Cirugía bariátrica y evolución de COVID-19 / Bariatric surgery and evolution of COVID-19

INTRODUCTION. Obesity is recognized as a risk factor for developing severe new coronavirus disease. Bariatric surgery prior to infection could behave as a protective factor against serious infections and death. OBJECTIVE. To describe the impact of bariatric surgery on the severity and mortality of patients with obesity and new coronavirus disease; through a systematic review and meta-analysis of the specialized literature from 2020-2022. METHODOLOGY. Publications indexed in databases such as Pubmed, Tripdatabase, and Google scholar, on the impact of previous bariatric surgery on the evolution and prognosis of patients with new coronavirus disease were taken. The Newcastle-Ottawa scale was used to assess quality and risk of bias. RevMan 5.0 software was used for statistical analysis. RESULTS. Eight cohort studies were included, with a population of 137 620 adult subjects with obesity and new coronavirus disease; of these, 5638 (4.09%) had a history of bariatric surgery. In the meta-analysis, it was determined that, in subjects with obesity and new coronavirus disease, the history of bariatric surgery had a protective effect against the use of mechanical ventilation [OR: 0.68; 95% CI: 0.62-0.75] (p<0.001) and mortality [OR: 0.57; 95% CI: 0.50-0.65] (p<0.01). CONCLUSIONS. The history of bariatric surgery in subjects with obesity seems to have a protective effect against the severity defined by the use of mechanical ventilation in patients with obesity and mortality due to the new coronvirus disease; therefore, the resumption of bariatric surgical activity, at pre-pandemic levels, could represent an additional benefit for candidate subjects.

INTRODUCTION. Obesity is recognized as a risk factor for developing severe new coronavirus disease. Bariatric surgery prior to infection could behave as a protective factor against serious infections and death. OBJECTIVE. To describe the impact of bariatric surgery on the severity and mortality of patients with obesity and new coronavirus disease; through a systematic review and meta-analysis of the specialized literature from 2020-2022. METHODOLOGY. Publications indexed in databases such as Pubmed, Tripdatabase, and Google scholar, on the impact of previous bariatric surgery on the evolution and prognosis of patients with new coronavirus disease were taken. The Newcastle-Ottawa scale was used to assess quality and risk of bias. RevMan 5.0 software was used for statistical analysis. RESULTS. Eight cohort studies were included, with a population of 137 620 adult subjects with obesity and new coronavirus disease; of these, 5638 (4.09%) had a history of bariatric surgery. In the meta-analysis, it was determined that, in subjects with obesity and new coronavirus disease, the history of bariatric surgery had a protective effect against the use of mechanical ventilation [OR: 0.68; 95% CI: 0.62-0.75] (p<0.001) and mortality [OR: 0.57; 95% CI: 0.50-0.65] (p<0.01). CONCLUSIONS. The history of bariatric surgery in subjects with obesity seems to have a protective effect against the severity defined by the use of mechanical ventilation in patients with obesity and mortality due to the new coronvirus disease; therefore, the resumption of bariatric surgical activity, at pre-pandemic levels, could represent an additional benefit for candidate subjects.

Escala CAS, una nueva valoración de ansiedad generalizada por Covid-19 en México.

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International Federation of Clinical Neurophysiology (IFCN) - EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies.

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published "IFCN Guidelines for topographic and frequency analysis of EEGs and EPs" (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of "synchronization" features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of "connectivity" features based on frequency analysis (linear and nonlinear measures); (6) extraction of "topographic" features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising "synchronization" and "connectivity" features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses.

Selective recruitment of cortical neurons by electrical stimulation.

Despite its critical importance in experimental and clinical neuroscience, at present there is no systematic method to predict which neural elements will be activated by a given stimulation regime. Here we develop a novel approach to model the effect of cortical stimulation on spiking probability of neurons in a volume of tissue, by applying an analytical estimate of stimulation-induced activation of different cell types across cortical layers. We utilize the morphology and properties of axonal arborization profiles obtained from publicly available anatomical reconstructions of the twelve main categories of neocortical neurons to derive the dependence of activation probability on cell type, layer and distance from the source. We then propagate this activity through the local network incorporating connectivity, synaptic and cellular properties. Our work predicts that (a) intracranial cortical stimulation induces selective activation across cell types and layers; (b) superficial anodal stimulation is more effective than cathodal at cell activation; (c) cortical surface stimulation focally activates layer I axons, and (d) there is an optimal stimulation intensity capable of eliciting cell activation lasting beyond the end of stimulation. We conclude that selective effects of cortical electrical stimulation across cell types and cortical layers are largely driven by their different axonal arborization and myelination profiles.

Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review.

A biophysical framework needed to interpret electrophysiological data recorded at multiple spatial scales of brain tissue is developed. Micro current sources at membrane surfaces produce local field potentials, electrocorticography, and electroencephalography (EEG). We categorize multi-scale sources as genuine, equivalent, or representative. Genuine sources occur at the micro scale of cell surfaces. Equivalent sources provide identical experimental outcomes over a range of scales and applications. In contrast, each representative source distribution is just one of many possible source distributions that yield similar experimental outcomes. Macro sources ("dipoles") may be defined at the macrocolumn (mm) scale and depend on several features of the micro sources-magnitudes, micro synchrony within columns, and distribution through the cortical depths. These micro source properties are determined by brain dynamics and the columnar structure of cortical tissue. The number of representative sources underlying EEG data depends on the spatial scale of neural tissue under study. EEG inverse solutions (e.g. dipole localization) and high resolution estimates (e.g. Laplacian, dura imaging) have both strengths and limitations that depend on experimental conditions. The proposed theoretical framework informs studies of EEG source localization, source characterization, and low pass filtering. It also facilitates interpretations of brain dynamics and cognition, including measures of synchrony, functional connections between cortical locations, and other aspects of brain complexity.

Optical aperture synthesis with electronically connected telescopes.

Highest resolution imaging in astronomy is achieved by interferometry, connecting telescopes over increasingly longer distances and at successively shorter wavelengths. Here, we present the first diffraction-limited images in visual light, produced by an array of independent optical telescopes, connected electronically only, with no optical links between them. With an array of small telescopes, second-order optical coherence of the sources is measured through intensity interferometry over 180 baselines between pairs of telescopes, and two-dimensional images reconstructed. The technique aims at diffraction-limited optical aperture synthesis over kilometre-long baselines to reach resolutions showing details on stellar surfaces and perhaps even the silhouettes of transiting exoplanets. Intensity interferometry circumvents problems of atmospheric turbulence that constrain ordinary interferometry. Since the electronic signal can be copied, many baselines can be built up between dispersed telescopes, and over long distances. Using arrays of air Cherenkov telescopes, this should enable the optical equivalent of interferometric arrays currently operating at radio wavelengths.

EEG functional connectivity, axon delays and white matter disease.

OBJECTIVE: Both structural and functional brain connectivities are closely linked to white matter disease. We discuss several such links of potential interest to neurologists, neurosurgeons, radiologists, and non-clinical neuroscientists. METHODS: Treatment of brains as genuine complex systems suggests major emphasis on the multi-scale nature of brain connectivity and dynamic behavior. Cross-scale interactions of local, regional, and global networks are apparently responsible for much of EEG's oscillatory behaviors. Finite axon propagation speed, often assumed to be infinite in local network models, is central to our conceptual framework. RESULTS: Myelin controls axon speed, and the synchrony of impulse traffic between distant cortical regions appears to be critical for optimal mental performance and learning. Several experiments suggest that axon conduction speed is plastic, thereby altering the regional and global white matter connections that facilitate binding of remote local networks. CONCLUSIONS: Combined EEG and high resolution EEG can provide distinct multi-scale estimates of functional connectivity in both healthy and diseased brains with measures like frequency and phase spectra, covariance, and coherence. SIGNIFICANCE: White matter disease may profoundly disrupt normal EEG coherence patterns, but currently these kinds of studies are rare in scientific labs and essentially missing from clinical environments.

Neocortical dynamics due to axon propagation delays in cortico-cortical fibers: EEG traveling and standing waves with implications for top-down influences on local networks and white matter disease.

The brain is treated as a nested hierarchical complex system with substantial interactions across spatial scales. Local networks are pictured as embedded within global fields of synaptic action and action potentials. Global fields may act top-down on multiple networks, acting to bind remote networks. Because of scale-dependent properties, experimental electrophysiology requires both local and global models that match observational scales. Multiple local alpha rhythms are embedded in a global alpha rhythm. Global models are outlined in which cm-scale dynamic behaviors result largely from propagation delays in cortico-cortical axons and cortical background excitation level, controlled by neuromodulators on long time scales. The idealized global models ignore the bottom-up influences of local networks on global fields so as to employ relatively simple mathematics. The resulting models are transparently related to several EEG and steady state visually evoked potentials correlated with cognitive states, including estimates of neocortical coherence structure, traveling waves, and standing waves. The global models suggest that global oscillatory behavior of self-sustained (limit-cycle) modes lower than about 20 Hz may easily occur in neocortical/white matter systems provided: Background cortical excitability is sufficiently high; the strength of long cortico-cortical axon systems is sufficiently high; and the bottom-up influence of local networks on the global dynamic field is sufficiently weak. The global models provide "entry points" to more detailed studies of global top-down influences, including binding of weakly connected networks, modulation of gamma oscillations by theta or alpha rhythms, and the effects of white matter deficits.

