RESUMEN

Chromosomal deletion and duplication syndromes can lead to intellectual disability, autism, microcephaly, and poor growth. Usually manifestations of duplication syndromes are milder than that of the deletion syndromes. With the availability of tests for analysis of copy number variants, it is possible to identify the deletion and duplication syndromes with greater ease. We report 32 cases of chromosomal duplication syndromes, identified in children presenting with developmental delay, intellectual disability, or microcephaly and/or additional features, at a tertiary care center on karyotyping or microarray analysis. Seven were isolated duplications, and one child had an additional smaller pathogenic deletion. Thus, duplication syndromes can have milder presentations with spectrum of dysmorphism, behavioral problems, and intellectual disability, but it is possible to diagnose easily with latest emerging high-throughput technologies.

Asunto(s)

Discapacidad Intelectual , Microcefalia , Niño , Humanos , Duplicación Cromosómica/genética , Microcefalia/genética , Discapacidad Intelectual/genética , Investigación , Deleción Cromosómica , Síndrome

RESUMEN

Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth. Since the disease spreads from carriers to susceptible individuals via some form of contact, it inherently relies upon a network of individuals for its growth, with edges established via direct interaction, such as shared physical proximity. Using individual-level COVID-19 data from the early days (January 30 to April 15, 2020) of the pandemic in India, and under a network-based SIR model framework, we performed state-specific forecasting under multiple scenarios characterized by the basic reproduction number of COVID-19 across 34 Indian states and union territories. We validated our short-term projections using observed case counts and the long-term projections using national sero-survey findings. Based on healthcare availability data, we also performed projections to assess the burdens on the infrastructure along the spectrum of the pandemic growth. We have developed an interactive dashboard summarizing our results. Our predictions successfully identified the initial hot-spots of India such as Maharashtra and Delhi, and those that emerged later, such as Madhya Pradesh and Kerala. These models have the potential to inform appropriate policies for isolation and mitigation strategies to contain the pandemic, through a phased approach by appropriate resource prioritization and allocation.

RESUMEN

ImportanceIndia has taken strong and early public health measures for arresting the spread of the COVID-19 epidemic. With only 536 COVID-19 cases and 11 fatalities, India - a democracy of 1.34 billion people - took the historic decision of a 21-day national lockdown on March 25. The lockdown was further extended to May 3rd, soon after the analysis of this paper was completed. ObjectiveTo study the short- and long-term impact of an initial 21-day lockdown on the total number of COVID-19 cases in India compared to other less severe non-pharmaceutical interventions using epidemiological forecasting models and Bayesian estimation algorithms; to compare effects of hypothetical durations of lockdown from an epidemiological perspective; to study alternative explanations for slower growth rate of the virus outbreak in India, including exploring the association of the number of cases and average monthly temperature; and finally, to outline the pivotal role of reliable and transparent data, reproducible data science methods, tools and products as we reopen the country and prepare for a post lock-down phase of the pandemic. Design, Setting, and ParticipantsWe use the daily data on the number of COVID-19 cases, of recovered and of deaths from March 1 until April 7, 2020 from the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Additionally, we use COVID-19 incidence counts data from Kaggle and the monthly average temperature of major cities across the world from Wikipedia. Main Outcome and MeasuresThe current time-series data on daily proportions of cases and removed (recovered and death combined) from India are analyzed using an extended version of the standard SIR (susceptible, infected, and removed) model. The eSIR model incorporates time-varying transmission rates that help us predict the effect of lockdown compared to other hypothetical interventions on the number of cases at future time points. A Markov Chain Monte Carlo implementation of this model provided predicted proportions of the cases at future time points along with credible intervals (CI). ResultsOur predicted cumulative number of COVID-19 cases in India on April 30 assuming a 1-week delay in peoples adherence to a 21-day lockdown (March 25 - April 14) and a gradual, moderate resumption of daily activities after April 14 is 9,181 with upper 95% CI of 72,245. In comparison, the predicted cumulative number of cases under "no intervention" and "social distancing and travel bans without lockdown" are 358 thousand and 46 thousand (upper 95% CI of nearly 2.3 million and 0.3 million) respectively. An effective lockdown can prevent roughly 343 thousand (upper 95% CI 1.8 million) and 2.4 million (upper 95% CI 38.4 million) COVID-19 cases nationwide compared to social distancing alone by May 15 and June 15, respectively. When comparing a 21-day lockdown with a hypothetical lockdown of longer duration, we find that 28-, 42-, and 56-day lockdowns can approximately prevent 238 thousand (upper 95% CI 2.3 million), 622 thousand (upper 95% CI 4.3 million), 781 thousand (upper 95% CI 4.6 million) cases by June 15, respectively. We find some suggestive evidence that the COVID-19 incidence rates worldwide are negatively associated with temperature in a crude unadjusted analysis with Pearson correlation estimates [95% confidence interval] between average monthly temperature and total monthly incidence around the world being -0.185 [-0.548, 0.236] for January, -0.110 [-0.362, 0.157] for February, and -0.173 [-0.314, -0.026] for March. Conclusions and RelevanceThe lockdown, if implemented correctly in the end, has a high chance of reducing the total number of COVID-19 cases in the short term, and buy India invaluable time to prepare its healthcare and disease monitoring system. Our analysis shows we need to have some measures of suppression in place after the lockdown for the best outcome. We cannot heavily rely on the hypothetical prevention governed by meteorological factors such as temperature based on current evidence. From an epidemiological perspective, a longer lockdown between 42-56 days is preferable. However, the lockdown comes at a tremendous price to social and economic health through a contagion process not dissimilar to that of the coronavirus itself. Data can play a defining role as we design post-lockdown testing, reopening and resource allocation strategies. SoftwareOur contribution to data science includes an interactive and dynamic app (covind19.org) with short- and long-term projections updated daily that can help inform policy and practice related to COVID-19 in India. Anyone can visualize the observed data for India and create predictions under hypothetical scenarios with quantification of uncertainties. We make our prediction codes freely available (https://github.com/umich-cphds/cov-ind-19) for reproducible science and for other COVID-19 affected countries to use them for their prediction and data visualization work.

RESUMEN

Herein, we report for the first time the successful preparation of a gold(III) nitrate [Au(NO3)3] water-based precursor for use in a bottom-up ultrasonic spray pyrolysis (USP) process. Due to its limited solubility in water, the precursor was prepared under reflux conditions with nitric acid (HNO3) as the solvent and ammonium hydroxide (NH4OH) as a neutralizer. This precursor enabled the USP synthesis of gold nanoparticles (AuNPs) and the in situ formation of low concentrations of NO2- and NO3- ions, which were caught directly in deionized water in a collection system. These ions were proven to act as stabilizers for the AuNPs. Investigations showed that the AuNPs were monodispersed and spherically shaped with a size distribution over three groups: the first contained 5.3 % AuNPs with diameters (2 r) <15 nm, the second contained 82.5 % AuNPs with 2 r between 15 and 200 nm, and the third contained 12.2 % AuNPs with 2 r>200 nm. UV/Vis spectroscopy revealed the maximum absorbance band of the AuNPs at λ=528 nm. Additionally, scanning transmission electron microscopy (STEM) observations of the smallest AuNPs (2 r<5 nm) revealed atomically resolved coalescence phenomena induced by interaction with the electron beam. Four stages of the particle-growth process were distinguished: 1) movement and rotation of the AuNPs; 2) necking mechanism; 3) orientated attachment at matching facets; 4) reshaping of the AuNPs by surface diffusion. This provided important insight into the formation/synthesis process of the AuNPs.