Development of a Low-Cost Smart Sensor GNSS System for Real-Time Positioning and Orientation for Floating Offshore Wind Platform.

A low-cost smart sensor GNSS system has been developed to provide accurate real-time position and orientation measurements on a floating offshore wind platform. The approach chosen to offer a viable and reliable solution for this application is based on the use of the well-known advantages of the GNSS system as the main driver for enhancing the accuracy of positioning. For this purpose, the data reported in this work are captured through a GNSS receiver operating over multiple frequency bands (L1, L2, L5) and combining signals from different constellations of navigation satellites (GPS, Galileo, and GLONASS), and they are processed through the precise point positioning (PPP) and real-time kinematic (RTK) techniques. Furthermore, aiming to improve global positioning, the processing unit fuses the results obtained with the data acquired through an inertial measurement unit (IMU), reaching final accuracy of a few centimeters. To validate the system designed and developed in this proposal, three different sets of tests were carried out in a (i) rotary table at the laboratory, (ii) GNSS simulator, and (iii) real conditions in an oceanic buoy at sea. The real-time positioning solution was compared to solutions obtained by post-processing techniques in these three scenarios and similar results were satisfactorily achieved.

The challenge of diagnosing Guillain-Barre syndrome in patients with COVID-19 in the intensive care unit.

Various neurological complications have been described in COVID-19 patients, especially Guillain-Barre syndrome (GBS). The underlying mechanisms on the association between SARS-CoV-2 infection and GBS remain unclear, but several hypotheses have been proposed. It seems that post-SARS-CoV-2 GBS shares many characteristics with classic post-infectious GBS; however, it may occur in sedated and intubated patients hospitalized in the intensive care unit for SARS-CoV-2 acute respiratory distress syndrome, which presents challenges in the diagnosis and treatment of GBS. In this study, we describe three cases of post-SARS-CoV-2 GBS that were hospitalized in the intensive care unit.

Prevention of contrast-induced nephropathy by N-acetylcysteine in critically ill patients: different definitions, different results.

PURPOSE: The use of N-acetylcysteine (NAC) for preventing contrast induced nephropathy (CIN) is debated in the intensive care unit. NAC may alter the concentration of serum creatinine and interfere with CIN diagnosis. The effectiveness of NAC was evaluated with a special attention on its specific effect on creatinine levels compared to cystatin C. METHODS: In a first period, we prospectively enrolled patients receiving saline and low osmolality contrast media for 140 exams in 2 intensive care units with opposite policies regarding the use of NAC. Renal impairment was defined by both the classical CIN and the "sensitive" Acute Kidney Injury Network (AKIN) (taking creatinine and diuresis) definitions. In a second period, we compared the evolution of serum creatinine and cystatin C after 23 additional contrast examinations under NAC. RESULTS: Seventy exams with and without NAC were compared in the first period. Risk factors for CIN were similar in the two intensive care unit populations. No difference in CIN incidence was found with and without NAC, using the CIN (10/70 vs 15/70) or the AKIN (24/70 vs 22/70) definition. Interestingly, NAC seemed to reduce renal impairment when the creatinine criterion of the AKIN definition was considered alone [9% vs 21%, P=.033]. Overall, the incidence of renal impairment was 18%, 33% and 15% using the CIN definition, the AKIN, or using AKIN with creatinine alone. Serum creatinine significantly decreased after exams with NAC while cystatin C remained stable. CONCLUSION: The incidence of CIN does not seem to be influenced by NAC, except if small changes in creatinine only are considered.

Model for predicting short-term mortality of severe sepsis.

INTRODUCTION: To establish a prognostic model for predicting 14-day mortality in ICU patients with severe sepsis overall and according to place of infection acquisition and to sepsis episode number. METHODS: In this prospective multicentre observational study on a multicentre database (OUTCOMEREA) including data from 12 ICUs, 2268 patients with 2737 episodes of severe sepsis were randomly divided into a training cohort (n = 1458) and a validation cohort (n = 810). Up to four consecutive severe sepsis episodes per patient occurring within the first 28 ICU days were included. We developed a prognostic model for predicting death within 14 days after each episode, based on patient data available at sepsis onset. RESULTS: Independent predictors of death were logistic organ dysfunction (odds ratio (OR), 1.22 per point, P < 10-4), septic shock (OR, 1.40; P = 0.01), rank of severe sepsis episode (1 reference, 2: OR, 1.26; P = 0.10 >or= 3: OR, 2.64; P < 10-3), multiple sources of infection (OR; 1.45, P = 0.03), simplified acute physiology score II (OR, 1.02 per point; P < 10-4), McCabe score ([greater than or equal to]2) (OR, 1.96; P < 10-4), and number of chronic co-morbidities (1: OR, 1.75; P < 10-3, >or= 2: OR, 2.24, P < 10-3). Validity of the model was good in whole cohorts (AUC-ROC, 0.76; 95%CI, 0.74 to 0.79; and HL Chi-square: 15.3 (P = 0.06) for all episodes pooled). CONCLUSIONS: In ICU patients, a prognostic model based on a few easily obtained variables is effective in predicting death within 14 days after the first to fourth episode of severe sepsis complicating community-, hospital-, or ICU-acquired infection.