Effectiveness of nitric oxide during spontaneous breathing in experimental lung injury

Inhaled nitric oxide (iNO) improves gas exchange in about 60% of patients with acute respiratory distress syndrome (ARDS). Recruitment of atelectatic lung areas may improve responsiveness and preservation of spontaneous breathing (SB) may cause recruitment. Accordingly, preservation of SB may improve effectiveness of iNO. To test this hypothesis, iNO was evaluated in experimental acute lung injury (ALI) during SB. In 24 pigs with ALI, effects of 10 ppm iNO were evaluated during controlled mechanical ventilation (CMV) and SB in random order. Preservation of SB was provided by 4 different modes: Unassisted SB was enabled by biphasic positive airway pressure (BIPAP), moderate inspiratory assist was provided by pressure support (PS) and volume-assured pressure support (VAPS), maximum assist was ensured by assist control (A/C). Statistical analysis did not reveal gas exchange improvements due to SB alone. Significant gas exchange improvements due to iNO were only achieved during unassisted SB with BIPAP (P <.05) but not during CMV or assisted SB. The authors conclude that effectiveness of iNO may be improved by unassisted SB during BIPAP but not by assisted SB. Thus combined iNO and unassisted SB is possibly most effective to improve gas exchange in severe hypoxemic ARDS.

Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T(1) and T(2) differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets.

Learning Spike-Based Population Codes by Reward and Population Feedback

We investigate a recently proposed model for decision learning in a population of spiking neurons where synaptic plasticity is modulated by a population signal in addition to reward feedback. For the basic model, binary population decision making based on spike/no-spike coding, a detailed computational analysis is given about how learning performance depends on population size and task complexity. Next, we extend the basic model to n-ary decision making and show that it can also be used in conjunction with other population codes such as rate or even latency coding.

Comparison of zotarolimus-eluting and everolimus-eluting coronary stents

New-generation coronary stents that release zotarolimus or everolimus have been shown to reduce the risk of restenosis. However, it is unclear whether there are differences in efficacy and safety between the two types of stents on the basis of prospectively adjudicated end points endorsed by the Food and Drug Administration.

Do new biologics meet the unmet medical need in rheumatoid arthritis? Safety and efficacy of abatacept following B-cell depletion

Sir, anti TNF-α agents (aTNFs) are the most commonly prescribed biological agents in RA. More recently abatacept (ABA), a T-cell costimulation modulator, and rituximab (RTX), a monoclonal antibody directed against CD20, have become available. Observational studies suggest that switching to a new drug class may be more effective in uncontrolled RA than switching to a class of biologics to which the patient had unsuccessfully been exposed [1]. Information about the efficacy and safety of cycling strategies through third-line biologics is lacking. This study aimed to analyse the effectiveness and safety of switching patients to ABA as the third biological class after failure of aTNF plus RTX. The Swiss Clinical Quality Management (SCQM) programme for RA is a longitudinal population-based cohort, which has been approved by the local ethics committees of all participating centres [2]. For this analysis, we collected all the …

Selective REM sleep deprivation in narcolepsy

Narcolepsy is characterized by excessive daytime sleepiness and rapid eye movement (REM) sleep abnormalities, including cataplexy. The aim of this study was to assess REM sleep pressure and homeostasis in narcolepsy. Six patients with narcolepsy and six healthy controls underwent a REM sleep deprivation protocol, including one habituation, one baseline, two deprivation nights (D1, D2) and one recovery night. Multiple sleep latency tests (MSLTs) were performed during the day after baseline and after D2. During D1 and D2 REM sleep was prevented by awakening the subjects at the first polysomnographic signs of REM sleep for 2 min. Mean sleep latency and number of sleep-onset REM periods (SOREMs) were determined on all MSLT. More interventions were required to prevent REM sleep in narcoleptics compared with control subjects during D1 (57 ± 16 versus 24 ± 10) and D2 (87 ± 22 versus 35 ± 8, P = 0.004). Interventions increased from D1 to D2 by 46% in controls and by 53% in narcoleptics (P < 0.03). Selective REM sleep deprivation was successful in both controls (mean reduction of REM to 6% of baseline) and narcoleptics (11%). Both groups had a reduction of total sleep time during the deprivation nights (P = 0.03). Neither group had REM sleep rebound in the recovery night. Narcoleptics had, however, an increase in the number of SOREMs on MSLT (P = 0.005). There was no increase in the number of cataplexies after selective REM sleep deprivation. We conclude that: (i) REM sleep pressure is higher in narcoleptics; (ii) REM sleep homeostasis is similar in narcoleptics and controls; (iii) in narcoleptics selective REM sleep deprivation may have an effect on sleep propensity but not on cataplexy.

