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BACKGROUND: Immunomodulatory drugs have been used in patients with severe COVID-19. The objective of this study was to evaluate the effects of two different strategies, based either on an interleukin-1 inhibitor, anakinra, or on a JAK inhibitor, such as baricitinib, on the survival of patients hospitalized with COVID-19 pneumonia. METHODS: Individuals admitted to two hospitals because of COVID-19 were included if they fulfilled the clinical, radiological, and laboratory criteria for moderate-to-severe disease. Patients were classified according to the first immunomodulatory drug prescribed: anakinra or baricitinib. All subjects were concomitantly treated with corticosteroids, in addition to standard care. The main outcomes were the need for invasive mechanical ventilation (IMV) and in-hospital death. Statistical analysis included propensity score matching and Cox regression model. RESULTS: The study subjects included 125 and 217 individuals in the anakinra and baricitinib groups, respectively. IMV was required in 13 (10.4%) and 10 (4.6%) patients, respectively (p = 0.039). During this period, 22 (17.6%) and 36 (16.6%) individuals died in both groups (p = 0.811). Older age, low functional status, high comorbidity, need for IMV, elevated lactate dehydrogenase, and use of a high flow of oxygen at initially were found to be associated with worse clinical outcomes. No differences according to the immunomodulatory therapy used were observed. For most of the deceased individuals, early interruption of anakinra or baricitinib had occurred at the time of their admission to the intensive care unit. CONCLUSIONS: Similar mortality is observed in patients treated with anakinra or baricitinib plus corticosteroids.

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Networks of randomly connected neurons are among the most popular models in theoretical neuroscience. The connectivity between neurons in the cortex is however not fully random, the simplest and most prominent deviation from randomness found in experimental data being the overrepresentation of bidirectional connections among pyramidal cells. Using numerical and analytical methods, we investigate the effects of partially symmetric connectivity on the dynamics in networks of rate units. We consider the two dynamical regimes exhibited by random neural networks: the weak-coupling regime, where the firing activity decays to a single fixed point unless the network is stimulated, and the strong-coupling or chaotic regime, characterized by internally generated fluctuating firing rates. In the weak-coupling regime, we compute analytically, for an arbitrary degree of symmetry, the autocorrelation of network activity in the presence of external noise. In the chaotic regime, we perform simulations to determine the timescale of the intrinsic fluctuations. In both cases, symmetry increases the characteristic asymptotic decay time of the autocorrelation function and therefore slows down the dynamics in the network.

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Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

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In visual and auditory scenes, we are able to identify shared features among sensory objects and group them according to their similarity. This grouping is preattentive and fast and is thought of as an elementary form of categorization by which objects sharing similar features are clustered in some abstract perceptual space. It is unclear what neuronal mechanisms underlie this fast categorization. Here we propose a neuromechanistic model of fast feature categorization based on the framework of continuous attractor networks. The mechanism for category formation does not rely on learning and is based on biologically plausible assumptions, for example, the existence of populations of neurons tuned to feature values, feature-specific interactions, and subthreshold-evoked responses upon the presentation of single objects. When the network is presented with a sequence of stimuli characterized by some feature, the network sums the evoked responses and provides a running estimate of the distribution of features in the input stream. If the distribution of features is structured into different components or peaks (i.e., is multimodal), recurrent excitation amplifies the response of activated neurons, and categories are singled out as emerging localized patterns of elevated neuronal activity (bumps), centered at the centroid of each cluster. The emergence of bump states through sequential, subthreshold activation and the dependence on input statistics is a novel application of attractor networks. We show that the extraction and representation of multiple categories are facilitated by the rich attractor structure of the network, which can sustain multiple stable activity patterns for a robust range of connectivity parameters compatible with cortical physiology.

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Severe feeding disorders often require inpatient treatment and need a holistic assessment and treatment approach. This article introduces some of the current literature concerning feeding disorders in infants and toddlers. The philosophy of the Department of Infant Psychosomatics at the University Children's Hospital Zürich emphasizes interdisciplinary teamwork, the application of psychodyamic tools in pediatric liaison psychiatry and utilization of group settings. Clinical approach and course of treatment are illustrated by three case reports, highlighting specific psychotherapeutic interventions with the parent-infant relationship in the context of a paediatric clinic.

