Fundamentals of adhesion of organic compounds and aqueous cleaning of glass and metal surfaces : applications in the pharmaceutical and chemical industries

In this thesis we studied the physics and chemistry of the adhesion of various classes of organic compounds to glass and stainless steel surfaces, by using diagnostic aqueous cleaning solutions and other techniques. We defined the thermodynamic requirements for aqueous cleaning based on extensive experimental and theoretical work. Novel cleaning diagrams are introduced, based on electrostatic interactions between organic compounds and solid surfaces, to facilitate the design of aqueous cleaning systems. We studied the mass transfer parameters for selected situations. The electrochemical Pourbaix-diagrams were used to explain the effect of hydrogen peroxide added to aqueous solutions and to avoid corrosion of solid surfaces during cleaning. On the basis of this work, we defined the necessary requirements for new non-stick materials to be developed. The use of such new materials should minimize the adhesion of organic materials to vessel surfaces

Decay of bow thruster induced near-bed flow velocities at a vertical quay wall: A field measurement

During berthing operations of large vessels, the bow thruster jet deflecting on the quay wall and the bed can lead to high flow velocities near the bed. This may scour the bed when it is left unprotected, causing instability of the adjacent quay wall. Due to the complex flow field of the reflected jet, the decay in near-bed flow velocities perpendicular to the quay wall is unknown. This results in uncertainties in the design of bed protections, especially in the required width.  In this research, the decay of the near-bed flow velocity perpendicular and parallel to the quay wall induced by a 4-channel bow thruster is studied. Field measurements have been conducted in the North Sea Port of Gent with one of the largest Dutch inland vessels. The near-bed flow velocities have been measured at multiple distances from the quay wall. For the flow velocity measurements four main parameters have been varied: the applied bow thruster power, quay wall clearance, number of thrusters, and the lateral distance between jet axis and measurement sensors. The highest flow velocities were measured near the quay wall, rapidly declining while moving away from the quay. Comparison of the measurement results to the Dutch and German guidelines generally leads to the conclusion that these guidelines are conservative. In addition, the dependency of the velocity on the total travelled distance by the jet as given in the Dutch method is not reflected in the measurement results. Furthermore, fundamentally different outcomes on the influence of the quay wall clearance on the near-bed flow velocity have been found. When the measured near-bed flow velocities are used as the sole input to calculate the required bed protection, significantly smaller rock sizes and asphalt mattress thickness would be necessary to withstand the hydraulic load of the jet in comparison to current guidelines. Further studies with different vessels and direct measurement of the efflux velocity of the thrusters are recommended

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ^| ≈ 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.</p

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Outcome Prediction in Postanoxic Coma With Deep Learning

OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent.DESIGN: Prospective cohort study.SETTING: Medical ICU of five teaching hospitals in the Netherlands.PATIENTS: Eight-hundred ninety-five consecutive comatose patients after cardiac arrest.INTERVENTIONS: None.MEASUREMENTS AND MAIN RESULTS: Continuous electroencephalogram was recorded during the first 3 days after cardiac arrest. Functional outcome at 6 months was classified as good (Cerebral Performance Category 1-2) or poor (Cerebral Performance Category 3-5). We trained a convolutional neural network, with a VGG architecture (introduced by the Oxford Visual Geometry Group), to predict neurologic outcome at 12 and 24 hours after cardiac arrest using electroencephalogram epochs and outcome labels as inputs. Output of the network was the probability of good outcome. Data from two hospitals were used for training and internal validation (n = 661). Eighty percent of these data was used for training and cross-validation, the remaining 20% for independent internal validation. Data from the other three hospitals were used for external validation (n = 234). Prediction of poor outcome was most accurate at 12 hours, with a sensitivity in the external validation set of 58% (95% CI, 51-65%) at false positive rate of 0% (CI, 0-7%). Good outcome could be predicted at 12 hours with a sensitivity of 48% (CI, 45-51%) at a false positive rate of 5% (CI, 0-15%) in the external validation set.CONCLUSIONS: Deep learning of electroencephalogram signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual electroencephalogram assessment by trained electroencephalogram experts. Our approach offers the potential for objective and real time, bedside insight in the neurologic prognosis of comatose patients after cardiac arrest.</p

