Self-Assembly of Supramolecular Triblock Copolymer Complexes

Four different poly(tert-butoxystyrene)-b-polystyrene-b-poly(4-vinylpyridine) (PtBOS-b-PS-b-P4VP) linear triblock copolymers, with the P4VP weight fraction varying from 0.08 to 0.39, were synthesized via sequential anionic polymerization. The values of the unknown interaction parameters between styrene and tert-butoxystyrene and between tert-butoxystyrene and 4-vinylpyridine were determined from random copolymer blend miscibility studies and found to satisfy 0.031<χS,tBOS<0.034 and 0.39<χ4VP,tBOS<0.43, the latter being slightly larger than the known 0.30<χS,4VP≤0.35 value range. All triblock copolymers synthesized adopted a P4VP/PS core/shell cylindrical self-assembled morphology. From these four triblock copolymers supramolecular complexes were prepared by hydrogen bonding a stoichiometric amount of pentadecylphenol (PDP) to the P4VP blocks. Three of these complexes formed a triple lamellar ordered state with additional short length scale ordering inside the P4VP(PDP) layers. The self-assembled state of the supramolecular complex based on the triblock copolymer with the largest fraction of P4VP consisted of alternating layers of PtBOS and P4VP(PDP) layers with PS cylinders inside the latter layers. The difference in morphology between the triblock copolymers and the supramolecular complexes is due to two effects: (i) a change in effective composition and, (ii) a reduction in interfacial tension between the PS and P4VP containing domains. The small angle X-ray scattering patterns of the supramolecules systems are very temperature sensitive. A striking feature is the disappearance of the first order scattering peak of the triple lamellar state in certain temperature intervals, while the higher order peaks (including the third order) remain. This is argued to be due to the thermal sensitivity of the hydrogen bonding and thus directly related to the very nature of these systems.

Muscle wasting and survival following pre-operative chemoradiotherapy for locally advanced rectal carcinoma

Background & aims: Neoadjuvant chemoradiotherapy (NACRT) has increased local control in locally advanced rectal cancer. Reduced skeletal muscle mass (sarcopenia), or ongoing muscle wasting, is associated with decreased survival in cancer. This study aims to assess the change in body composition during NACRT and its impact on outcome using computed tomography (CT) imaging in locally advancedrectal cancer (LARC) patients. Methods: LARC patients treated with NACRT were selected from a prospectively maintained database and retrospectively analyzed. One-hundred twenty-two patients who received treatment between 2004 and 2012 with available diagnostic CT imaging obtained before and after NACRT were identified. Cross-sectional areas for skeletal muscle was determined, and subsequently normalized for patient height. Differences between skeletal muscle areas before and after NACRT were computed, and their influenceon overall and disease-free survival was assessed. Results: A wide distribution in change of body composition was observed. Loss of skeletal muscle mass during chemoradiotherapy was independently associated with disease-free survival (HR0.971; 95% CI:0.946e0.996; p¼0.025) and distant metastasis-free survival (HR0.942; 95% CI: 0.898e0.988; p¼0.013).No relation was observed with overall survival in the current cohort. Conclusions:Loss of skeletal muscle mass during NACRT in rectal cancer patients is an independent prognostic factor for disease-free survival and distant metastasis-free survival following curative intentresection

Distributional theory for the DIA method

The DIA method for the detection, identification and adaptation of model misspecifications combines estimation with testing. The aim of the present contribution is to introduce a unifying framework for the rigorous capture of this combination. By using a canonical model formulation and a partitioning of misclosure space, we show that the whole estimation–testing scheme can be captured in one single DIA estimator. We study the characteristics of this estimator and discuss some of its distributional properties. With the distribution of the DIA estimator provided, one can then study all the characteristics of the combined estimation and testing scheme, as well as analyse how they propagate into final outcomes. Examples are given, as well as a discussion on how the distributional properties compare with their usage in practice