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During coordinated eye-hand movements, saccade reaction times (SRTs) and reach reaction times (RRTs) are correlated in humans and monkeys. Reaction times (RTs) measure the degree of movement preparation and can correlate with movement speed and accuracy. However, RTs can also reflect effector nonspecific influences, such as motivation and arousal. We use a combination of behavioral psychophysics and computational modeling to identify plausible mechanisms for correlations in SRTs and RRTs. To disambiguate nonspecific mechanisms from mechanisms specific to movement coordination, we introduce a dual-task paradigm in which a reach and a saccade are cued with a stimulus onset asynchrony (SOA). We then develop several variants of integrate-to-threshold models of RT, which postulate that responses are initiated when the neural activity encoding effector-specific movement preparation reaches a threshold. The integrator models formalize hypotheses about RT correlations and make predictions for how each RT should vary with SOA. To test these hypotheses, we trained three monkeys to perform the eye-hand SOA task and analyzed their SRTs and RRTs. In all three subjects, RT correlations decreased with increasing SOA duration. Additionally, mean SRT decreased with decreasing SOA, revealing facilitation of saccades with simultaneous reaches, as predicted by the model. These results are not consistent with the predictions of the models with common modulation or common input but are compatible with the predictions of a model with mutual excitation between two effector-specific integrators. We propose that RT correlations are not simply attributable to motivation and arousal and are a signature of coordination.

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We introduce in this paper a new method for reducing neurodynamical data to an effective diffusion equation, either experimentally or using simulations of biophysically detailed models. The dimensionality of the data is first reduced to the first principal component, and then fitted by the stationary solution of a mean-field-like one-dimensional Langevin equation, which describes the motion of a Brownian particle in a potential. The advantage of such description is that the stationary probability density of the dynamical variable can be easily derived. We applied this method to the analysis of cortical network dynamics during up and down states in an anesthetized animal. During deep anesthesia, intracellularly recorded up and down states transitions occurred with high regularity and could not be adequately described by a one-dimensional diffusion equation. Under lighter anesthesia, however, the distributions of the times spent in the up and down states were better fitted by such a model, suggesting a role for noise in determining the time spent in a particular state.

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In human and animal hypertension models, increased activity of G-protein-coupled receptor kinase (GRK) 2 determines a generalized decrease of beta-adrenergic vasodilatation. We analyzed the possibility of differential changes in the expression and functionality of alpha(1A), alpha(1B), alpha(1D), beta(1), beta(2), and beta(3)-ARs also being involved in the process. We combined the quantification of mRNA levels with immunoblotting and functional studies in aortas of young and adult spontaneously hypertensive rats (SHRs) and their controls (Wistar Kyoto). We found the expression and function of beta(1)-adrenoceptors in young prehypertensive SHRs to be higher, whereas a generalized increase in the expression of the six adrenoceptors and GRK2 was observed in aortas of adult hypertensive SHRs. alpha(1D)- and beta(3)-adrenoceptors, the subtypes that are more resistant to GRK2-mediated internalization and mostly expressed in rat aorta, exhibited an increased functional role in hypertensive animals, showing two hemodynamic consequences: 1) an increased sensitivity to the vasoconstrictor stimulus accompanied by a decreased sensitivity to the vasodilator stimulus (alpha(1D)-ARs are the most sensitive to agonists, and beta(3)-ARs are the least sensitive to agonists); and 2) a slower recovery of the basal tone after adrenergic stimulus removal because of the kinetic characteristic of the alpha(1D) subtype. These functional changes might be involved in the greater sympathetic vasoconstrictor tone observed in hypertension.

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The spike activity of cells in some cortical areas has been found to be correlated with reaction times and behavioral responses during two-choice decision tasks. These experimental findings have motivated the study of biologically plausible winner-take-all network models, in which strong recurrent excitation and feedback inhibition allow the network to form a categorical choice upon stimulation. Choice formation corresponds in these models to the transition from the spontaneous state of the network to a state where neurons selective for one of the choices fire at a high rate and inhibit the activity of the other neurons. This transition has been traditionally induced by an increase in the external input that destabilizes the spontaneous state of the network and forces its relaxation to a decision state. Here we explore a different mechanism by which the system can undergo such transitions while keeping the spontaneous state stable, based on an escape induced by finite-size noise from the spontaneous state. This decision mechanism naturally arises for low stimulus strengths and leads to exponentially distributed decision times when the amount of noise in the system is small. Furthermore, we show using numerical simulations that mean decision times follow in this regime an exponential dependence on the amplitude of noise. The escape mechanism provides thus a dynamical basis for the wide range and variability of decision times observed experimentally.

