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Objective: To develop an instrument to examine tobacco-free campus policy components. Participants: Missouri two- and four-year, specialized/technical, and religious colleges and universities (N = 76). Methods: The instrument was informed via literature review and expert interviews. Coder agreement was strong (κ =.80). Qualitative policy language examples were identified. Results: Model policy components including consideration for population, prohibited products, location restrictions, enforcement, consequences, promotions, communications, cessation, designated smoking areas and exemptions; comprehensive policies included all populations, for all tobacco products, and at all locations on the campus. Nineteen campuses had comprehensive tobacco-free policies, five had comprehensive smoke-free policies (cigarettes and e-cigarettes), and no policy included all model components. Fifty-two were non-comprehensive. Conclusions: This instrument can allow campuses to identify components for comprehensive and model tobacco-free campus policies and assist officials in improving policy language. Future research can use this instrument to examine the effectiveness of components and their impact on tobacco use outcomes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Congenital heart defects (CHD) are the most common birth defects and previous estimates report the disease affects 1% of births annually in the United States. To date, CHD prevalence estimates are inconsistent due to varied definitions, data reliant on birth registries, and are geographically limited. These data sources may not be representative of the total prevalence of the CHD population. It is therefore important to derive high-quality, population-based estimates of the prevalence of CHD to help care for this vulnerable population. METHODS: We performed a descriptive, retrospective 8-year analysis using all-payer claims data from Colorado from 2012 to 2019. Children with CHD were identified by applying International Classification of Diseases-Ninth Revision (ICD-9) and International Classification of Diseases-Tenth Revision (ICD-10) diagnosis codes from the American Heart Association--American College of Cardiology harmonized cardiac codes. We included children with CHD <18 years of age who resided in Colorado, had a documented zip code, and had at least 1 health care claim. CHD type was categorized as simple, moderate, and severe disease. Association with comorbid conditions and genetic diagnoses were analyzed using χ² test. We used direct standardization to calculate adjusted prevalence rates, controlling for age, sex, primary insurance provider, and urban-rural residence. RESULTS: We identified 1 566 328 children receiving care in Colorado from 2012 to 2019. Of those, 30 512 children had at least 1 CHD diagnosis, comprising 1.95% (95% CI, 1.93-1.97) of the pediatric population. Over half of the children with CHD also had at least 1 complex chronic condition. After direct standardization, the adjusted prevalence rates show a small increase in simple severity diagnoses across the study period (adjusted rate of 11.5 [2012]-14.4 [2019]; P<0.001). CONCLUSIONS: The current study is the first population-level analysis of pediatric CHD in the United States. Using administrative claims data, our study found a higher CHD prevalence and comorbidity burden compared with previous estimates. [ABSTRACT FROM AUTHOR]

Numerous studies have demonstrated that endostatin (ES), a potent angiostatic peptide derived from collagen type XVIII α 1 chain and encoded by COL18A1, is elevated in pulmonary arterial hypertension (PAH). It is important to note that elevated ES has consistently been associated with altered hemodynamics, poor functional status, and adverse outcomes in adult and pediatric PAH. This study used serum samples from patients with Group I PAH and plasma and tissue samples derived from the Sugen/hypoxia rat pulmonary hypertension model to define associations between COL18A1/ES and disease development, including hemodynamics, right ventricle (RV) remodeling, and RV dysfunction. Using cardiac magnetic resonance imaging and advanced hemodynamic assessments with pressure–volume loops in patients with PAH to assess RV–pulmonary arterial coupling, we observed a strong relationship between circulating ES levels and metrics of RV structure and function. Specifically, RV mass and the ventricular mass index were positively associated with ES, whereas RV ejection fraction and RV–pulmonary arterial coupling were inversely associated with ES levels. Our animal data demonstrate that the development of pulmonary hypertension is associated with increased COL18A1/ES in the heart as well as the lungs. Disease-associated increases in COL18A1 mRNA and protein were most pronounced in the RV compared with the left ventricle and lung. COL18A1 expression in the RV was strongly associated with disease-associated changes in RV mass, fibrosis, and myocardial capillary density. These findings indicate that COL18A1/ES increases early in disease development in the RV and implicates COL18A1/ES in pathologic RV dysfunction in PAH. [ABSTRACT FROM AUTHOR]

Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific "answers," but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks. Plain Language Summary: As the Arctic Ocean gets warmer, melting ice is opening up new opportunities for fishing. However, we don't know where fish will go and how they can be managed sustainably. An important first step is to figure out which unknowns we can solve quickly with more research, and what is so uncertain that we will have to make decisions without ideal information. In this paper, we looked at uncertainty in a set of global models that predict how fish populations might shift in the next 10–25 years. Overall, these models show that fish populations might increase in the northern parts of the Arctic while decreasing in the south. But the models make very different predictions, and some disagree on whether fish populations will increase or decrease in certain areas. A major source of uncertainty is how sea ice will change, and how ocean life will respond. Therefore, this is a priority area to invest in long‐term research and better models. Overall, these models are too uncertain to rely on for specific management decisions about Arctic fishing. Instead, scientists and decision makers can use them to shape more informed discussions about potential trade‐offs and risks of future fishing in the Arctic. Key Points: Variation and disagreement in marine ecosystem model projections are too high to be informative for near‐term Arctic fisheries managementInsufficient inclusion and knowledge of sea ice cover and sea ice productivity dynamics are major drivers of uncertaintyResearchers should be transparent about uncertainty and risk; present model projections as the basis for hypotheses and scenario planning [ABSTRACT FROM AUTHOR]

