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© 2020 The Authors. Published by Elsevier B.V.Liposomes are an important tool and have gained much attention for their promise as an effective means of delivering small therapeutic compounds to targeted sites. In an effort to establish an effective method to produce liposomes from the lipid, dipalmitoyl-phosphatidylcholine or DPPC, we have found important aspects that must be taken into consideration. Here, we used probe-tip sonication to prepare liposomes on a batch scale. During this process we uncovered interesting steps in their preparation that altered the thermodynamic properties and phase transitions of the resulting liposome mixtures. Using differential scanning calorimetry to assess this we found that increasing the sonication time had the most dramatic effect on our sample, producing almost an entirely separate phase transition relative to the main phase transition. This result is consistent with reports from the current literature. We also highlight a smaller transition, which we attribute to traces of unincorporated lipid that seems to gradually disappear as the total lipid concentration decreases. Overall, sonication is an effective means of producing liposomes, but we cannot assert this method is optimal in producing them with precise physical properties. Here we highlight the physical effects at play during this process. © 2020 The Authors.Background and Aims Tricuspid regurgitation (TR) is a frequent valvular heart disease with relevant adverse impact on patients' prognosis. Adequate TR imaging and evaluation is challenging. In this study, we aimed to compare different imaging modalities (echocardiography and multi-slice computed tomography) for the assessment of tricuspid valve (TV) function and geometry. Methods We retrospectively investigated patients that presented to University Hospital Bonn, Germany, between September 2018 and March 2019, who underwent comprehensive echocardiography and multi-slice computed tomography (MSCT) to evaluate TR. Dizocilpine purchase MSCT was considered the reference approach for dimensional assessment of TV anatomy and echocardiography (transthoracic echocardiography + transesophageal echocardiography) for functional assessment of TV. We used Spearman's Rank order correlation, Bland-Altman analysis, and intra-class correlation to compare the different imaging modalities. Results Forty patients (Mean Age ± SD 77.5 ± 7.1 years; 35%Altman analysis, we found a relevant agreement between MSCT and 3D-TEE only for TV area (bias -1.95, 95% limits of agreement -3.6 to -0.1). Conclusion Only 3D-TEE allowed for sufficient simultaneous functional and dimensional assessment of TR in our cohort. © 2020 The Authors. Health Science Reports published by Wiley Periodicals LLC.There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods. © The Author(s) 2020.Digital medicine is an interdisciplinary field, drawing together stakeholders with expertize in engineering, manufacturing, clinical science, data science, biostatistics, regulatory science, ethics, patient advocacy, and healthcare policy, to name a few. Although this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We focus on the evaluation of BioMeTs as fit-for-purpose for use in clinical trials. However, our intent is for this framework to be instructional to all users of digital measurement tools, regardless of setting or intended use. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes (1) verification, (2) analytical validation, and (3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field. © The Author(s) 2020.Background This study aims to explore the correlations of body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and body composition with levels of asymmetric dimethylarginine (ADMA), endothelin 1(ET-1), N-terminal brain natriuretic pro-peptide (NT-proBNP) and calculated cardiovascular risks. Methods 102 women and 67 men with type 2 diabetes participated. Serum levels of NT-proBNP were measured by electro-hemi-luminescence while ELISA were used for ADMA and ET-1. Cardiovascular risks were calculated using the Framingham Risk Score (FRS), the UKPDS 2.0 and the ADVANCE risk engines. Statistical analysis was performed on an IBM SPSS 19.0. Results The BMI outperformed all other indices of obesity (WC, WHtR, WHR), as well as body composition parameters (body fat%, fat mass, fat free mass and total body water) in relation to the estimated risks for coronary heart disease and stroke, based on different calculators. The correlations of the obesity indices with the serum cardiovascular biomarkers were not significant except for BMI and fat mass versus ET-1, and for fat free mass and total body water versus ADMA.
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