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The analysis of transitions in stochastic neurodynamical systems is essential to understand the computational principles that underlie those perceptual and cognitive processes involving multistable phenomena, like decision making and bistable perception. To investigate the role of noise in a multistable neurodynamical system described by coupled differential equations, one usually considers numerical simulations, which are time consuming because of the need for sufficiently many trials to capture the statistics of the influence of the fluctuations on that system. An alternative analytical approach involves the derivation of deterministic differential equations for the moments of the distribution of the activity of the neuronal populations. However, the application of the method of moments is restricted by the assumption that the distribution of the state variables of the system takes on a unimodal Gaussian shape. We extend in this paper the classical moments method to the case of bimodal distribution of the state variables, such that a reduced system of deterministic coupled differential equations can be derived for the desired regime of multistability.

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Many perceptual and cognitive processes, like decision-making and bistable perception, involve multistable phenomena under the influence of noise. The role of noise in a multistable neurodynamical system can be formally treated within the Fokker-Planck framework. Nevertheless, because of the underlying nonlinearities, one usually considers numerical simulations of the stochastic differential equations describing the original system, which are time consuming. An alternative analytical approach involves the derivation of reduced deterministic differential equations for the moments of the distribution of the activity of the neuronal populations. The study of the reduced deterministic system avoids time consuming computations associated with the need to average over many trials. We apply this technique to describe multistable phenomena. We show that increasing the noise amplitude results in a shifting of the bifurcation structure of the system.

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Urea and guanidine-hydrochloride (GdnHCl) are frequently used for protein denaturation in order to determine the Gibbs free energy of folding and kinetic folding/unfolding parameters. Constant pH value is applied in the folding/unfolding experiments at different denaturant concentrations and steady protonation state of titratable groups is assumed in the folded and unfolded protein, respectively. The apparent side-chain pKa values of Asp, Glu, His and Lys in the absence and presence of 6 M urea and GdnHCl, respectively, have been determined by 1H-NMR. pKa values of all four residues are up-shifted by 0.3-0.5 pH units in presence of 6 M urea by comparison with pKa values of the residues dissolved in water. In the presence of 6 M GdnHCl, pKa values are down-shifted by 0.2-0.3 pH units in the case of acidic and up-shifted by 0.3-0.5 pH units in the case of basic residues. Shifted pKa values in the presence of denaturant may have a pronounced effect on the outcome of the protein stability obtained from denaturant unfolding experiments.

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The mRNA levels for the three alpha1-adrenoceptor subtypes, alpha1A, alpha1B, and alpha1D, were quantified by real-time RT-PCR in arteries from Wistar rats. The alpha1D-adrenoceptor was prominent in both aorta (79.0%) and mesenteric artery (68.7%), alpha1A predominated in tail (61.7%) and small mesenteric artery (73.3%), and both alpha1A- and alpha1D-subtypes were expressed at similar levels in iliac artery. The mRNA levels of the alpha1B-subtype were a minority in all vessels (1.7-11.1%). Concentration-response curves of contraction in response to phenylephrine or relaxation in response to alpha1-adrenoceptor antagonists on maximal sustained contraction induced by phenylephrine were constructed from control vessels and vessels pretreated with 100 micromol/l chloroethylclonidine (CEC) for 30 min. The significant decrease in the phenylephrine potency observed after CEC treatment together with the inhibitory potency displayed by 8-{2-[4-(2-methoxyphenyl)-1-piperazinyl]-8-azaspiro (4,5) decane-7-dionedihydrochloride} (BMY-7378, an alpha1D-adrenoceptor antagonist) confirm the relevant role of alpha1D-adrenoceptors in aorta and iliac and proximal mesenteric arteries. The potency of 5-methylurapidil (an alpha1A-adrenoceptor antagonist) and the changes in the potency of both BMY-7378 and 5-methylurapidil after CEC treatment provided evidence of a mixed population of alpha1A- and alpha1D-adrenoceptors in iliac and distal mesenteric arteries. The low potency of prazosin (pIC50 < 9) as well as the high 5-methylurapidil potency in tail and small mesenteric arteries suggest the main role of alpha1A/alpha1L-adrenoceptors with minor participation of the alpha1D-subtype. The mRNA levels and CEC treatment corroborated this pattern and confirmed that the alpha1L-adrenoceptor could be a functional isoform of the alpha1A-subtype.

