Atherosusceptible Shear Stress Activates Endoplasmic Reticulum Stress to Promote Endothelial Inflammation.

Atherosclerosis impacts arteries where disturbed blood flow renders the endothelium susceptible to inflammation. Cytokine activation of endothelial cells (EC) upregulates VCAM-1 receptors that target monocyte recruitment to atherosusceptible regions. Endoplasmic reticulum (ER) stress elicits EC dysregulation in metabolic syndrome. We hypothesized that ER plays a central role in mechanosensing of atherosusceptible shear stress (SS) by signaling enhanced inflammation. Aortic EC were stimulated with low-dose TNFα (0.3 ng/ml) in a microfluidic channel that produced a linear SS gradient over a 20mm field ranging from 0-16 dynes/cm2. High-resolution imaging of immunofluorescence along the monolayer provided a continuous spatial metric of EC orientation, markers of ER stress, VCAM-1 and ICAM-1 expression, and monocyte recruitment. VCAM-1 peaked at 2 dynes/cm2 and decreased to below static TNFα-stimulated levels at atheroprotective-SS of 12 dynes/cm2, whereas ICAM-1 rose to a maximum in parallel with SS. ER expansion and activation of the unfolded protein response also peaked at 2 dynes/cm2, where IRF-1-regulated VCAM-1 expression and monocyte recruitment also rose to a maximum. Silencing of PECAM-1 or key ER stress genes abrogated SS regulation of VCAM-1 transcription and monocyte recruitment. We report a novel role for ER stress in mechanoregulation at arterial regions of atherosusceptible-SS inflamed by low-dose TNFα

Natural variability in a stable, 1000-yr global coupled climate-carbon cycle simulation

Author Posting. © American Meteorological Society 2006. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 19 (2006): 3033–3054, doi:10.1175/JCLI3783.1.A new 3D global coupled carbon–climate model is presented in the framework of the Community Climate System Model (CSM-1.4). The biogeochemical module includes explicit land water–carbon coupling, dynamic carbon allocation to leaf, root, and wood, prognostic leaf phenology, multiple soil and detrital carbon pools, oceanic iron limitation, a full ocean iron cycle, and 3D atmospheric CO2 transport. A sequential spinup strategy is utilized to minimize the coupling shock and drifts in land and ocean carbon inventories. A stable, 1000-yr control simulation [global annual mean surface temperature ±0.10 K and atmospheric CO2 ± 1.2 ppm (1σ)] is presented with no flux adjustment in either physics or biogeochemistry. The control simulation compares reasonably well against observations for key annual mean and seasonal carbon cycle metrics; regional biases in coupled model physics, however, propagate clearly into biogeochemical error patterns. Simulated interannual-to-centennial variability in atmospheric CO2 is dominated by terrestrial carbon flux variability, ±0.69 Pg C yr−1 (1σ global net annual mean), which in turn reflects primarily regional changes in net primary production modulated by moisture stress. Power spectra of global CO2 fluxes are white on time scales beyond a few years, and thus most of the variance is concentrated at high frequencies (time scale 20 yr), global net ocean CO2 flux is strongly anticorrelated (0.7–0.95) with the net CO2 flux from land; the ocean tends to damp (20%–25%) slow variations in atmospheric CO2 generated by the terrestrial biosphere. The intrinsic, unforced natural variability in land and ocean carbon storage is the “noise” that complicates the detection and mechanistic attribution of contemporary anthropogenic carbon sinks.This work was supported by NCAR, NSF ATM-9987457, NASA EOS-IDS Grant NAG5-9514, NASA Carbon Cycle Program Grant NAG5-11200, Lawrence Berkeley National Laboratory LDRD, and the WHOI Ocean and Climate Change Institute

Misdiagnosis, Mistreatment, and Harm - When Medical Care Ignores Social Forces.

The Case Studies in Social Medicine demonstrate that when physicians use only biologic or individual behavioral interventions to treat diseases that stem from or are exacerbated by social factors, we risk harming the patients we seek to serve

Drawbacks and benefits associated with inter-organizational collaboration along the discovery-development-delivery continuum: a cancer research network case study

BACKGROUND: The scientific process around cancer research begins with scientific discovery, followed by development of interventions, and finally delivery of needed interventions to people with cancer. Numerous studies have identified substantial gaps between discovery and delivery in health research. Team science has been identified as a possible solution for closing the discovery to delivery gap; however, little is known about effective ways of collaborating within teams and across organizations. The purpose of this study was to determine benefits and drawbacks associated with organizational collaboration across the discovery-development-delivery research continuum. METHODS: Representatives of organizations working on cancer research across a state answered a survey about how they collaborated with other cancer research organizations in the state and what benefits and drawbacks they experienced while collaborating. We used exponential random graph modeling to determine the association between these benefits and drawbacks and the presence of a collaboration tie between any two network members. RESULTS: Different drawbacks and benefits were associated with discovery, development, and delivery collaborations. The only consistent association across all three was with the drawback of difficulty due to geographic differences, which was negatively associated with collaboration, indicating that those organizations that had collaborated were less likely to perceive a barrier related to geography. The benefit, enhanced access to other knowledge, was positive and significant in the development and delivery networks, indicating that collaborating organizations viewed improved knowledge exchange as a benefit of collaboration. ‘Acquisition of additional funding or other resources’ and ‘development of new tools and methods’ were negatively significantly related to collaboration in these networks. So, although improved knowledge access was an outcome of collaboration, more tangible outcomes were not being realized. In the development network, those who collaborated were less likely to see ‘enhanced influence on treatment and policy’ and ‘greater quality or frequency of publications’ as benefits of collaboration. CONCLUSION: With the exception of the positive association between knowledge transfer and collaboration and the negative association between geography and collaboration, the significant relationships identified in this study all reflected challenges associated with inter-organizational collaboration. Understanding network structures and the perceived drawbacks and benefits associated with collaboration will allow researchers to build and funders to support successful collaborative teams and perhaps aid in closing the discovery to delivery gap

Can a single image processing algorithm work equally well across all phases of DCE-MRI?

