Use of a fluorescence assay to monitor the kinetics of fusion between erythrocyte ghosts, as induced by sendai virus

1986 ◽  
Vol 6 (11) ◽  
pp. 953-960 ◽  
Author(s):  
Dick Hoekstra ◽  
Karin Klappe
Keyword(s):  
Rhodamine B ◽  
Experimental Approach ◽  
Sendai Virus ◽  
Fluorescence Assay ◽  
Fusion Process ◽  
Erythrocyte Membranes ◽  
Erythrocyte Ghosts ◽  
Wide Range ◽  
Kinetics Of ◽  
Insight Into

The kinetics of the fusion process between erythrocyte ghosts, as induced by Sendal virus, were readily revealed by a simple fluorescence procedure previously employed to characterize the fusion of viruses with biological membranes. The method relies on the relief of fluorescence selfquenching of the membrane-inserted probe octadecyl Rhodamine B chloride (R18) as occurs when labeled membranes fuse with unlabeled counterparts. The kinetics of R18 insertion into ghost membranes, the non-exchangeable properties of the fluorophore and the kinetics, and some characteristics of Sendai virus-induced fusion of ghosts, are described. We propose that the experimental approach may be particularly advantageous to obtain insight into the efficiency and mechanism of a wide range of fusogens, capable of inducing fusion of erythrocyte membranes.

Characterization of the fusogenic properties of Sendai virus: kinetics of fusion with erythrocyte membranes

Biochemistry ◽  
1985 ◽  
Vol 24 (18) ◽  
pp. 4739-4745 ◽  
Author(s):  
Dick Hoekstra ◽  
Karin Klappe ◽  
Tiny De Boer ◽  
Jan Wilschut
Keyword(s):  
Sendai Virus ◽  
Erythrocyte Membranes ◽  
Kinetics Of

Kinetics of Sendai virus envelope fusion with erythrocyte membranes and virus-induced hemolysis

Biochemistry ◽  
1979 ◽  
Vol 18 (23) ◽  
pp. 5088-5095 ◽  
Author(s):  
Douglas S. Lyles ◽  
Frank R. Landsberger
Keyword(s):  
Sendai Virus ◽  
Erythrocyte Membranes ◽  
Virus Envelope ◽  
Kinetics Of

Sentence-Based Experience Logging in New Hearing Aid Users

2020 ◽  
Vol 29 (3S) ◽  
pp. 631-637
Author(s):  
Katja Lund ◽  
Rodrigo Ordoñez ◽  
Jens Bo Nielsen ◽  
Dorte Hammershøi
Keyword(s):  
Hearing Aid ◽  
Patient Experiences ◽  
Online Tool ◽  
Rehabilitation Process ◽  
Hearing Rehabilitation ◽  
Clinical Observations ◽  
Hearing Aid Use ◽  
Wide Range ◽  
Insight Into ◽  
Shed Light

Purpose The aim of this study was to develop a tool to gain insight into the daily experiences of new hearing aid users and to shed light on aspects of aided performance that may not be unveiled through standard questionnaires. Method The tool is developed based on clinical observations, patient experiences, expert involvement, and existing validated hearing rehabilitation questionnaires. Results An online tool for collecting data related to hearing aid use was developed. The tool is based on 453 prefabricated sentences representing experiences within 13 categories related to hearing aid use. Conclusions The tool has the potential to reflect a wide range of individual experiences with hearing aid use, including auditory and nonauditory aspects. These experiences may hold important knowledge for both the patient and the professional in the hearing rehabilitation process.

Scintigraphic studies in rats. Kinetics of insulin analogues covering wide range of receptor affinities

Diabetes ◽  
1991 ◽  
Vol 40 (5) ◽  
pp. 628-632 ◽  
Author(s):  
I. Jensen ◽  
V. Kruse ◽  
U. D. Larsen
Keyword(s):  
Insulin Analogues ◽  
Wide Range ◽  
Kinetics Of

scholarly journals Engineering the Interface between Inorganic Nanoparticles and Biological Systems through Ligand Design

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1001
Author(s):  
Rui Huang ◽  
David C. Luther ◽  
Xianzhi Zhang ◽  
Aarohi Gupta ◽  
Samantha A. Tufts ◽  
...  
Keyword(s):  
Organic Synthesis ◽  
Molecular Design ◽  
Biological Systems ◽  
Ligand Design ◽  
Inorganic Nanoparticles ◽  
Organic Ligands ◽  
Biological Applications ◽  
Wide Range ◽  
Insight Into

