Expression, Purification and Crystallization of Wheat Profilin (Tri a 12)

Profilin from wheat (Triticum aestivum) has been identified as an allergen (Tri a 12). The recombinant 14 kDa protein was produced in Escherichia coli, purified and crystallized using the hangingdrop vapour-diffusion method. A diffraction-data set was collected in-house from a single crystal to a resolution of 3.3 Å. The crystals belonged to space group P3221, with unit-cell parameters a = b = 58.9 Å, c = 82.5 Å, α = β = 90° and γ = 120°. (doi: 10.5562/cca1790

Framework and baseline examination of the German National Cohort (NAKO)

The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19–74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2–3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4–5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-022-00890-5

Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators

Membrane oxygenators are devices that benefit from automatic control. This is especially true for implantable membrane oxygenators—a class of wearable rehabilitation devices that show high potential for fast recovery after lung injury. We present a performance comparison for reference tracking of carbon dioxide partial pressure between three control algorithms—a classical proportional-integral (PI) controller, a modern non-linear model predictive controller, and a novel deep reinforcement learning controller. The results are based on simulation studies of an improved compartmental model of a membrane oxygenator. The compartmental model of the oxygenator was improved by decoupling the oxygen kinetics from the system and only using the oxygen saturation as an input to the model. Both the gas flow rate and blood flow rate were used as the manipulated variable of the controllers. All three controllers were able to track references satisfactorily, based on several performance metrics. The PI controller had the fastest response, with an average rise time and settling time of 1.18 s and 2.24 s and the lowest root mean squared error of 1.06 mmHg. The NMPC controller showed the lowest steady state error of 0.17 mmHg and reached the reference signal with less than 2% error in 90% of the cases within 15 s. The PI and RL reached the reference with less than 2% error in 84% and 50% of the cases, respectively, and showed a steady state error of 0.29 mmHg and 0.5 mmHg

Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators

Membrane oxygenators are devices that benefit from automatic control. This is especially true for implantable membrane oxygenators—a class of wearable rehabilitation devices that show high potential for fast recovery after lung injury. We present a performance comparison for reference tracking of carbon dioxide partial pressure between three control algorithms—a classical proportional-integral (PI) controller, a modern non-linear model predictive controller, and a novel deep reinforcement learning controller. The results are based on simulation studies of an improved compartmental model of a membrane oxygenator. The compartmental model of the oxygenator was improved by decoupling the oxygen kinetics from the system and only using the oxygen saturation as an input to the model. Both the gas flow rate and blood flow rate were used as the manipulated variable of the controllers. All three controllers were able to track references satisfactorily, based on several performance metrics. The PI controller had the fastest response, with an average rise time and settling time of 1.18 s and 2.24 s and the lowest root mean squared error of 1.06 mmHg. The NMPC controller showed the lowest steady state error of 0.17 mmHg and reached the reference signal with less than 2% error in 90% of the cases within 15 s. The PI and RL reached the reference with less than 2% error in 84% and 50% of the cases, respectively, and showed a steady state error of 0.29 mmHg and 0.5 mmHg

O-Mannosylation precedes and potentially controls the N-glycosylation of a yeast cell wall glycoprotein

Secretory proteins in yeast are N- and O-glycosylated while they enter the endoplasmic reticulum. N-glycosylation is initiated by the oligosaccharyl transferase complex and O-mannosylation is initiated by distinct O-mannosyltransferase complexes of the protein mannosyl transferase Pmt1/Pmt2 and Pmt4 families. Using covalently linked cell-wall protein 5 (Ccw5) as a model, we show that the Pmt4 and Pmt1/Pmt2 mannosyltransferases glycosylate different domains of the Ccw5 protein, thereby mannosylating several consecutive serine and threonine residues. In addition, it is shown that O-mannosylation by Pmt4 prevents N-glycosylation by blocking the hydroxy amino acid of the single N-glycosylation site present in Ccw5. These data prove that the O- and N-glycosylation machineries compete for Ccw5; therefore O-mannosylation by Pmt4 precedes N-glycosylation

Suitable CO₂ Solubility Models for Determination of the CO₂ Removal Performance of Oxygenators

CO2 removal via membrane oxygenators during lung protective ventilation has become a reliable clinical technique. For further optimization of oxygenators, accurate prediction of the CO2 removal rate is necessary. It can either be determined by measuring the CO2 content in the exhaust gas of the oxygenator (sweep flow-based) or using blood gas analyzer data and a CO2 solubility model (blood-based). In this study, we determined the CO2 removal rate of a prototype oxygenator utilizing both methods in in vitro trials with bovine and in vivo trials with porcine blood. While the sweep flow-based method is reliably accurate, the blood-based method depends on the accuracy of the solubility model. In this work, we quantified performances of four different solubility models by calculating the deviation of the CO2 removal rates determined by both methods. Obtained data suggest that the simplest model (Loeppky) performs better than the more complex ones (May, Siggaard-Anderson, and Zierenberg). The models of May, Siggaard-Anderson, and Zierenberg show a significantly better performance for in vitro bovine blood data than for in vivo porcine blood data. Furthermore, the suitability of the Loeppky model parameters for bovine blood (in vitro) and porcine blood (in vivo) is evaluated

Water as a Blood Model for Determination of CO2 Removal Performance of Membrane Oxygenators

CO2 removal via membrane oxygenators has become an important and reliable clinical technique. Nevertheless, oxygenators must be further optimized to increase CO2 removal performance and to reduce severe side effects. Here, in vitro tests with water can significantly reduce costs and effort during development. However, they must be able to reasonably represent the CO2 removal performance observed with blood. In this study, the deviation between the CO2 removal rate determined in vivo with porcine blood from that determined in vitro with water is quantified. The magnitude of this deviation (approx. 10%) is consistent with results reported in the literature. To better understand the remaining difference in CO2 removal rate and in order to assess the application limits of in vitro water tests, CFD simulations were conducted. They allow to quantify and investigate the influences of the differing fluid properties of blood and water on the CO2 removal rate. The CFD results indicate that the main CO2 transport resistance, the diffusional boundary layer, behaves generally differently in blood and water. Hence, studies of the CO2 boundary layer should be preferably conducted with blood. In contrast, water tests can be considered suitable for reliable determination of the total CO2 removal performance of oxygenators