Top-down influences on local networks: basic theory with experimental implications.

The response of a population of cortical neurons to an external stimulus depends not only on the receptive field properties of the neurons, but also the level of arousal and attention or goal-oriented cognitive biases that guide information processing. These top-down effects on cortical neurons bias the output of the neurons and affect behavioral outcomes such as stimulus detection, discrimination, and response time. In any physiological study, neural dynamics are observed in a specific brain state; the background state partly determines neuronal excitability. Experimental studies in humans and animal models have also demonstrated that slow oscillations (typically in the alpha or theta bands) modulate the fast oscillations (gamma band) associated with local networks of neurons. Cross-frequency interaction is of interest as a mechanism for top-down or bottom up interactions between systems at different spatial scales. We develop a generic model of top-down influences on local networks appropriate for comparison with EEG. EEG provides excellent temporal resolution to investigate neuronal oscillations but is space-averaged on the cm scale. Thus, appropriate EEG models are developed in terms of population synaptic activity. We used the Wilson-Cowan population model to investigate fast (gamma band) oscillations generated by a local network of excitatory and inhibitory neurons. We modified the Wilson-Cowan equations to make them more physiologically realistic by explicitly incorporating background state variables into the model. We found that the population response is strongly influenced by the background state. We apply the model to reproduce the modulation of gamma rhythms by theta rhythms as has been observed in animal models and human ECoG and EEG studies. The concept of a dynamic background state presented here using the Wilson-Cowan model can be readily applied to incorporate top-down modulation in more detailed models of specific cortical systems.

Screening of transition and post-transition metals to incorporate into copper oxide and copper bismuth oxide for photoelectrochemical hydrogen evolution.

A new dispenser and scanner system is used to create and screen Bi-M-Cu oxide arrays for cathodic photoactivity, where M represents 1 of 22 different transition and post-transition metals. Over 3000 unique Bi : M : Cu atomic ratios are screened. Of the 22 metals tested, 10 show a M-Cu oxide with higher photoactivity than CuO and 10 show a Bi-M-Cu oxide with higher photoactivity than CuBi2O4. Cd, Zn, Sn, and Co produce the most photoactive M-Cu oxides, all showing a 200-300% improvement in photocurrent over CuO. Ag, Cd, and Zn produce the highest photoactivity Bi-M-Cu oxides with a 200-400% improvement over CuBi2O4. Most notable is a Bi-Ag-Cu oxide (Bi : Ag : Cu atomic ratio of 22 : 3 : 11) which shows 4 times higher photocurrent than CuBi2O4. This material is capable of evolving hydrogen under illumination in neutral electrolyte solutions at 0.6 V vs. RHE when Pt is added to the surface as an electrocatalyst.

Neocortical dynamics at multiple scales: EEG standing waves, statistical mechanics, and physical analogs.

The dynamic behavior of scalp potentials (EEG) is apparently due to some combination of global and local processes with important top-down and bottom-up interactions across spatial scales. In treating global mechanisms, we stress the importance of myelinated axon propagation delays and periodic boundary conditions in the cortical-white matter system, which is topologically close to a spherical shell. By contrast, the proposed local mechanisms are multiscale interactions between cortical columns via short-ranged non-myelinated fibers. A mechanical model consisting of a stretched string with attached nonlinear springs demonstrates the general idea. The string produces standing waves analogous to large-scale coherent EEG observed in some brain states. The attached springs are analogous to the smaller (mesoscopic) scale columnar dynamics. Generally, we expect string displacement and EEG at all scales to result from both global and local phenomena. A statistical mechanics of neocortical interactions (SMNI) calculates oscillatory behavior consistent with typical EEG, within columns, between neighboring columns via short-ranged non-myelinated fibers, across cortical regions via myelinated fibers, and also derives a string equation consistent with the global EEG model.

Implications of white matter correlates of EEG standing and traveling waves.

This letter addresses the recent paper titled "White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm" (Valdés-Hernández et al., 2009) and takes issue with some of its conclusions. I suggest here that the standing wave model cited by the authors provides a robust connection to a restricted class of genuine EEG experiments. The alpha band actually consists of a mixture of distinct phenomena; the standing wave model applies only to the distributed and coherent (global) part. New approaches are suggested that may either refute or support competing dynamic models of EEG and can have major impacts on the experimental design of new cognitive studies.

