Central nervous system infection in the intensive care unit: Development and validation of a multi-parameter diagnostic prediction tool to identify suspected patients.

BACKGROUND: Central nervous system infections (CNSI) are diseases with high morbidity and mortality, and their diagnosis in the intensive care environment can be challenging. Objective: To develop and validate a diagnostic model to quickly screen intensive care patients with suspected CNSI using readily available clinical data. METHODS: Derivation cohort: 783 patients admitted to an infectious diseases intensive care unit (ICU) in Oswaldo Cruz Foundation, Rio de Janeiro RJ, Brazil, for any reason, between 01/01/2012 and 06/30/2019, with a prevalence of 97 (12.4%) CNSI cases. Validation cohort 1: 163 patients prospectively collected, between 07/01/2019 and 07/01/2020, from the same ICU, with 15 (9.2%) CNSI cases. Validation cohort 2: 7,270 patients with 88 CNSI (1.21%) admitted to a neuro ICU in Chicago, IL, USA between 01/01/2014 and 06/30/2019. Prediction model: Multivariate logistic regression analysis was performed to construct the model, and Receiver Operating Characteristic (ROC) curve analysis was used for model validation. Eight predictors-age <56 years old, cerebrospinal fluid white blood cell count >2 cells/mm3, fever (≥38°C/100.4°F), focal neurologic deficit, Glasgow Coma Scale <14 points, AIDS/HIV, and seizure-were included in the development diagnostic model (P<0.05). RESULTS: The pool data's model had an Area Under the Receiver Operating Characteristics (AUC) curve of 0.892 (95% confidence interval 0.864-0.921, P<0.0001). CONCLUSIONS: A promising and straightforward screening tool for central nervous system infections, with few and readily available clinical variables, was developed and had good accuracy, with internal and external validity.

Denitrification as an N2O sink.

The strong greenhouse gas nitrous oxide (N2O) can be emitted from wastewater treatment systems as a byproduct of ammonium oxidation and as the last intermediate in the stepwise reduction of nitrate to N2 by denitrifying organisms. A potential strategy to reduce N2O emissions would be to enhance the activity of N2O reductase (NOS) in the denitrifying microbial community. A survey of existing literature on denitrification in wastewater treatment systems showed that the N2O reducing capacity (VmaxN2O→N2) exceeded the capacity to produce N2O (VmaxNO3→N2O) by a factor of 2-10. This suggests that denitrification can be an effective sink for N2O, potentially scavenging a fraction of the N2O produced by ammonium oxidation or abiotic reactions. We conducted a series of incubation experiments with freshly sampled activated sludge from a wastewater treatment system in Oslo and found that the ratio α = VmaxN2O→N2/VmaxNO3→N2O fluctuated between 2 and 5 in samples taken at intervals over a period of 5 weeks. Adding a cocktail of carbon substrates resulted in increasing rates, but had no significant effect on α. Based on these results - complemented with qPCR and metaproteomic data - we discuss whether the overcapacity to reduce N2O can be ascribed to gene/protein abundance ratios (nosZ/nir), or whether in-cell competition between the reductases for electrons could be of greater importance.