Host-range mutants of adenovirus 5 that contain a defect in region ElA (0-4.5 units) fail to replicate in HeLa cells and to transform rodent cells. In HeLa cells, these mutants synthesize only the two RNAs from EIA that share the same 5' and 3' termini but differ in length by the amount of internal sequence removed by splicing. RNA In eukaryotic cells and their viruses, modulation ofgene expression by a specific gene product has been shown in only a few instances. Examples of these include early RNA transcription in papovaviruses (1) and the regulation of galactose catabolism in yeast (2). In this paper, we report the characterization of adenovirus mutants and describe a specific gene product that regulates the expression of other viral genes.Early in adenovirus infection, five transcription regions (E1A and E1B and E2-E4) of the 35-kilobase genome are independently promoted (3) and produce one or more overlapping RNAs, most ofwhich have had internal sequences removed by splicing (4). Studies using wild-type and mutant viruses show that the onset, peak, and decline oftranscripts from these early regions is temporal and initiated with the expression of ElA (5-8). The two RNAs derived from ElA share the saime overlapping 5' and 3' termini but differ in length by the amount of intervening sequence removed (4). Adenovirus S (AdS) hostrange (Ad5hr) mutants defective within region ElA transcribe only the two cytoplasmic RNAs that originate from this region (5, 6). Nuclease S1 mapping shows that these ElA RNAs are indistinguishable from those of wild-type virus (5). These data imply that either the ElA RNAs or their encoded polypeptides (9) are required for the accumulation ofcytoplasmic transcripts from other early regions.In this paper, we analyze how the defect within E1A of each

These findings suggest that clinically important AAA events may be reduced in patients with diabetes who are prescribed metformin, but not those with diabetes receiving other treatments. A randomised controlled trial is needed to definitively test whether metformin reduces AAA related clinical events in patients with small AAAs who do not have diabetes.

There has been increasing interest in incorporating β‐lactam precision dosing into routine clinical care, but robust population pharmacokinetic models in critically ill children are needed for these purposes. The objective of this study was to demonstrate the feasibility of an opportunistic sampling approach that utilizes scavenged residual blood for future pharmacokinetic studies of cefepime, meropenem, and piperacillin. We aimed to show that opportunistic samples would cover the full concentration‐versus‐time profiles and to evaluate stability of the antibiotics in whole blood and plasma to optimize future use of the opportunistic sampling approach. A prospective observational study was conducted in a single‐center pediatric intensive care unit, where pediatric patients administered at least 1 dose of cefepime, meropenem, or piperacillin/tazobactam and who had residual blood scavenged from samples obtained for routine clinical care were enrolled. A total of 138 samples from 22 pediatric patients were collected in a 2‐week period. For all 3 antibiotics, the samples collected covered the entire dosing intervals and were not clustered around specific times. There was high variability in the free concentrations and in the percentage of drug bound to protein. There was less than 15% degradation for meropenem or piperacillin when stored in whole blood or plasma at 4°C after 6 days. Cefepime degraded by more than 15% after 3 days. The opportunistic sampling approach is a powerful and feasible method to obtain sufficient samples to study the variability of drug concentrations and protein binding for future pharmacokinetic studies in the pediatric critical care population.

Critical illness, including sepsis, causes significant pathophysiologic changes that alter the pharmacokinetics (PK) of antibiotics. Ceftriaxone is one of the most prescribed antibiotics in patients admitted to the pediatric intensive care unit (PICU). We sought to develop population PK models of both total ceftriaxone and free ceftriaxone in children admitted to a single-center PICU using a scavenged opportunistic sampling approach. We tested if the presence of sepsis and phase of illness (before or after 48 hours of antibiotic treatment) altered ceftriaxone PK parameters. We performed Monte Carlo simulations to evaluate whether dosing regimens commonly used in PICUs in the United States (50 mg/kg every 12 hours vs. 24 hours) resulted in adequate antimicrobial coverage. We found that a two-compartment model best described both total and free ceftriaxone concentrations. For free concentrations, the population clearance value is 6.54 L/h/70 kg, central volume is 25.4 L/70 kg and the peripheral volume is 19.6 L/70kg. For both models, we found that allometric weight scaling, post-menstrual age, creatinine clearance and daily highest temperature had significant effects on clearance. Presence of sepsis or phase of illness did not have a significant effect on clearance or volume of distribution. Monte Carlo simulations demonstrated that to achieve free concentrations above 1 μg/mL for 100% of the dosing intervals, a dosing regimen of 50 mg/kg every 12 hours is recommended for most patients. A continuous infusion could be considered if the target is to maintain free concentrations four times above the minimum inhibitory concentrations (4 μg/mL).

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IMPORTANCE Families and clinicians have limited validated tools available to assist in estimating long-term outcomes early after pediatric cardiac arrest. Blood-based brain-specific biomarkers may be helpful tools to aid in outcome assessment. OBJECTIVETo analyze the association of blood-based brain injury biomarker concentrations with outcomes 1 year after pediatric cardiac arrest. DESIGN, SETTING, AND PARTICIPANTSThe Personalizing Outcomes After Child Cardiac Arrest multicenter prospective cohort study was conducted in pediatric intensive care units at 14 academic referral centers in the US between May 16, 2017, and August 19, 2020, with the primary investigators blinded to 1-year outcomes. The study included 120 children aged 48 hours to 17 years who were resuscitated after cardiac arrest, had pre-cardiac arrest Pediatric Cerebral Performance Category scores of 1 to 3 points, and were admitted to an intensive care unit after cardiac arrest. EXPOSURE Cardiac arrest. MAIN OUTCOMES AND MEASURESThe primary outcome was an unfavorable outcome (death or survival with a Vineland Adaptive Behavior Scales, third edition, score of <70 points) at 1 year after cardiac arrest. Glial fibrillary acidic protein (GFAP), ubiquitin carboxyl-terminal esterase L1 (UCH-L1), neurofilament light (NfL), and tau concentrations were measured in blood samples from days 1 to 3 after cardiac arrest. Multivariate logistic regression and area under the receiver operating characteristic curve (AUROC) analyses were performed to examine the association of each biomarker with outcomes on days 1 to 3. RESULTS Among 120 children with primary outcome data available, the median (IQR) age was 1.0 (0-8.5) year; 71 children (59.2%) were male. A total of 5 children (4.2%) were Asian, 19 (15.8%) were Black, 81 (67.5%) were White, and 15 (12.5%) were of unknown race; among 110 children with data on ethnicity, 11 (10.0%) were Hispanic, and 99 (90.0%) were non-Hispanic. Overall, 70 children (58.3%) had a favorable outcome, and 50 children (41.7%) had an unfavorable outcome, including 43 deaths. On days 1 to 3 after cardiac arrest, concentrations of all 4 measured biomarkers were higher in children with an unfavorable vs a favorable outcome at 1 year. After covariate adjustment, NfL

Improved situation awareness (SA) decreases rates of clinical deterioration in the pediatric inpatient setting. We used a prospective, cross-sectional, observational study to measure interprofessional care team SA for a pediatric intensive care unit (PICU) patients. The resident, bedside nurse, and respiratory therapist for each patient were surveyed regarding high clinical deterioration risk status as defined by clinical criteria identified by the PICU fellow or attending and mitigation plan. From March 2018 to July 2019, we surveyed 400 care team trios caring for 73 high-risk patients. Nurses identified the patient’s risk status correctly for 375 of 400 patients (94%), respiratory therapists, 380 (95%; P = .4), and residents, 349 (87%; P = .002). For the 73 high-risk patients, nurses were correct 82% of the time, respiratory therapists, 85%, P = .7, and residents, 67%, P = .04. Interventions targeting resident SA are needed within the PICU, especially for high-risk patients.

OBJECTIVES: Pediatric acute respiratory distress syndrome (PARDS) is a source of substantial morbidity and mortality in the PICU, and different plasma biomarkers have identified different PARDS and ARDS subgroups. We have a poor understanding of how these biomarkers change over time and with changing lung injuries. We sought to determine how biomarker levels change over PARDS course, whether they are correlated, and whether they are different in critically ill non-PARDS patients.DESIGN: Two-center prospective observational study. SETTING:Two quaternary care academic children's hospitals PATIENTS: Subjects under 18 years of age admitted to the PICU who were intubated and met the Second Pediatric Acute Lung Injury Consensus Conference-2 PARDS diagnostic criteria and nonintubated critically ill subjects without apparent lung disease. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:Plasma samples were obtained on study days 1, 3, 7, and 14. The levels of 16 biomarkers were measured using a fluorometric bead-based assay. Compared with non-PARDS subjects, on day 1 PARDS subjects had increased concentrations of tumor necrosis factor-alpha, interleukin (IL)-8, interferon-γ, IL17, granzyme B, soluble intercellular adhesion molecule-1 (sICAM1), surfactant protein D, and IL18 but reduced matrix metalloproteinase 9 (MMP-9) concentrations (all p < 0.05). Day 1 biomarker concentrations and PARDS severity were not correlated. Over PARDS course, changes in 11 of the 16 biomarkers positively correlated with changing lung injury with sICAM1 (R = 0.69, p = 2.2 × 10 -16 ) having the strongest correlation. By Spearman rank correlation of biomarker concentrations in PARDS subjects, we identified two patterns. One had elevations of plasminogen activator inhibitor-1, MMP-9, and myeloperoxidase, and the other had higher inflammatory cytokines.CONCLUSIONS: sICAM1 had the strongest positive correlation with worsening lung injury across all study time points suggesting that it is perhaps the most biologically relevant of the 16 analytes. There was no correlation between biomarker concentration on day 1 and day 1 PARDS severity; however, changes in most biomarkers over time positively correlated with changing lung injury. Finally, in day 1 samples, 7 of the 16 biomarkers were not significantly different between PARDS and critically ill non-PARDS subjects. These data highlight the difficulty of using plasma biomarkers to identify organ-specific pathology in critically ill patients.