently services are fragmented and the quality of care is variable. Case management is a popular approach to care co-ordination, but evidence to date on its effectiveness in dementia has been equivocal. Case management interventions need to be designed to overcome obstacles to care co-ordination and maximise benefit. A successful case management methodology was adapted from the United States (US) version for use in English primary care, with a view to a definitive trial. Medical Research Council guidance on the development of complex interventions was implemented in the adaptation process, to capture the skill sets, person characteristics and learning needs of primary care based case managers. Methods: Co-design of the case manager role in a single NHS provider organisation, with external peer review by professionals and carers, in an iterative technology development process. Results: The generic skills and personal attributes were described for practice nurses taking up the case manager role in their workplaces, and for social workers seconded to general practice teams, together with a method of assessing their learning needs. A manual of information material for people with dementia and their family carers was also created using the US intervention as its source. Conclusions: Co-design produces rich products that have face validity and map onto the complexities of dementia and of health and care services. The feasibility of the case manager role, as described and defined by this process, needs evaluation in ‘real life’ settings

Talk the talk, walk the walk: Defining Critical Race Theory in research

Over the last decade there has been a noticeable growth in published works citing Critical Race Theory (CRT). This has led to a growth in interest in the UK of practical research projects utilising CRT as their framework. It is clear that research on 'race' is an emerging topic of study. What is less visible is a debate on how CRT is positioned in relation to methodic practice, substantive theory and epistemological underpinnings. The efficacy of categories of data gathering tools, both traditional and non-traditional is a discussion point here to explore the complexities underpinning decisions to advocate a CRT framework. Notwithstanding intersectional issues, a CRT methodology is recognisable by how philosophical, political and ethical questions are established and maintained in relation to racialised problematics. This paper examines these tensions in establishing CRT methodologies and explores some of the essential criteria for researchers to consider in utilising a CRT framework. © 2012 Copyright Taylor and Francis Group, LLC

Particulate air pollution and survival in a COPD cohort

<p>Abstract</p> <p>Background</p> <p>Several studies have shown cross-sectional associations between long term exposure to particulate air pollution and survival in general population or convenience cohorts. Less is known about susceptibility, or year to year changes in exposure. We investigated whether particles were associated with survival in a cohort of persons with COPD in 34 US cities, eliminating the usual cross-sectional exposure and treating PM₁₀as a within city time varying exposure.</p> <p>Methods</p> <p>Using hospital discharge data, we constructed a cohort of persons discharged alive with chronic obstructive pulmonary disease using Medicare data between 1985 and 1999. 12-month averages of PM₁₀were merged to the individual annual follow up in each city. We applied Cox's proportional hazard regression model in each city, with adjustment for individual risk factors.</p> <p>Results</p> <p>We found significant associations in the survival analyses for single year and multiple lag exposures, with a hazard ratio for mortality for an increase of 10 μg/m³PM₁₀over the previous 4 years of 1.22 (95% CI: 1.17–1.27).</p> <p>Conclusion</p> <p>Persons discharged alive for COPD have substantial mortality risks associated with exposure to particles. The risk is evident for exposure in the previous year, and higher in a 4 year distributed lag model. These risks are significantly greater than seen in time series analyses.</p