Identification of wave-like spatial structure in the SSVEP: comparison of simultaneous EEG and MEG.

Steady-state visual-evoked potentials/fields (SSVEPs/SSVEFs) are used in cognitive and clinical electroencephalogram (EEG) and magnetoencephalogram (MEG) studies because of their excellent signal-to-noise ratios and relative immunity to artifact. Steady-state paradigms are also used to characterize preferred frequencies of dynamic neocortical processes. In this study, SSVEPs and SSVEFs were simultaneously recorded while subjects viewed checkerboard patterns alternating (black to white, white to black) with fixed driving frequency between 2 and 20 Hz. Distinct peaks in SSVEP/SSVEF power were observed in the theta (4-8 Hz) and upper alpha (10-14 Hz) bands. A distinct peak in SSVEP power was also observed in the beta band (between 15 and 20 Hz) which had no counterpart in the MEG. One-dimensional spatial spectra indicate that distinct large-scale source distributions contribute to SSVEP power in the upper alpha band in the form of long wavelength (lambda>20 cm) traveling waves propagating from occipital to prefrontal electrodes. In the beta band, spatial spectra and SSVEF indicate that long-wavelength source distributions over posterior and anterior regions form standing wave patterns. These results suggest that simple models of SSVEP based on a single dipole source in the occipital lobe are inadequate to explain the dynamic spatial patterns of SSVEP magnitude and phase. Theoretical models of SSVEP should include multiple local and distributed sources and exhibit both traveling and standing wave dynamics.

EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics.

We contrasted coherence estimates obtained with EEG, Laplacian, and MEG measures of synaptic activity using simulations with head models and simultaneous recordings of EEG and MEG. EEG coherence is often used to assess functional connectivity in human cortex. However, moderate to large EEG coherence can also arise simply by the volume conduction of current through the tissues of the head. We estimated this effect using simulated brain sources and a model of head tissues (cerebrospinal fluid (CSF), skull, and scalp) derived from MRI. We found that volume conduction can elevate EEG coherence at all frequencies for moderately separated (<10 cm) electrodes; a smaller levation is observed with widely separated (>20 cm) electrodes. This volume conduction effect was readily observed in experimental EEG at high frequencies (40-50 Hz). Cortical sources generating spontaneous EEG in this band are apparently uncorrelated. In contrast, lower frequency EEG coherence appears to result from a mixture of volume conduction effects and genuine source coherence. Surface Laplacian EEG methods minimize the effect of volume conduction on coherence estimates by emphasizing sources at smaller spatial scales than unprocessed potentials (EEG). MEG coherence estimates are inflated at all frequencies by the field spread across the large distance between sources and sensors. This effect is most apparent at sensors separated by less than 15 cm in tangential directions along a surface passing through the sensors. In comparison to long-range (>20 cm) volume conduction effects in EEG, widely spaced MEG sensors show smaller field-spread effects, which is a potentially significant advantage. However, MEG coherence estimates reflect fewer sources at a smaller scale than EEG coherence and may only partially overlap EEG coherence. EEG, Laplacian, and MEG coherence emphasize different spatial scales and orientations of sources.

Comparison of the effect of volume conduction on EEG coherence with the effect of field spread on MEG coherence.

We analyzed models of volume conduction and magnetic field spread to account for aspects of spatial structure in electroencephalographic (EEG) and magnetoencephalographic (MEG) coherence. The head volume conduction model consisted of three confocal ellipsoids, representing three layers (brain, skull, and scalp) with different tissue conductivities, while the magnetic field model follows from the Biot-Savart law in a spherically symmetric medium. Source models were constructed based on magnetic resonance imaging data from three subjects, approximating neocortical current source distributions as dipoles oriented perpendicular to the local cortical surface. Assuming that every source is uncorrelated to every other source, coherence between sensors due to volume conduction and field-spread effects was estimated. Spatial properties of the model coherences were then compared with simultaneously recorded spontaneous EEG and MEG. In both models and experimental data, EEG and MEG coherence was elevated between closely spaced channels. At very large channel separations, the field-spread effect on MEG coherence appears smaller than the volume conduction effect on EEG coherence. In EEG coherence studies, surface Laplacian methods can be used to remove volume conduction effects. With single-coil magnetometers, MEG coherences are free of field effects only for sensor pairs separated by more than 20 cm. Model coherences resemble most high-frequency (e.g. >20 Hz) data; volume conduction and field-spread effects are independent of frequency, suggesting mostly uncorrelated sources in these bands. High-frequency EEG and MEG coherence can evidently serve as an estimate of coherence effects due to volume conduction and field effects, when source and head models are not available for individual subjects.