Child and adolescent psychiatric genetics

The current status of child and adolescent psychiatric genetics appears promising in light of the initiation of genome-wide association studies (GWAS) for diverse polygenic disorders and the molecular elucidation of monogenic Rett syndrome, for which recent functional studies provide hope for pharmacological treatment strategies. Within the last 50 years, tremendous progress has been made in linking genetic variation to behavioral phenotypes and psychiatric disorders. We summarize the major findings of the Human Genome Project and dwell on largely unsuccessful candidate gene and linkage studies. GWAS for the first time offer the possibility to detect single nucleotide polymorphisms and copy number variants without a priori hypotheses as to their molecular etiology. At the same time it is becoming increasingly clear that very large sample sizes are required in order to enable genome wide significant findings, thus necessitating further large-scaled ascertainment schemes for the successful elucidation of the molecular genetics of childhood and adolescent psychiatric disorders. We conclude by reflecting on different scenarios for future research into the molecular basis of early onset psychiatric disorders. This review represents the introductory article of this special issue of the European Child and Adolescent Psychiatry.

Optimizing heroin-assisted treatment (HAT): Assessment of the contribution of direct ethanol metabolites in identifying hazardous and harmful alcohol use

Heavy alcohol consumption may accelerate the progression of hepatitis C-related liver disease and/or limit efforts at antiviral treatment in opioid-dependent patients receiving heroin-assisted treatment (HAT). Our study aims to assess alcohol intake among HAT patients by self-reports compared to direct ethanol metabolites.

In vivo nuclear translocation of mineralocorticoid and glucocorticoid receptors in rat kidney: differential effect of corticosteroids along the distal tubule

Aldosterone and corticosterone bind to mineralocorticoid (MR) and glucocorticoid receptors (GR), which, upon ligand binding, are thought to translocate to the cell nucleus to act as transcription factors. Mineralocorticoid selectivity is achieved by the 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) that inactivates 11β-hydroxy glucocorticoids. High expression levels of 11β-HSD2 characterize the aldosterone-sensitive distal nephron (ASDN), which comprises the segment-specific cells of late distal convoluted tubule (DCT2), connecting tubule (CNT), and collecting duct (CD). We used MR- and GR-specific antibodies to study localization and regulation of MR and GR in kidneys of rats with altered plasma aldosterone and corticosterone levels. In control rats, MR and GR were found in cell nuclei of thick ascending limb (TAL), DCT, CNT, CD cells, and intercalated cells (IC). GR was also abundant in cell nuclei and the subapical compartment of proximal tubule (PT) cells. Dietary NaCl loading, which lowers plasma aldosterone, caused a selective removal of GR from cell nuclei of 11β-HSD2-positive ASDN. The nuclear localization of MR was unaffected. Adrenalectomy (ADX) resulted in removal of MR and GR from the cell nuclei of all epithelial cells. Aldosterone replacement rapidly relocated the receptors in the cell nuclei. In ASDN cells, low-dose corticosterone replacement caused nuclear localization of MR, but not of GR. The GR was redistributed to the nucleus only in PT, TAL, early DCT, and IC that express no or very little 11β-HSD2. In ASDN cells, nuclear GR localization was only achieved when corticosterone was replaced at high doses. Thus ligand-induced nuclear translocation of MR and GR are part of MR and GR regulation in the kidney and show remarkable segment- and cell type-specific characteristics. Differential regulation of MR and GR may alter the level of heterodimerization of the receptors and hence may contribute to the complexity of corticosteroid effects on ASDN function.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.

Spatio-temporal credit assignment in population learning

We present a model for plasticity induction in reinforcement learning which is based on a cascade of synaptic memory traces. In the cascade of these so called eligibility traces presynaptic input is first corre lated with postsynaptic events, next with the behavioral decisions and finally with the external reinforcement. A population of leaky integrate and fire neurons endowed with this plasticity scheme is studied by simulation on different tasks. For operant co nditioning with delayed reinforcement, learning succeeds even when the delay is so large that the delivered reward reflects the appropriateness, not of the immediately preceeding response, but of a decision made earlier on in the stimulus - decision sequence . So the proposed model does not rely on the temporal contiguity between decision and pertinent reward and thus provides a viable means of addressing the temporal credit assignment problem. In the same task, learning speeds up with increasing population si ze, showing that the plasticity cascade simultaneously addresses the spatial problem of assigning credit to the different population neurons. Simulations on other task such as sequential decision making serve to highlight the robustness of the proposed sch eme and, further, contrast its performance to that of temporal difference based approaches to reinforcement learning.