Influence of smoking and obesity on alveolar-arterial gas pressure differences and dead space ventilation at rest and peak exercise in healthy men and women

SummaryBackground and aimsBesides exercise intolerance, the assessment of ventilatory and perfusion adequacy allows additional insights in the disease pathophysiology in many cardiovascular or pulmonary diseases. Valid measurements of dead space/tidal volume ratios (VD/VT), arterial (a′) – end-tidal (et) carbon dioxide (CO2) and oxygen (O2) pressure differences (p(a′-et)CO2) and (p(et-a′)O2), and alveolar (A)–a′ O2 pressure differences (p(A-a′)O2) require using blood samples in addition to gas exchange analyses on a breath-by-breath-basis. Smoking and nutritional status are also important factors in defining disorders. Using a large healthy population we considered the impact of these factors to develop useful prediction equations.Methods and resultsIncremental cycle exercise protocols were applied to apparently healthy volunteer adults who did not have structural heart disease or echocardiographic or lung function pathologies. Age, height, weight, and smoking were analysed for their influence on the target parameters in each gender. Reference values were determined by regression analyses. The final study sample consisted of 476 volunteers (190 female), aged 25–85 years. Smoking significantly influences p(A-a′)O2 and p(a′-et)CO2 at rest and peak exercise, and VD/VT during exercise. Obesity influences upper limits of VD/VT, p(a′-et)CO2 and p(et-a′)O2 at rest as well as p(A-a′)O2 and p(et-a′)O2 at exercise. Reference equations for never-smokers as well as for apparently healthy smokers considering influencing factors are given.ConclusionGender, age, height, weight, and smoking significantly influence gas exchange. Considering all of these factors this study provides a comprehensive set of reference equations derived from a large number of participants of a population-based study

The future of European Nephrology 'Guidelines' - a declaration of intent by European Renal Best Practice (ERBP)

The disparities of medical practice, together with a growing number of possible interventions, have increased the demand for well-conceived guidance for practitioners [1]. However, this development is hampered by the number and quality of scientific studies that test medical hypotheses, which are often unsatisfactory. This is especially true in nephrology, where well-conducted controlled trials are rare [2]. Because patients with renal failure are generally excluded from controlled studies in the general population [3], the development of sufficiently well-founded guidance in nephrology has always been difficult. With the development of European Best Practice Guidelines (EBPG), the European Renal Association–European Dialysis and Transplantation Association (ERA–EDTA) has created its own guidance-generating process. Similar initiatives have also arisen in the USA (Kidney Disease Outcome Initiative—K/DOQI), Australia (Caring for Australasians with Renal Impairment—CARI), Canada (Canadian Society of Nephrology—CSN), the UK (United Kingdom Renal Association—UKRA), as well as at several other locations around the world. These institutions have generated a plethora of often parallel recommendations on similar topics but sometimes with different messages [4]. The question can be asked: ‘Is there still a place for an institution generating European nephrology guidance?’ If there is, how should such an initiative be managed to conform with current demands? To answer these questions, the Council of ERA–EDTA set up a commission that convened three times in the course of 2008–09. The present text is a distillation of the discussions, reflections and final conclusions of this commission. It is an ad hoc document, reflecting the current status. In the future, concepts and attitudes might change, as medical thinking is influenced by changes in practice, needs, general philosophy, ethics and political/financial conditions

Distinct Subunit Domains Govern Synaptic Stability and Specificity of the Kainate Receptor

SummarySynaptic communication between neurons requires the precise localization of neurotransmitter receptors to the correct synapse type. Kainate-type glutamate receptors restrict synaptic localization that is determined by the afferent presynaptic connection. The mechanisms that govern this input-specific synaptic localization remain unclear. Here, we examine how subunit composition and specific subunit domains contribute to synaptic localization of kainate receptors. The cytoplasmic domain of the GluK2 low-affinity subunit stabilizes kainate receptors at synapses. In contrast, the extracellular domain of the GluK4/5 high-affinity subunit synergistically controls the synaptic specificity of kainate receptors through interaction with C1q-like proteins. Thus, the input-specific synaptic localization of the native kainate receptor complex involves two mechanisms that underlie specificity and stabilization of the receptor at synapses

No recombination of mtDNA after heteroplasmy for 50 generations in the mouse maternal germline