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The pH-dependent stability of a protein is strongly affected by electrostatic interactions between ionizable residues in the folded as well as unfolded state. Here we characterize the individual contributions of charged Glu and His residues to stability and determine the NMR structure of the designed, heterodimeric leucine zipper AB consisting of an acidic A chain and a basic B chain. Thermodynamic parameters are compared with those of the homologous leucine zipper AB(SS) in which the A and B chains are disulfide-linked. NMR structures of AB based on (1)H NMR data collected at 600 MHz converge, and formation of the same six interchain salt bridges found previously in disulfide-linked AB(SS) [Marti, D. N., and Bosshard, H. R. (2003) J. Mol. Biol. 330, 621-637] is indicated. While the structures of AB and AB(SS) are very similar, their pH-dependent relative stabilities are strikingly different. The stability of AB peaks at pH approximately 4.5 and is higher at pH 8 than at pH 2. In contrast, AB(SS) is most stable at acidic pH where no interhelical salt bridges are formed. The different energetic contributions of charged Glu and His residues to stability of the two coiled coil structures were evaluated from pK(a) shifts induced by folding. The six charged Glu residues involved in salt bridges stabilize leucine zipper AB by 4.5 kJ/mol yet destabilize disulfide-linked AB(SS) by -1.1 kJ/mol. Two non-ion-paired Glu charges destabilize AB by only -1.8 kJ/mol but AB(SS) by -5.6 kJ/mol. The higher relative stability of AB at neutral pH is not caused by more favorable electrostatic interactions in the folded leucine zipper. It is due mainly to unfavorable electrostatic interactions in the unfolded A and B chains and may therefore be called an inverse electrostatic effect. This study illustrates the importance of residual interactions in the unfolded state and how the energetics of the unfolded state affect the stability of the folded protein.

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Four bisbenzyltetrahydroisoquinoline alkaloids (-)-medelline, (+)-antioquine, (+)-aromoline, and (+)-obamegine were isolated from the fruits of Xylopia columbiana. These compounds, the previously isolated alkaloids (+)-thaligrisine and (+)-isotetrandrine, as well as their O-acetylated derivatives were assayed on submitochondrial particles from beef heart as inhibitors of the mammalian respiratory chain. The results revealed that these alkaloids act as selective inhibitors of mitochondrial complex I in a 0.15 - 4.71 microM range. O-Acetylation, which increases their lipophilicity, considerably increased the inhibitory potency.

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Salt bridges in proteins are bonds between oppositely charged residues that are sufficiently close to each other to experience electrostatic attraction. They contribute to protein structure and to the specificity of interaction of proteins with other biomolecules, but in doing so they need not necessarily increase a protein's free energy of unfolding. The net electrostatic free energy of a salt bridge can be partitioned into three components: charge-charge interactions, interactions of charges with permanent dipoles, and desolvation of charges. Energetically favorable Coulombic charge-charge interaction is opposed by often unfavorable desolvation of interacting charges. As a consequence, salt bridges may destabilize the structure of the folded protein. There are two ways to estimate the free energy contribution of salt bridges by experiment: the pK(a) approach and the mutation approach. In the pK(a) approach, the contribution of charges to the free energy of unfolding of a protein is obtained from the change of pK(a) of ionizable groups caused by altered electrostatic interactions upon folding of the protein. The pK(a) approach provides the relative free energy gained or lost when ionizable groups are being charged. In the mutation approach, the coupling free energy between interacting charges is obtained from a double mutant cycle. The coupling free energy is an indirect and approximate measure of the free energy of charge-charge interaction. Neither the pK(a) approach nor the mutation approach can provide the net free energy of a salt bridge. Currently, this is obtained only by computational methods which, however, are often prone to large uncertainties due to simplifying assumptions and insufficient structural information on which calculations are based. This state of affairs makes the precise thermodynamic quantification of salt bridge energies very difficult. This review is focused on concepts and on the assessment of experimental methods and does not cover the vast literature.