Quantitative analysis of electroencephalography (qEEG) is a potential source of biomarkers for neonatal encephalopathy (NE). However, prior studies using qEEG in NE were limited in their generalizability due to individualized techniques for calculating qEEG features or labor-intensive pre-selection of EEG data. We piloted a fully automated method using commercially available software to calculate the suppression ratio (SR), absolute delta power, and relative delta, theta, alpha, and beta power from EEG of neonates undergoing 72 h of therapeutic hypothermia (TH) for NE between April 20, 2018, and November 4, 2019. We investigated the association of qEEG with degree of encephalopathy (modified Sarnat score), severity of neuroimaging abnormalities following TH (National Institutes of Child Health and Development Neonatal Research Network [NICHD-NRN] score), and presence of seizures. Thirty out of 38 patients met inclusion criteria. A more severe modified Sarnat score was associated with higher SR during all phases of TH, lower absolute delta power during all phases except rewarming, and lower relative delta power during the last 24 h of TH. In 21 patients with neuroimaging data, a worse NICHD-NRN score was associated with higher SR, lower absolute delta power, and higher relative beta power during all phases. QEEG features were not significantly associated with the presence of seizures after correction for multiple comparisons. Our results are consistent with those of prior studies using qEEG in NE and support automated qEEG analysis as an accessible, generalizable method for generating biomarkers of NE and response to TH. Additionally, we found evidence of an immature relative frequency composition in neonates with more severe brain injury, suggesting that automated qEEG analysis may have a use in the assessment of brain maturity. [ABSTRACT FROM AUTHOR]

Neonatal hypoxic-ischemic encephalopathy (HIE) is the leading cause of acquired neonatal brain injury with the risk of developing serious neurological sequelae and death. An accurate and robust prediction of short- and long-term outcomes may provide clinicians and families with fundamental evidence for their decision-making, the design of treatment strategies, and the discussion of developmental intervention plans after discharge. Diffusion tensor imaging (DTI) is one of the most powerful neuroimaging tools with which to predict the prognosis of neonatal HIE by providing microscopic features that cannot be assessed by conventional magnetic resonance imaging (MRI). DTI provides various scalar measures that represent the properties of the tissue, such as fractional anisotropy (FA) and mean diffusivity (MD). Since the characteristics of the diffusion of water molecules represented by these measures are affected by the microscopic cellular and extracellular environment, such as the orientation of structural components and cell density, they are often used to study the normal developmental trajectory of the brain and as indicators of various tissue damage, including HIE-related pathologies, such as cytotoxic edema, vascular edema, inflammation, cell death, and Wallerian degeneration. Previous studies have demonstrated widespread alteration in DTI measurements in severe cases of HIE and more localized changes in neonates with mild-to-moderate HIE. In an attempt to establish cutoff values to predict the occurrence of neurological sequelae, MD and FA measurements in the corpus callosum, thalamus, basal ganglia, corticospinal tract, and frontal white matter have proven to have an excellent ability to predict severe neurological outcomes. In addition, a recent study has suggested that a data-driven, unbiased approach using machine learning techniques on features obtained from whole-brain image quantification may accurately predict the prognosis of HIE, including for mild-to-moderate cases. Further efforts are needed to overcome current challenges, such as MRI infrastructure, diffusion modeling methods, and data harmonization for clinical application. In addition, external validation of predictive models is essential for clinical application of DTI to prognostication. [ABSTRACT FROM AUTHOR]

Early diagnosis of acute brain injury (ABI) is critical for patients on veno-arterial extracorporeal membrane oxygenation (V-A ECMO) to guide anticoagulation strategy; however, neurological assessment in ECMO is often limited by patient sedation.In this pilot study of adults from June 2018 to May 2019, plasma samples of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), and tubulin associated unit (Tau) were collected daily after V-A ECMO cannulation and measured using a multiplex platform. Primary outcomes were occurrence of ABI, assessed clinically, and neurologic outcome, assessed by modified Rankin Scale (mRS).Of 20 consented patients (median age = 48.5°years; 55% female), 8 (40%) had ABI and 15 (75%) had unfavorable neurologic outcome at discharge. 10 (50%) patients were centrally cannulated. Median duration on ECMO was 4.5°days (IQR: 2.5–9.5). Peak GFAP, NFL, and Tau levels were higher in patients with ABI vs. without (AUC = 0.77; 0.85; 0.57, respectively) and in patients with unfavorable vs. favorable neurologic outcomes (AUC = 0.64; 0.59; 0.73, respectively). GFAP elevated first, NFL elevated to the highest degree, and Tau showed limited change regardless of ABI.Further studies are warranted to determine how plasma biomarkers may facilitate early detection of ABIs in V-A ECMO to assist timely clinical decision-making. [ABSTRACT FROM AUTHOR]