Peer reviewe

PyMVPA: A Unifying Approach to the Analysis of Neuroscientific Data

The Python programming language is steadily increasing in popularity as the language of choice for scientific computing. The ability of this scripting environment to access a huge code base in various languages, combined with its syntactical simplicity, make it the ideal tool for implementing and sharing ideas among scientists from numerous fields and with heterogeneous methodological backgrounds. The recent rise of reciprocal interest between the machine learning (ML) and neuroscience communities is an example of the desire for an inter-disciplinary transfer of computational methods that can benefit from a Python-based framework. For many years, a large fraction of both research communities have addressed, almost independently, very high-dimensional problems with almost completely non-overlapping methods. However, a number of recently published studies that applied ML methods to neuroscience research questions attracted a lot of attention from researchers from both fields, as well as the general public, and showed that this approach can provide novel and fruitful insights into the functioning of the brain. In this article we show how PyMVPA, a specialized Python framework for machine learning based data analysis, can help to facilitate this inter-disciplinary technology transfer by providing a single interface to a wide array of machine learning libraries and neural data-processing methods. We demonstrate the general applicability and power of PyMVPA via analyses of a number of neural data modalities, including fMRI, EEG, MEG, and extracellular recordings

Tracking the coherent generation of polaron pairs in conjugated polymers

The optical excitation of organic semiconductors not only generates charge-neutral electron-hole pairs (excitons), but also charge-separated polaron pairs with high yield. The microscopic mechanisms underlying this charge separation have been debated for many years. Here we use ultrafast two-dimensional electronic spectroscopy to study the dynamics of polaron pair formation in a prototypical polymer thin film on a sub-20-fs time scale. We observe multi-period peak oscillations persisting for up to about 1 ps as distinct signatures of vibronic quantum coherence at room temperature. The measured two-dimensional spectra show pronounced peak splittings revealing that the elementary optical excitations of this polymer are hybridized exciton-polaron-pairs, strongly coupled to a dominant underdamped vibrational mode. Coherent vibronic coupling induces ultrafast polaron pair formation, accelerates the charge separation dynamics and makes it insensitive to disorder. These findings open up new perspectives for tailoring light-to-current conversion in organic materials

Exploiting photosynthesis-driven P450 activity to produce indican in tobacco chloroplasts

Photosynthetic organelles offer attractive features for engineering small molecule bioproduction by their ability to convert solar energy into chemical energy required for metabolism. The possibility to couple biochemical production directly to photosynthetic assimilation as a source of energy and substrates has intrigued metabolic engineers. Specifically, the chemical diversity found in plants often relies on cytochrome P450-mediated hydroxylations that depend on reductant supply for catalysis and which often lead to metabolic bottlenecks for heterologous production of complex molecules. By directing P450 enzymes to plant chloroplasts one can elegantly deal with such redox prerequisites. In this study, we explore the capacity of the plant photosynthetic machinery to drive P450-dependent formation of the indigo precursor indoxyl-β-D-glucoside (indican) by targeting an engineered indican biosynthetic pathway to tobacco (Nicotiana benthamiana) chloroplasts. We show that both native and engineered variants belonging to the human CYP2 family are catalytically active in chloroplasts when driven by photosynthetic reducing power and optimize construct designs to improve productivity. However, while increasing supply of tryptophan leads to an increase in indole accumulation, it does not improve indican productivity, suggesting that P450 activity limits overall productivity. Co-expression of different redox partners also does not improve productivity, indicating that supply of reducing power is not a bottleneck. Finally, in vitro kinetic measurements showed that the different redox partners were efficiently reduced by photosystem I but plant ferredoxin provided the highest light-dependent P450 activity. This study demonstrates the inherent ability of photosynthesis to support P450-dependent metabolic pathways. Plants and photosynthetic microbes are therefore uniquely suited for engineering P450-dependent metabolic pathways regardless of enzyme origin. Our findings have implications for metabolic engineering in photosynthetic hosts for production of high-value chemicals or drug metabolites for pharmacological studies

The Locations of Gamma-Ray Bursts Measured by COMPTEL

The COMPTEL instrument on the Compton Gamma Ray Observatory is used to measure the locations of gamma-ray bursts through direct imaging of MeV photons. In a comprehensive search, we have detected and localized 29 bursts observed between 1991 April 19 and 1995 May 31. The average location accuracy of these events is 1.25\arcdeg (1$\sigma$), including a systematic error of \sim0.5\arcdeg, which is verified through comparison with Interplanetary Network (IPN) timing annuli. The combination of COMPTEL and IPN measurements results in locations for 26 of the bursts with an average ``error box'' area of only $\sim$0.3 deg$^2$ (1$\sigma$). We find that the angular distribution of COMPTEL burst locations is consistent with large-scale isotropy and that there is no statistically significant evidence of small-angle auto-correlations. We conclude that there is no compelling evidence for burst repetition since no more than two of the events (or $\sim$7% of the 29 bursts) could possibly have come from the same source. We also find that there is no significant correlation between the burst locations and either Abell clusters of galaxies or radio-quiet quasars. Agreement between individual COMPTEL locations and IPN annuli places a lower limit of $\sim$100~AU (95% confidence) on the distance to the stronger bursts.Comment: Accepted for publication in the Astrophysical Journal, 1998 Jan. 1, Vol. 492. 33 pages, 9 figures, 5 table