Ruxolitinib in patients with polycythemia vera resistant and/or intolerant to hydroxyurea:European observational study

Background: Hydroxyurea (HU) is a commonly used first-line treatment in patients with polycythemia vera (PV). However, approximately 15%–24% of PV patients report intolerance and resistance to HU. Methods: This phase IV, European, real-world, observational study assessed the efficacy and safety of ruxolitinib in PV patients who were resistant and/or intolerant to HU, with a 24-month follow-up. The primary objective was to describe the profile and disease burden of PV patients. Results: In the 350 enrolled patients, 70% were &gt;60 years old. Most patients (59.4%) had received ≥1 phlebotomy in the 12 months prior to the first dose of ruxolitinib. Overall, 68.2% of patients achieved hematocrit control with 92.3% patients having hematocrit &lt;45% and 35.4% achieved hematologic remission at month 24. 85.1% of patients had no phlebotomies during the study. Treatment-related adverse events were reported in 54.3% of patients and the most common event was anemia (22.6%). Of the 10 reported deaths, two were suspected to be study drug-related. Conclusion: This study demonstrates that ruxolitinib treatment in PV maintains durable hematocrit control with a decrease in the number of phlebotomies in the majority of patients and was generally well tolerated.</p

Measuring macroscopic brain connections in vivo

Decades of detailed anatomical tracer studies in non-human animals point to a rich and complex organization of long-range white matter connections in the brain. State-of-the art in vivo imaging techniques are striving to achieve a similar level of detail in humans, but multiple technical factors can limit their sensitivity and fidelity. In this review, we mostly focus on magnetic resonance imaging of the brain. We highlight some of the key challenges in analyzing and interpreting in vivo connectomics data, particularly in relation to what is known from classical neuroanatomy in laboratory animals. We further illustrate that, despite the challenges, in vivo imaging methods can be very powerful and provide information on connections that is not available by any other means

Absorption and mobility of foliar-applied boron in soybean as affected by plant boron status and application as a polyol complex

In the present study (i) the impact of plant Boron (B) status on foliar B absorption and (ii) the effect of B complexation with polyols (sorbitol or mannitol) on B absorption and translocation was investigated. Soybean (Glycine max (L.) Meer.) plants grown in nutrient solution containing 0 μM, 10 μM, 30 μM or 100 μM 11B labelled boric acid (BA) were treated with 50 mM 10B labelled BA applied to the basal parts of two leaflets of one leaf, either pure or in combination with 500 mM sorbitol or mannitol. After one week, 10B concentrations in different plant parts were determined. In B deficient leaves (0 μM 11B), 10B absorption was significantly lower than in all other treatments (9.7% of the applied dose vs. 26%–32%). The application of BA in combination with polyols increased absorption by 18–25% as compared to pure BA. The absolute amount of applied 10B moving out of the application zone was lowest in plants with 0 μM 11B supply (1.1% of the applied dose) and highest in those grown in 100 μM 11B (2.8%). The presence of sorbitol significantly decreased the share of mobile 10B in relation to the amount absorbed. The results suggest that 11B deficiency reduces the permeability of the leaf surface for BA. The addition of polyols may increase 10B absorption, but did not improve 10B distribution within the plant, which was even hindered when applied a sorbitol complex

G12 signaling through c-jun nh 2-terminal kinase promotes breast cancer cell invasion

10.1371/journal.pone.0026085PLoS ONE611

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

On the Origins of Suboptimality in Human Probabilistic Inference

Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior