Use of Oversized Injectable Valves in Growing Children for Total Repair of Right Ventricular Outflow Tract Anomalies (Preliminary Results)

Right ventricular outflow tract surgery was originally confined to transannular patching, in the belief that pulmonary regurgitation was well tolerated. Because follow-up evaluations revealed the deleterious effects of pulmonary regurgitation, surgery today aims to spare or replace the valve. Available replacement devices have short lifetimes, considering growth mismatch in children. We hypothesize that oversizing the right infundibulum anticipates growth and that a squeezed prosthesis can complete the expansion process.The No-React^® Injectable BioPulmonic Valve is designed for right infundibular surgery in adults, and hundreds of implants have shown promising results. We used this device for surgery in babies, with the addition of an innovative oversizing technique. This study evaluates our preliminary results and investigates whether such a technique might reduce growth mismatch.From September 2010 through July 2012, we implanted 11 injectable pulmonic valves. The median age of our patients was 23 months. After opening the right infundibulum, we enlarged it as much as possible with a wide patch. Before completing the patch suture, we injected an oversized valve.No problems occurred during surgery. No major insufficiency or leak was observed. We conclude that prostheses can be quite oversized and perform well even when not completely expanded.Oversized injectable pulmonic valves, shrunken to a smaller diameter, enabled the implantation of a device wider than otherwise possible, without affecting performance. Moreover, the prosthesis tended to return to its original size following growth, thereby reducing growth mismatch. Longer follow-up and larger numbers of patients are needed for verification.     

Heart Failure in Remission for More than 13 Years after Removal of a Left Ventricular Assist Device

Mechanical cardiac unloading with use of a left ventricular assist device (LVAD) is associated with substantial improvements in left ventricular function and enables subsequent LVAD explantation in some patients. We describe the case of a 35-year-old man with dilated nonischemic cardiomyopathy who was supported with an LVAD for 9 months. After the device was removed, he led a normal life for 13 years and 4 months. However, at 49 years of age, he presented with new signs and symptoms of heart failure, necessitating implantation of a 2nd LVAD. Afterwards, he has remained asymptomatic. This case is unique in that the patient lived a normal life for longer than a decade before renewed left ventricular decompensation necessitated repeat LVAD therapy. Histologic examination revealed few changes between the first device''s removal in 1999 and the 2nd device''s implantation in 2012.     

Clinical characteristics and viral shedding kinetics of 38 asymptomatic patients with coronavirus disease 2019: A retrospective observational study

This study aims to investigate the clinical characteristics and viral shedding kinetics of asymptomatic patients with coronavirus disease 2019 (COVID-19).The data of 38 asymptomatic patients positive for SARS-CoV-2 nucleic acid were collected from February to March 2020 in Tuanfeng County, Huanggang, Hubei, China. The epidemiology, laboratory examination, chest imaging, viral nucleic acid test results, clinical characteristics, and viral shedding time were summarized in this retrospective study.The study included 20 family members of patients with COVID-19, 10 medical personnel participating in COVID-19 treatment or working in a fever clinic, 6 personnel from quarantine places, 1 individual with a close contact history with confirmed patients, and 1 local epidemic prevention personnel. All were positive for SARS-CoV-2 nucleic acid. The white blood cell (WBC) count, the absolute value of lymphocytes, C-reactive protein (CRP), and D-dimer were normal. Pneumonia manifestations were not found in the chest computed tomography (CT) scan of 36 patients; the remaining 2 cases included a 1-year-old child and a pregnant woman, and they did not undergo chest CT. The viral shedding time was 6 days.All asymptomatic patients with COVID-19 had a history of close contact or exposure. Laboratory tests were normal. Chest imaging did not show any pneumonia manifestation. The viral shedding time was <10 days, which is shorter than that of patients with COVID-19. A timely discovery of such asymptomatic infections is crucial for blocking the spread of the virus and strengthening the prevention and control measures.     

Association between time spent outdoors and myopia among junior high school students: A 3-wave panel study in China

This study sought to investigate the recent incidence rate of myopia in Chinese junior high school students and analyze the effect of time spent outdoors on myopia, in addition to facilitating the prevention and control of myopia among students.This study was derived from a national panel study, the China Education Panel Survey. We conducted three rounds of follow-up visits among 10,279 seventh grade students from 112 middle schools in 20 provinces in 2013. In total, 3571 students were selected for the analysis in 2020 by excluding those lost to follow-up and students who were myopic in the first round. The primary outcomes were the prevalence of myopia and the effect of time outdoors on myopia. The baseline characteristics of the included students were described, and the correlation between time spent outdoors and myopia in the three rounds of data was analyzed by a correlation chi-square test. Then, the generalized estimation equation (GEE) was used to estimate the influence of time spent outdoors on myopia after follow-up.There were 3571 students with normal baseline vision, and 1508 (42.23%) students progressed from having a normal vision to myopia in the third round, of whom 706 (46.82%) were male and 802 (53.18%) were female. The results of the chi-square test showed that the time spent outdoors of all students and girls, specifically, was related to myopia (P < .05). Next, the GEE was used to analyze the influence of time spent outdoors on myopia after follow-up. After two model adjustments (individual and family-related characteristics of students), students with < 7 hours/week time spent outdoors retained a high myopia rate than ≥14 hours/week (OR = 1.250; 95% CI: 1.070–1.460). Among boys, there was no statistical correlation between time spent outdoors and myopia (P > .05). For girls, compared with students who spent ≥14 hours/week outdoors, students with <7 hours/week spent outdoors retained a higher myopia rate (OR = 1.355; 95%CI: 1.067–1.720).Increased time spent outdoors can delay the development of myopia. In terms of gender, girls should be targeted to more effectively prevent and control the development and progression of myopia.     

