RAFT Polymerization of a Biorenewable/Sustainable Monomer via a Green Process

A biorenewable polymer is synthesized via a green process using the RAFT principle for the first time in supercritical CO2 at 300 bar and 80 °C. α-Methylene-γ-butyrolactone polymers of various chain lengths and molecular weights are obtained. The molecular weights vary from 10 000 up to 20 000 with low polydispersity indexes (PDI <1.5). Furthermore, the monomer conversion in supercritical CO2 is substantially higher, respectively 85% for ScCO2 compared to ≈65% for polymerizations conducted in dimethyl formamide (DMF) solvent. Chain extensions are carried out to confirm the livingness of the formed polymers in ScCO2. This opens up future possibilities of the formation of different polymer architectures in ScCO2. The polymers synthesized in ScCO2 have glass transition temperature (Tg) values ranging from 155 up to 190 °C. However, the presence of residual monomer encapsulated inside the formed polymer matrix affects the glass transition of the polymer that is lowered by increasing monomer concentrations. Hence, additional research is required to eliminate the remaining monomer concentration in the polymer matrix in order to arrive at the optimal Tg

Modelling the Cost-Effectiveness of Implementing a Dietary Intervention in Renal Transplant Recipients

Background: The Dietary Approach to Stop Hypertension (DASH) and potassium supplementation have been shown to reduce the risk of death with a functioning graft (DWFG) and renal graft failure in renal transplant recipients (RTR). Unfortunately, a key problem for patients is the adherence to these diets. The aim of this study is to evaluate the cost-effectiveness and budget impact of higher adherence to either the DASH or potassium supplementation. Methods: A Markov model was used to simulate the life course of 1000 RTR in the Netherlands. A societal perspective with a lifetime time horizon was used. The potential effect of improvement of dietary adherence was modelled in different scenarios. The primary outcomes are the incremental cost-effectiveness ratio (ICER) and the budget impact. Results: In the base case, improved adherence to the DASH diet saved 27,934,786 and gained 1880 quality-adjusted life years (QALYs). Improved adherence to potassium supplementation saved euro1,217,803 and gained 2901 QALYs. Both resulted in dominant ICERs. The budget impact over a five-year period for the entire Dutch RTR population was euro8,144,693. Conclusion: Improving dietary adherence in RTR is likely to be cost-saving and highly likely to be cost-effective compared to the current standard of care in the Netherlands

Ventricular synchrony is not significantly determined by absolute myocardial perfusion in patients with chronic heart failure:A N-13-ammonia PET study

Background It is thought that heart failure (HF) patients may benefit from the evaluation of mechanical (dys)synchrony, and an independent inverse relationship between myocardial perfusion and ventricular synchrony has been suggested. We explore the relationship between quantitative myocardial perfusion and synchrony parameters when accounting for the presence and extent of fixed perfusion defects in patients with chronic HF. Methods We studied 98 patients with chronic HF who underwent rest and stress Nitrogen-13 ammonia PET. Multivariate analyses of covariance were performed to determine relevant predictors of synchrony (measured as bandwidth, standard deviation, and entropy). Results In our population, there were 43 (44%) women and 55 men with a mean age of 71 +/- 9.6 years. The SRS was the strongest independent predictor of mechanical synchrony variables (p <.01), among other considered predictors including: age, sex, body mass index, smoking, diabetes mellitus, dyslipidemia, hypertension, rest myocardial blood flow (MBF), and myocardial perfusion reserve (MPR). Results were similar when considering stress MBF instead of MPR. Conclusions The existence and extent of fixed perfusion defects, but not the quantitative PET myocardial perfusion parameters (sMBF and MPR), constitute a significant independent predictor of ventricular mechanical synchrony in patients with chronic HF

Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty

Corneal thickness (pachymetry) maps can be used to monitor restoration of corneal endothelial function, for example after Descemet's membrane endothelial keratoplasty (DMEK). Automated delineation of the corneal interfaces in anterior segment optical coherence tomography (AS-OCT) can be challenging for corneas that are irregularly shaped due to pathology, or as a consequence of surgery, leading to incorrect thickness measurements. In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans. Three different deep learning strategies were developed based on 960 B-scans from 50 patients. On an independent test set of 320 B-scans, corneal thickness could be measured with an error of 13.98 to 15.50 micrometer for the central 9 mm range, which is less than 3% of the average corneal thickness. The accurate thickness measurements were used to construct detailed pachymetry maps. Moreover, follow-up scans could be registered based on anatomical landmarks to obtain differential pachymetry maps. These maps may enable a more comprehensive understanding of the restoration of the endothelial function after DMEK, where thickness often varies throughout different regions of the cornea, and subsequently contribute to a standardized postoperative regime.Comment: Fixed typo in abstract: The development set consists of 960 B-scans from 50 patients (instead of 68). The B-scans from the other 18 patients were used for testing onl

Direct Classification of Type 2 Diabetes From Retinal Fundus Images in a Population-based Sample From The Maastricht Study

Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [$\pm$0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [$\pm$0.003]), using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images.Comment: to be published in the proceeding of SPIE - Medical Imaging 2020, 6 pages, 1 figur