Forecasting linear aliphatic copolyester degradation through modular block design

AbstractThe development of efficient methods to predict the degradation of renewable polymeric materials is continuously sought in the field of polymer science. Herein, we present a modular build-up approach to create polyester-based materials with forecasted degradation rates based on the hydrolysis of the constituent polymer blocks. This involved the strategic combination of critical factors affecting polyester hydrolysis, i.e. hydrophobicity and degree of crystallinity. The starting point of this method was a toolbox of polymers with different hydrophobicities and degrees of crystallinity, as well as an understanding of their inherent differences in hydrolysis rate. Knowledge of the hydrolysis of each polymer block module enabled the prediction of the overall degradation behavior of the constructed copolymers. Taking advantage of the primary factors that affect polymer degradation, block copolymers could be independently designed to incorporate soft or rigid and faster or slower degradation properties. This approach generated a shift for how molecular design can be used to predict the degradation behavior of intended materials for different applications

Development and validation of a new clinical decision support tool to optimize screening for retinopathy of prematurity

Background/Aims Prematurely born infants undergo costly, stressful eye examinations to uncover the small fraction with retinopathy of prematurity (ROP) that needs treatment to prevent blindness. The aim was to develop a prediction tool (DIGIROP-Screen) with 100% sensitivity and high specificity to safely reduce screening of those infants not needing treatment. DIGIROP-Screen was compared with four other ROP models based on longitudinal weights. Methods Data, including infants born at 24–30 weeks of gestational age (GA), for DIGIROP-Screen development (DevGroup, N=6991) originate from the Swedish National Registry for ROP. Three international cohorts comprised the external validation groups (ValGroups, N=1241). Multivariable logistic regressions, over postnatal ages (PNAs) 6–14 weeks, were validated. Predictors were birth characteristics, status and age at first diagnosed ROP and essential interactions. Results ROP treatment was required in 287 (4.1%)/6991 infants in DevGroup and 49 (3.9%)/1241 in ValGroups. To allow 100% sensitivity in DevGroup, specificity at birth was 53.1% and cumulatively 60.5% at PNA 8 weeks. Applying the same cut-offs in ValGroups, specificities were similar (46.3% and 53.5%). One infant with severe malformations in ValGroups was incorrectly classified as not needing screening. For all other infants, at PNA 6–14 weeks, sensitivity was 100%. In other published models, sensitivity ranged from 88.5% to 100% and specificity ranged from 9.6% to 45.2%. Conclusions DIGIROP-Screen, a clinical decision support tool using readily available birth and ROP screening data for infants born GA 24–30 weeks, in the European and North American populations tested can safely identify infants not needing ROP screening. DIGIROP-Screen had equal or higher sensitivity and specificity compared with other models. DIGIROP-Screen should be tested in any new cohort for validation and if not validated it can be modified using the same statistical approaches applied to a specific clinical setting