Image segmentation and registration are said to be challenging when applied to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes rapid changes in intensity in the region of interest and elsewhere, which can lead to false positive predictions for segmentation tasks and confound the image registration similarity metric. While it is widely assumed that contrast changes increase the difficulty of these tasks, to our knowledge no work has quantified these effects. In this paper we examine the effect of training with different ratios of contrast enhanced (CE) data on two popular tasks: segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and VTN. We experimented further by strategically using the available datasets through pretraining and fine tuning with different splits of data. We found that to create a generalisable model, pretraining with CE data and fine tuning with non-CE data gave the best result. This interesting find could be expanded to other deep learning based image processing tasks with DCE-MRI and provide significant improvements to the models performance

Peripheral quantitative computed tomography (pQCT) predicts humeral diaphysis torsional mechanical properties with good short-term precision.

Peripheral quantitative computed tomography (pQCT) is a popular tool for non-invasively estimating bone mechanical properties. Previous studies have demonstrated pQCT provides precise estimates that are good predictors of actual bone mechanical properties at popular distal imaging sites (tibia and radius). The predictive ability and precision of pQCT at more proximal sites remains unknown. The aim of the current study was to explore the predictive ability and short-term precision of pQCT estimates of mechanical properties of the midshaft humerus, a site gaining popularity for exploring the skeletal benefits of exercise. Predictive ability was determined ex vivo by assessing the ability of pQCT-derived estimates of torsional mechanical properties in cadaver humeri (density-weighted polar moment of inertia [IP] and polar Strength Strain Index [SSIP]) to predict actual torsional properties. Short-term precision was assessed in vivo by performing six repeat pQCT scans at the level of the midshaft humerus in 30 young, healthy individuals (degrees of freedom = 150), with repeat scans performed by the same and different testers and on the same and different days to explore the influences of different testers and time between repeat scans on precision errors. IP and SSIP both independently predicted at least 90% of the variance in ex vivo midshaft humerus mechanical properties in cadaveric bones. Overall values for relative precision error (root mean squared coefficients of variation) for in vivo measures of IP and SSIP at the midshaft humerus were less than 1.5% and were not influenced by pQCT assessments being performed by different testers or on different days. These data indicate that pQCT provides very good prediction of midshaft humerus mechanical properties with good short-term precision, with measures being robust against the influences of different testers and time between repeat scans

Prediction of individual probabilities of livebirth and multiple birth events following in vitro fertilization (IVF): a new outcomes counselling tool for IVF providers and patients using HFEA metrics

In vitro fertilization (IVF) has become a standard treatment for subfertility after it was demonstrated to be of value to humans in 1978. However, the introduction of IVF into mainstream clinical practice has been accompanied by concerns regarding the number of multiple gestations that it can produce, as multiple births present significant medical consequences to mothers and offspring. When considering IVF as a treatment modality, a balance must be set between the chance of having a live birth and the risk of having a multiple birth. As IVF is often a costly decision for patients—financially, medically, and emotionally—there is benefit from estimating a patient’s specific chance that IVF could result in a birth as fertility treatment options are contemplated. Historically, a patient’s “chance of success” with IVF has been approximated from institution-based statistics, rather than on the basis of any particular clinical parameter (except age). Furthermore, the likelihood of IVF resulting in a twin or triplet outcome must be acknowledged for each patient, given the known increased complications of multiple gestation and consequent increased risk of poor birth outcomes. In this research, we describe a multivariate risk assessment model that incorporates metrics adapted from a national 7.5-year sampling of the Human Fertilisation & Embryology Authority (HFEA) dataset (1991–1998) to predict reproductive outcome (including estimation of multiple birth) after IVF. To our knowledge, http://www.formyodds.com is the first Software-as-a-Service (SaaS) application to predict IVF outcome. The approach also includes a confirmation functionality, where clinicians can agree or disagree with the computer-generated outcome predictions. It is anticipated that the emergence of predictive tools will augment the reproductive endocrinology consultation, improve the medical informed consent process by tailoring the outcome assessment to each patient, and reduce the potential for adverse outcomes with IVF

Nafion-stabilised platinum nanoparticles supported on titanium nitride: An efficient and durable electrocatalyst for phosphoric acid based polymer electrolyte fuel cells

Nafion derived platinum nanoparticles were produced and successfully anchored on titanium nitride (TiN) support (Pt/TiN) and its suitability for phosphoric acid based polymer electrolyte membrane fuel cells is reported. Electrochemical cycling of Nafion stabilised Pt/TiN electrocatalyst exhibits good stability, durability and better electrocatalytic activity than the traditionally employed carbon supported Pt (Pt/C). Platinum supported on TiN exhibits better oxygen reduction reaction (ORR) activity as compared to carbon black (Vulcan XC 72). Nafion stabilised Pt/TiN shows a positive shift of 20 mV in half-wave potential measured from ORR polarisation curve in relation to Pt/C. Nafion stabilised Pt/TiN shows approximately two-fold increase in mass and specific activities than the Pt/C calculated from ORR data at 0.9 V. The improved durability of Pt/TiN catalyst arises from Nafion layer surrounding the Pt nanoparticles and corrosion resistant TiN support. Transition metal nitride based electrocatalysts are more active for cathode due to synergistic effect, which is observed in oxygen reduction reaction.Web of Scienc