Response times from ensembles of accumulators

Decision-making is explained by psychologists through stochastic accumulator models and by neurophysiologists through the activity of neurons believed to instantiate these models. We investigated an overlooked scaling problem: How does a response time (RT) that can be explained by a single model accumulator arise from numerous, redundant accumulator neurons, each of which individually appears to explain the variability of RT? We explored this scaling problem by developing a unique ensemble model of RT, called e pluribus unum, which embodies the well-known dictum “out of many, one.” We used the e pluribus unum model to analyze the RTs produced by ensembles of redundant, idiosyncratic stochastic accumulators under various termination mechanisms and accumulation rate correlations in computer simulations of ensembles of varying size. We found that predicted RT distributions are largely invariant to ensemble size if the accumulators share at least modestly correlated accumulation rates and RT is not governed by the most extreme accumulators. Under these regimes the termination times of individual accumulators was predictive of ensemble RT. We also found that the threshold measured on individual accumulators, corresponding to the firing rate of neurons measured at RT, can be invariant with RT but is equivalent to the specified model threshold only when the rate correlation is very high.Response time (RT) is a core measure of human decision-making in experimental psychology (1). The random variation of RT across otherwise identical trials has been a puzzle since the mid-19th century. Since the 1960s, this variation of RT—measured in a wide range of perceptual, cognitive, and economic tasks (1–5)—has been explained through stochastic accumulator models. These models assume that a response is generated when evidence accumulates at a certain rate (v) over time to a threshold (θ) and that the stochastic variation of RTs arises primarily from random fluctuations in accumulation rates (Fig. 1A). Historically, these models were formulated and tested before data on the underlying neural processes were available.Open in a separate windowFig. 1.Response times predicted by ensembles of redundant stochastic accumulators. (A) Stochastic accumulator models describe RT in terms of an accumulation process (one trajectory per trial) that proceeds at a certain rate (v) to reach a fixed threshold (θ). Stochastic variation of RT arises from fluctuations of v between (η) and within trials (ξ). It is common to consider one accumulator associated with each of multiple responses; we considered instead the case of multiple accumulators associated with the same response (Inset). (B) RT can also be described by the time at which the evolving spike rates of certain neurons, averaged across bins of trials with common RTs (one trajectory per RT bin, replotted from ref. 49), reach an activation level that is invariant with RT (A_RT). These neurons have been argued to instantiate the process described by stochastic accumulator models. (C) Unless accumulators are perfectly correlated (Inset), it is unclear (i) how an ensemble of accumulators makes the transition from evidence accumulation to response execution, (ii) under what termination rules (p_N) and accumulation rate correlations (r_v) the dynamics of one accumulator (highlighted red) predicts RT distributions and the invariant relationship between A_RT and RT, as observed empirically, and (iii) how A_RT relates to the unobserved threshold of an accumulator (θ).Subsequently, neurons exhibiting accumulating discharge rates in various RT tasks have been found in sensory, sensorimotor, and motor brain structures; in premotor circuits for limb and eye movements it is known that the neurons with accumulating activity are necessary and sufficient for initiating movements (6, 7). Movements are initiated when the trial-averaged accumulating spike rate of these neurons reaches a fixed activation level (6) (A_RT) like a threshold, and the distribution of RTs is accounted for by the stochastic variability in the rate of growth of neural activity toward A_RT (Fig. 1B). This discovery inspired the conjecture that individual neurons instantiate the evidence accumulation process described by stochastic accumulator models (6). This conjecture has stimulated extensive research replicating the original observation and equating accumulator model parameters with measures of neural dynamics assessed by spike rates (8–17), EEG (18, 19), magnetoencephalography (MEG) (20), and functional MRI (21–23) and simulated with neural network models (24–28).However, this productive line of research has overlooked a fundamental scaling problem. On the one hand, the behavior of specific single neurons seems sufficient to account for the RT of the whole brain. On the other hand, we know that ensembles of tens of thousands of neurons are necessary to produce any action (SI Text, How Many Neurons Produce a Movement?). Hence, how can each individual accumulator neuron, recorded in isolation, seem sufficient to initiate a movement by crossing a unique threshold when no single accumulator neuron is necessary for a movement to occur? In other words, how is the accumulating activity of numerous redundant and idiosyncratic neurons in a large ensemble coordinated and combined to produce variable RTs that can be predicted by a model consisting of just a single stochastic accumulator? This question has not been addressed previously (SI Text, Extension of Previous Work).This question is challenging to investigate empirically because the limited number of spikes emitted by individual neurons precludes reliable assessment of single-trial dynamics, and simultaneous measurement of numerous functionally homogeneous neurons is not possible with current technology. Therefore, we performed computer simulations of ensembles of stochastic accumulators.We address four major issues. First, we investigate how RT distributions can be explained both by a single accumulator model and by the ensemble activity of many accumulators. Second, we explore how RT distributions scale with the accumulator ensemble size. Third, we investigate how the A_RT measured across trials from an individual accumulator can be invariant with RT even though RT is produced by a large ensemble of accumulators with different growth rates. Fourth, we explore how the measured A_RT from an individual accumulator relates to the actual threshold of that accumulator (θ).To address these issues, we developed a unique ensemble model of RT, called e pluribus unum (EPU), which embodies the well-known dictum “out of many, one.” Stochastic accumulator models are typically designed to explain both RT and accuracy obtained in choice tasks. However, our questions are specifically centered on the basic variability of RT that is observed in responses in any task. Thus, this model does not address accuracy, although we envision natural extensions of this approach to racing or competing ensembles of accumulators embodied by simple differential equations or in more complex spiking network models.In single-accumulator models, RT critically depends on two key parameters: the accumulation rate (v) and the threshold (θ). Extrapolating these parameters to the ensemble case is not trivial (Fig. 1C).First, how are accumulation rates coordinated across the ensemble? At one extreme, if all accumulators share identical dynamics, then the ensemble reduces to one accumulator (Fig. 1C, Inset), yet perfect correlation is implausible (29). At the other extreme, if all accumulators have uncorrelated dynamics, then unrealistic RT variability would occur. Moreover, uncorrelated dynamics would also be implausible from a biological perspective, given that ensembles receive common inputs, have recurrent connections, and are modulated by common neurotransmitter systems. We investigated this question by sampling correlated accumulation rates, with rate correlation (r_v) varying between 0.0 and 1.0. Though the range of rate correlations we simulated exceeds the noise correlation found among neighboring neurons (30, 31), they can arise naturally from redundancy in common inputs, recurrent connectivity, and modulation by a common source (32–34).Second, how is ensemble activity combined to produce one RT? At one extreme, if RT is specified by the time when the fastest accumulator reaches threshold, the RT distribution will shrink with ensemble size. At the other extreme, if RT is specified by the time when the slowest accumulator reaches threshold, the RT distribution will expand with ensemble size. How large is the region between these two extremes where the RT distribution remains stable with ensemble size? We investigated these questions by assuming that each accumulator projects to a unit that either tallies the proportion of accumulators having crossed a threshold activation (a “polling” mechanism akin to quorum sensing) (35) or monitors the average firing rate of the ensemble (a “pooling” mechanism akin to the vector averaging that guides movement dynamics in final common neural circuits that initiate movements) (36, 37). When this unit tallies a critical proportion of units hitting threshold (p_N, polling) or reaches a threshold of average activity (θ_trigger, pooling), an overt response is triggered that is measured as RT.We determined how RT distributions and the dynamics of individual accumulators were influenced by three ensemble properties: the number of accumulators (1 ≤ n ≤ 1,000), the correlation of accumulation rates across accumulators (0.0 ≤ r_v ≤ 1.0), and the termination rule of the accumulation process (polling: 0% < p_N ≤ 100%; and pooling: Σ A_i(t)/N ≥ θ_trigger). We explored two influential types of stochastic accumulator models, one assuming within-trial as well as between-trial variability in accumulation (diffusion model) (38) and one assuming only between-trial variability (linear ballistic accumulator model) (39), as well as four variants making additional assumptions. Conclusions based on simulation of these models agreed, so we present the simple linear ballistic accumulator model here and the diffusion model and other more complex models in SI Text, Robustness of Findings.