Slit2 prevents neutrophil recruitment and renal ischemia-reperfusion injury.

Neutrophils recruited to the postischemic kidney contribute to the pathogenesis of ischemia-reperfusion injury (IRI), which is the most common cause of renal failure among hospitalized patients. The Slit family of secreted proteins inhibits chemotaxis of leukocytes by preventing activation of Rho-family GTPases, suggesting that members of this family might modulate the recruitment of neutrophils and the resulting IRI. Here, in static and microfluidic shear assays, Slit2 inhibited multiple steps required for the infiltration of neutrophils into tissue. Specifically, Slit2 blocked the capture and firm adhesion of human neutrophils to inflamed vascular endothelial barriers as well as their subsequent transmigration. To examine whether these observations were relevant to renal IRI, we administered Slit2 to mice before bilateral clamping of the renal pedicles. Assessed at 18 hours after reperfusion, Slit2 significantly inhibited renal tubular necrosis, neutrophil and macrophage infiltration, and rise in plasma creatinine. In vitro, Slit2 did not impair the protective functions of neutrophils, including phagocytosis and superoxide production, and did not inhibit neutrophils from killing the extracellular pathogen Staphylococcus aureus. In vivo, administration of Slit2 did not attenuate neutrophil recruitment or bacterial clearance in mice with ascending Escherichia coli urinary tract infections and did not increase the bacterial load in the livers of mice infected with the intracellular pathogen Listeria monocytogenes. Collectively, these results suggest that Slit2 may hold promise as a strategy to combat renal IRI without compromising the protective innate immune response.

Heart-Brain 346-7 Score: the development and validation of a simple mortality prediction score for carbon monoxide poisoning utilizing deep learning.

INTRODUCTION: Acute mortality from carbon monoxide poisoning is 1-3%. The long-term mortality risk of survivors of carbon monoxide poisoning is doubled compared to age-matched controls. Cardiac involvement also increases mortality risk. We built a clinical risk score to identify carbon monoxide-poisoned patients at risk for acute and long-term mortality. METHODS: We performed a retrospective analysis. We identified 811 adult carbon monoxide-poisoned patients in the derivation cohort, and 462 adult patients in the validation cohort. We utilized baseline demographics, laboratory values, hospital charge transactions, discharge disposition, and clinical charting information in the electronic medical record in Stepwise Akaike's Information Criteria with Firth logistic regression to determine optimal parameters to create a prediction model. RESULTS: In the derivation cohort, 5% had inpatient or 1-year mortality. Three variables following the final Firth logistic regression minimized Stepwise Akaike's Information Criteria: altered mental status, age, and cardiac complications. The following predict inpatient or 1-year mortality: age > 67, age > 37 with cardiac complications, age > 47 with altered mental status, or any age with cardiac complications and altered mental status. The sensitivity of the score was 82% (95% confidence interval: 65-92%), the specificity was 80% (95% confidence interval: 77-83%), negative predictive value was 99% (95% confidence interval: 98-100%), positive predictive value 17% (95% confidence interval: 12-23%), and the area under the receiver operating characteristic curve was 0.81 (95% confidence interval: 0.74-0.87). A score above the cut-off point of -2.9 was associated with an odds ratio of 18 (95% confidence interval: 8-40). In the validation cohort (462 patients), 4% had inpatient death or 1-year mortality. The score performed similarly in the validation cohort: sensitivity was 72% (95% confidence interval: 47-90%), specificity was 69% (95% confidence interval: 63-73%), negative predictive value was 98% (95% confidence interval: 96-99%), positive predictive value was 9% (95% confidence interval: 5-15%) and the area under the receiver operating characteristic curve was 0.70 (95% confidence interval: 60%-81%). CONCLUSIONS: We developed and validated a simple, clinical-based scoring system, the Heart-Brain 346-7 Score to predict inpatient and long-term mortality based on the following: age > 67, age > 37 with cardiac complications, age > 47 with altered mental status, or any age with cardiac complications and altered mental status. With further validation, this score will hopefully aid decision-making to identify carbon monoxide-poisoned patients with higher mortality risk.

Defining linkages between the GSC and NSF's LTER program: how the Ecological Metadata Language (EML) relates to GCDML and other outcomes.

The Genomic Standards Consortium (GSC) invited a representative of the Long-Term Ecological Research (LTER) to its fifth workshop to present the Ecological Metadata Language (EML) metadata standard and its relationship to the Minimum Information about a Genome/Metagenome Sequence (MIGS/MIMS) and its implementation, the Genomic Contextual Data Markup Language (GCDML). The LTER is one of the top National Science Foundation (NSF) programs in biology since 1980, representing diverse ecosystems and creating long-term, interdisciplinary research, synthesis of information, and theory. The adoption of EML as the LTER network standard has been key to build network synthesis architectures based on high-quality standardized metadata. EML is the NSF-recognized metadata standard for LTER, and EML is a criteria used to review the LTER program progress. At the workshop, a potential crosswalk between the GCDML and EML was explored. Also, collaboration between the LTER and GSC developers was proposed to join efforts toward a common metadata cataloging designer's tool. The community adoption success of a metadata standard depends, among other factors, on the tools and trainings developed to use the standard. LTER's experience in embracing EML may help GSC to achieve similar success. A possible collaboration between LTER and GSC to provide training opportunities for GCDML and the associated tools is being explored. Finally, LTER is investigating EML enhancements to better accommodate genomics data, possibly integrating the GCDML schema into EML. All these action items have been accepted by the LTER contingent, and further collaboration between the GSC and LTER is expected.