Cognition, emotional state, and quality of life of survivors after cardiac arrest with rhythmic and periodic EEG patterns

Aim: Rhythmic and periodic patterns (RPPs) on the electroencephalogram (EEG) in comatose patients after cardiac arrest have been associated with high case fatality rates. A good neurological outcome according to the Cerebral Performance Categories (CPC) has been reported in up to 10% of cases. Data on cognitive, emotional, and quality of life outcomes are lacking. We aimed to provide insight into these outcomes at one-year follow-up. Methods: We assessed outcome of surviving comatose patients after cardiac arrest with RPPs included in the ‘treatment of electroencephalographic status epilepticus after cardiopulmonary resuscitation’ (TELSTAR) trial at one-year follow-up, including the CPC for functional neurological outcome, a cognitive assessment, the hospital anxiety and depression scale (HADS) for emotional outcomes, and the 36-item short-form health survey (SF-36) for quality of life. Cognitive impairment was defined as a score of more than 1.5 SD below the mean on = 2 (sub)tests within a cognitive domain. Results: Fourteen patients were included (median age 58 years, 21% female), of whom 13 had a cognitive impairment. Eleven of 14 were impaired in memory, 9/14 in executive functioning, and 7/14 in attention. The median scores on the HADS and SF-36 were all worse than expected. Based on the CPC alone, 8/14 had a good outcome (CPC 1–2). Conclusion: Nearly all cardiac arrest survivors with RPPs during the comatose state have cognitive impairments at one-year follow-up. The incidence of anxiety and depression symptoms seem relatively high and quality of life relatively poor, despite ‘good’ outcomes according to the CPC

Myoclonus in comatose patients with electrographic status epilepticus after cardiac arrest:Corresponding EEG patterns, effects of treatment and outcomes

Objective: To clarify the significance of any form of myoclonus in comatose patients after cardiac arrest with rhythmic and periodic EEG patterns (RPPs) by analyzing associations between myoclonus and EEG pattern, response to anti-seizure medication and neurological outcome. Design: Post hoc analysis of the prospective randomized Treatment of ELectroencephalographic STatus Epilepticus After Cardiopulmonary Resuscitation (TELSTAR) trial. Setting: Eleven ICUs in the Netherlands and Belgium. Patients: One hundred and fifty-seven adult comatose post-cardiac arrest patients with RPPs on continuous EEG monitoring. Interventions: Anti-seizure medication vs no anti-seizure medication in addition to standard care. Measurements and Main Results: Of 157 patients, 98 (63%) had myoclonus at inclusion. Myoclonus was not associated with one specific RPP type. However, myoclonus was associated with a smaller probability of a continuous EEG background pattern (48% in patients with vs 75% without myoclonus, odds ratio (OR) 0.31; 95% confidence interval (CI) 0.16–0.64) and earlier onset of RPPs (24% vs 9% within 24 hours after cardiac arrest, OR 3.86;95% CI 1.64–9.11). Myoclonus was associated with poor outcome at three months, but not invariably so (poor neurological outcome in 96% vs 82%, p = 0.004). Anti-seizure medication did not improve outcome, regardless of myoclonus presence (6% good outcome in the intervention group vs 2% in the control group, OR 0.33; 95% CI 0.03–3.32). Conclusions: Myoclonus in comatose patients after cardiac arrest with RPPs is associated with poor outcome and discontinuous or suppressed EEG. However, presence of myoclonus does not interact with the effects of anti-seizure medication and cannot predict a poor outcome without false positives.</p

Early electroencephalography for outcome prediction of postanoxic coma:A prospective cohort study

OBJECTIVE: To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest.METHODS: In a 5-center prospective cohort study, we included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five-minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients' actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (&lt;10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category [CPC] = 1-2) or poor (CPC = 3-5).RESULTS: We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval [CI] = 0.42-0.51) at 12 hours and 0.30 (95% CI = 0.26-0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99-1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46-0.55) and specificity of 0.91 (95% CI = 0.88-0.93); at 24 hours or later, specificity for the prediction of good outcome was &lt;0.90.INTERPRETATION: EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019.</p

Outcome Prediction of Postanoxic Coma:A Comparison of Automated Electroencephalography Analysis Methods

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest