Tuning of Smart Multifunctional Polymer Coatings Made by Zwitterionic Phosphorylcholines

In the last years, the generation of multifunctional coatings has been moved into the focus of interface modifications to expand the spectrum of material applications and to introduce new smart properties. Herein a promising multifunctional and universally usable coating with simultaneous antifouling, easy-to-clean, and anti-fog functionality is presented based on smart polymer films consisting of copolymers with 2-methacryloyloxyethyl phosphorylcholine (MPC), realizing the function of the film and photoreactive 4-benzophenyl methacrylate (BPO), which is responsible for stability and crosslinking. The easy-to-clean effect is demonstrated qualitatively and quantitatively by oil droplet detachment experiments. The antifouling behavior against different germs is investigated by cell adhesion experiments. Furthermore the anti-fog performance is shown by breathing on the surfaces. To study the influence of the different amounts of copolymerized BPO, the grafted films are characterized by atomic force microscopy (AFM), infrared spectroscopy (ATR-FTIR), as well as contact angle measurements. In situ spectroscopic ellipsometry is performed to investigate the swelling behavior of the thin films as a function of the time of UV-irradiation. It is found that a degree of swelling of 15 and a water contact angle of less than 12° are the key parameters necessary for the generation of multifunctional coatings. © 2019 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Chromosomal evolution of the PKD1 gene family in primates

Correction to Kirsch S, Pasantes J, Wolf A, Bogdanova N, Münch C, Pennekamp P, Krawczak M, Dworniczak B, Schempp W: Chromosomal evolution of the PKD1 gene family in primates. BMC Evolutionary Biology 2008, 8:263 (doi:10.1186/1471-2148-8-263

An omics-based machine learning approach to predict diabetes progression:a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA 1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA 1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA 1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA 1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.).</p

Use and subjective experience of the impact of motor-assisted movement exercisers in people with amyotrophic lateral sclerosis: a multicenter observational study

Motor-assisted movement exercisers (MME) are devices that assist with physical therapy in domestic settings for people living with ALS. This observational cross-sectional study assesses the subjective experience of the therapy and analyzes users' likelihood of recommending treatment with MME. The study was implemented in ten ALS centers between February 2019 and October 2020, and was coordinated by the research platform Ambulanzpartner. Participants assessed symptom severity, documented frequency of MME use and rated the subjective benefits of therapy on a numerical scale (NRS, 0 to 10 points, with 10 being the highest). The Net Promotor Score (NPS) determined the likelihood of a participant recommending MME. Data for 144 participants were analyzed. Weekly MME use ranged from 1 to 4 times for 41% of participants, 5 to 7 times for 42%, and over 7 times for 17%. Particularly positive results were recorded in the following domains: amplification of a sense of achievement (67%), diminution of the feeling of having rigid limbs (63%), diminution of the feeling of being immobile (61%), improvement of general wellbeing (55%) and reduction of muscle stiffness (52%). Participants with more pronounced self-reported muscle weakness were more likely to note a beneficial effect on the preservation and improvement of muscle strength during MME treatment (p < 0.05). Overall, the NPS for MME was high (+ 61). High-frequency MME-assisted treatment (defined as a minimum of five sessions a week) was administered in the majority of participants (59%) in addition to physical therapy. Most patients reported having achieved their individual therapeutic objectives, as evidenced by a high level of satisfaction with MME therapy. The results bolster the justification for extended MME treatment as part of a holistic approach to ALS care

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.)

Constrained thermoresponsive polymers - new insights into fundamentals and applications

In the last decades, numerous stimuli-responsive polymers have been developed and investigated regarding their switching properties. In particular, thermoresponsive polymers, which form a miscibility gap with the ambient solvent with a lower or upper critical demixing point depending on the temperature, have been intensively studied in solution. For the application of such polymers in novel sensors, drug delivery systems or as multifunctional coatings, they typically have to be transferred into specific arrangements, such as micelles, polymer films or grafted nanoparticles. However, it turns out that the thermodynamic concept for the phase transition of free polymer chains fails, when thermoresponsive polymers are assembled into such sterically confined architectures. Whereas many published studies focus on synthetic aspects as well as individual applications of thermoresponsive polymers, the underlying structure-property relationships governing the thermoresponse of sterically constrained assemblies, are still poorly understood. Furthermore, the clear majority of publications deals with polymers that exhibit a lower critical solution temperature (LCST) behavior, with PNIPAAM as their main representative. In contrast, for polymer arrangements with an upper critical solution temperature (UCST), there is only limited knowledge about preparation, application and precise physical understanding of the phase transition. This review article provides an overview about the current knowledge of thermoresponsive polymers with limited mobility focusing on UCST behavior and the possibilities for influencing their thermoresponsive switching characteristics. It comprises star polymers, micelles as well as polymer chains grafted to flat substrates and particulate inorganic surfaces. The elaboration of the physicochemical interplay between the architecture of the polymer assembly and the resulting thermoresponsive switching behavior will be in the foreground of this consideration

Cognitive Control of Eating Behavior and the Disinhibition Effect

Restrained eaters have been reported to overeat following a high caloric preload, a phenomenon refered to as the disinhibition effect. However this effect has not been found when subjects were classified by the restraint subscales of the Three-Factor Eating Questionnaire (TFEQ; Stunkard & Messick, 1985) or the Dutch Eating Behaviour Questionnaire (van Strien et al., 1986). The present study investigates the disinhibition effect in 133 normal-weight young women, using a two-factorial classification including the TFEQ-restraint and the TFEQ-disinhibition scale. The subjects were requested to consume ice-cream ad libitum during a taste test following a 200-ml milkshake preload or without preload. The results show that the behavioural disinhibition effect occurs only in subjects with simultanous high scores on both subscales. In addition, subjects with high disinhibition scores consumed more ice-cream than low disinhibition subjects irrespective of their degree of restraint. While subject with a more rigid control of eating behaviour did not show a difference in the amount of ice-cream consumed with or without preload, subjects with a more flexible control of eating behaviour reduced their intake following the preload condition. With regard to the Revised Restraint Scale (RRS Herman & Polivy, 1980) multiple regression results show that high RRS scores may be due to either higher TFEQ-restraint or higher TFEQ-disinhibition scores. The interpretation of the results favours the renaming of the TFEQ-disinhibition scale to "susceptibility to eating problems" because high scores on this scale indicate overeating in a variety of situations without requiring prior inhibition i.e. dietary restraint. It is supposed that high susceptibility to eating problems may be caused by rigid control of eating behaviour, whereas flexible control of eating behaviour may be a less problematic strategy of long-term weight control