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The incidence of hypoglycemia in the immediate postnatal period is rising because of the increasing rate of preterm births, maternal diabetes, and maternal obesity. Severe hypoglycemia has been considered a risk factor for neuronal cell death and adverse neurodevelopmental outcomes. The American Academy of Pediatrics (AAP) suggests a goal of ≥45 mg/dL (≥2.5 mmol/L) for infants who are asymptomatic within the first 48 hours. The Pediatric Endocrine Society (PES) suggests that infants who are unable to maintain their blood glucose >50 mg/dL (>2.77 mmol/L) within the first 48 hours or >60 mg/dL (>3.33 mmol/L) after the first 48 hours are at risk for persistent hypoglycemia. While there is disagreement for target glucose levels within the first 48 hours, both the AAP and the PES suggest further investigation for persistent hypoglycemia beyond 48-72 hours, which is beyond the scope of this article. However, in the immediate postnatal period, much can be gained with familiarization of the two guidelines, as well as current management techniques. This article presents current definitions and treatment modalities for management of hypoglycemia in infants considered at high risk in the immediate postnatal period.Simulation is an effective teaching methodology to enhance clinical thinking and reasoning skills among nursing students and practicing nurses. The opportunity to practice in a safe environment maintains a structure that promotes learning at all levels. There are various levels of fidelity as well as cost to facilitate simulation in the neonatal setting. This at times hinders the ability to incorporate simulation into educational practices. The purpose of this article is to provide a discussion on simulation practices in the neonatal setting, an overview of low-cost neonatal simulation exemplars, and implications for practice.COVID-19 is devastating health systems globally and causing severe ventilator shortages. Nigericin sodium Since the beginning of the outbreak, the provision and use of ventilators has been a key focus of public discourse. Scientists and engineers from leading universities and companies have rushed to develop low-cost ventilators in hopes of supporting critically ill patients in developing countries. Philanthropists have invested millions in shipping ventilators to low-resource settings, and agencies such as the World Health Organization and the World Bank are prioritizing the purchase of ventilators. While we recognize the humanitarian nature of these efforts, merely shipping ventilators to low-resource environments may not improve outcomes of patients and could potentially cause harm. An ecosystem of considerable technological and human resources is required to support the usage of ventilators within intensive care settings. Medical-grade oxygen supplies, reliable electricity, bioengineering support, and consumables are all needed for ventilators to save lives. However, most ICUs in resource-poor settings do not have access to these resources. Patients on ventilators require continuous monitoring from physicians, nurses, and respiratory therapists skilled in critical care. Health care workers in many low-resource settings are already exceedingly overburdened, and pulling these essential human resources away from other critical patient needs could reduce the overall quality of patient care. When deploying medical devices, it is vital to align the technological intervention with the clinical reality. Low-income settings often will not benefit from resource-intensive equipment, but rather from contextually appropriate devices that meet the unique needs of their health systems.

Bedside monitors in the ICU routinely measure and collect patients’ physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the ability to store and rapidly process big data sets in recent years, these physiologic data show promise in identifying specific outcomes and/or events during patients’ ICU hospitalization.

We introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay (length of stay) on mechanical ventilation, defined as >4 d, in a pediatric ICU.

Continuous-in-time vital signs information and clinical history data were retrospectively collected for 284 ICU subjects from their first 24 h on mechanical ventilation from a medical-surgical pediatric ICU at Boston Children’s Hospital. Multiple machine learning models were trained on multiple subsets of thecalTrials.gov registration NCT02184208.).

Our proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments, exploiting real-time vital sign information from bedside monitors. (ClinicalTrials.gov registration NCT02184208.).

We aimed to measure severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serological responses in children hospitalized with multisystem inflammatory syndrome in children (MIS-C) compared with those with coronavirus disease 2019 (COVID-19), those with Kawasaki disease (KD), and hospitalized pediatric controls.

From March 17, 2020, to May 26, 2020, we prospectively identified hospitalized children with MIS-C (

= 10), symptomatic COVID-19 (

= 10), and KD (

= 5) and hospitalized controls (

= 4) at Children’s Healthcare of Atlanta. With institutional review board approval, we obtained prospective and residual blood samples from these children and measured SARS-CoV-2 spike receptor-binding domain (RBD) immunoglobulin M and immunoglobulin G (IgG), full-length spike IgG, and nucleocapsid protein antibodies using quantitative enzyme-linked immunosorbent assays and SARS-CoV-2 neutralizing antibodies using live-virus focus-reduction neutralization assays. We statistically compared the log-transforme lengths of stay (

= 0.590;

= .010).

Quantitative SARS-CoV-2 serology may have a role in establishing the diagnosis of MIS-C, distinguishing it from similar clinical entities, and stratifying risk for adverse outcomes.

Quantitative SARS-CoV-2 serology may have a role in establishing the diagnosis of MIS-C, distinguishing it from similar clinical entities, and stratifying risk for adverse outcomes.