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Human (HIV-1) and simian (SIV) immunodeficiency virus fusion with the host cell is promoted by the receptor-triggered refolding of the gp41 envelope protein into a stable trimer-of-hairpins structure that brings viral and cellular membranes into close proximity. The core of this hairpin structure is a six-helix bundle in which an inner homotrimeric coiled coil is buttressed by three antiparallel outer HR2 helices. We have used stopped-flow circular dichroism spectroscopy to characterize the unfolding and refolding kinetics of the six-helix bundle using the HIV-1 and SIV N34(L6)C28 polypeptides. In each case, the time-course of ellipticity changes in refolding experiments is well described by a simple two-state model involving the native trimer and the unfolded monomers. The unfolding free energy of the HIV-1 and SIV trimers and their urea dependence calculated from kinetic data are in very good agreement with data measured directly by isothermal unfolding experiments. Thus, formation of the gp41 six-helix bundle structure involves no detectable population of stable, partly folded intermediates. Folding of HIV-1 N34(L6)C28 is five orders of magnitudes faster than folding of its SIV counterpart in aqueous buffer: k(on),(HIV-1)=1.3 x 10(15)M(-2)s(-1) versus k(on),(SIV)=1.1 x 10(10)M(-2)s(-1). The unfolding rates are similar: k(off),(HIV-1)=1.1 x 10(-5)s(-1) versus k(off),(SIV=)5.7 x 10(-4)s(-1). Kinetic m-values indicate that the transition state for folding of the HIV-1 protein is significantly more compact than the transition state of the SIV protein. Replacement of a single SIV threonine by isoleucine corresponding to position 573 in the HIV-1 sequence significantly stabilizes the protein and renders the folding rate close to that of the HIV-1 protein yet without making the transition state of the mutant as compact as that of the HIV-1 protein. Therefore, the overall reduction of surface exposure in the high-energy transition state seems not to account for different folding rates. While the available biological evidence suggests that refolding of the gp41 protein is slow, our study implies that structural elements outside the trimer-of-hairpins limit the rate of HIV-1 fusion kinetics.

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Electrostatic interactions play a complex role in stabilizing proteins. Here, we present a rigorous thermodynamic analysis of the contribution of individual Glu and His residues to the relative pH-dependent stability of the designed disulfide-linked leucine zipper AB(SS). The contribution of an ionized side-chain to the pH-dependent stability is related to the shift of the pK(a) induced by folding of the coiled coil structure. pK(a)(F) values of ten Glu and two His side-chains in folded AB(SS) and the corresponding pK(a)(U) values in unfolded peptides with partial sequences of AB(SS) were determined by 1H NMR spectroscopy: of four Glu residues not involved in ion pairing, two are destabilizing (-5.6 kJ mol(-1)) and two are interacting with the positive alpha-helix dipoles and are thus stabilizing (+3.8 kJ mol(-1)) in charged form. The two His residues positioned in the C-terminal moiety of AB(SS) interact with the negative alpha-helix dipoles resulting in net stabilization of the coiled coil conformation carrying charged His (-2.6 kJ mol(-1)). Of the six Glu residues involved in inter-helical salt bridges, three are destabilizing and three are stabilizing in charged form, the net contribution of salt-bridged Glu side-chains being destabilizing (-1.1 kJ mol(-1)). The sum of the individual contributions of protonated Glu and His to the higher stability of AB(SS) at acidic pH (-5.4 kJ mol(-1)) agrees with the difference in stability determined by thermal unfolding at pH 8 and pH 2 (-5.3 kJ mol(-1)). To confirm salt bridge formation, the positive charge of the basic partner residue of one stabilizing and one destabilizing Glu was removed by isosteric mutations (Lys-->norleucine, Arg-->norvaline). Both mutations destabilize the coiled coil conformation at neutral pH and increase the pK(a) of the formerly ion-paired Glu side-chain, verifying the formation of a salt bridge even in the case where a charged side-chain is destabilizing. Because removing charges by a double mutation cycle mainly discloses the immediate charge-charge effect, mutational analysis tends to overestimate the overall energetic contribution of salt bridges to protein stability.

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