Nanoparticles (NPs) provide multipurpose platforms for a wide range of biological applications. These applications are enabled through molecular design of surface coverages, modulating NP interactions with biosystems. In this review, we highlight approaches to functionalize nanoparticles with ”small” organic ligands (Mw < 1000), providing insight into how organic synthesis can be used to engineer NPs for nanobiology and nanomedicine.

scholarly journals Gas Damping in Capacitive MEMS Transducers in the Free Molecular Flow Regime

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2566
Author(s):  
Boris A. Boom ◽  
Alessandro Bertolini ◽  
Eric Hennes ◽  
Johannes F. J. van den Brand
Keyword(s):  
Flow Regime ◽  
Gas Diffusion ◽  
Diffusion Time ◽  
Molecular Flow ◽  
Free Molecular Flow ◽  
Simple Analytical Model ◽  
Gas Damping ◽  
Wide Range ◽  
Capacitive Mems ◽  
Insight Into

We present a novel analysis of gas damping in capacitive MEMS transducers that is based on a simple analytical model, assisted by Monte-Carlo simulations performed in Molflow+ to obtain an estimate for the geometry dependent gas diffusion time. This combination provides results with minimal computational expense and through freely available software, as well as insight into how the gas damping depends on the transducer geometry in the molecular flow regime. The results can be used to predict damping for arbitrary gas mixtures. The analysis was verified by experimental results for both air and helium atmospheres and matches these data to within 15% over a wide range of pressures.

scholarly journals Rapid systemic surge of IL-33 after severe human trauma: a prospective observational study

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Olav Sundnes ◽  
William Ottestad ◽  
Camilla Schjalm ◽  
Peter Lundbäck ◽  
Lars la Cour Poulsen ◽  
...  
Keyword(s):  
High Resolution ◽  
Prospective Observational Study ◽  
Tissue Injury ◽  
Trauma Patients ◽  
Decoy Receptor ◽  
Interleukin 33 ◽  
Injured Patients ◽  
First Time ◽  
Kinetics Of ◽  
Insight Into

Abstract Background Alarmins are considered proximal mediators of the immune response after tissue injury. Understanding their biology could pave the way for development of new therapeutic targets and biomarkers in human disease, including multiple trauma. In this study we explored high-resolution concentration kinetics of the alarmin interleukin-33 (IL-33) early after human trauma. Methods Plasma samples were serially collected from 136 trauma patients immediately after hospital admission, 2, 4, 6, and 8 h thereafter, and every morning in the ICU. Levels of IL-33 and its decoy receptor sST2 were measured by immunoassays. Results We observed a rapid and transient surge of IL-33 in a subset of critically injured patients. These patients had more widespread tissue injuries and a greater degree of early coagulopathy. IL-33 half-life (t1/2) was 1.4 h (95% CI 1.2–1.6). sST2 displayed a distinctly different pattern with low initial levels but massive increase at later time points. Conclusions We describe for the first time early high-resolution IL-33 concentration kinetics in individual patients after trauma and correlate systemic IL-33 release to clinical data. These findings provide insight into a potentially important axis of danger signaling in humans.

scholarly journals Simulation of Folding Kinetics for Aligned RNAs

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 347
Author(s):  
Jiabin Huang ◽  
Björn Voß
Keyword(s):  
Evolutionary Conservation ◽  
Folding Kinetics ◽  
New Approach ◽  
Computational Approaches ◽  
Reasonable Time ◽  
Valuable Method ◽  
Kinetics Of ◽  
Insight Into ◽  
Folding Space ◽  
Standing Problem

Studying the folding kinetics of an RNA can provide insight into its function and is thus a valuable method for RNA analyses. Computational approaches to the simulation of folding kinetics suffer from the exponentially large folding space that needs to be evaluated. Here, we present a new approach that combines structure abstraction with evolutionary conservation to restrict the analysis to common parts of folding spaces of related RNAs. The resulting algorithm can recapitulate the folding kinetics known for single RNAs and is able to analyse even long RNAs in reasonable time. Our program RNAliHiKinetics is the first algorithm for the simulation of consensus folding kinetics and addresses a long-standing problem in a new and unique way.

Atomic-level insight into reasonable design of metal-based catalysts for hydrogen oxidation in alkaline electrolytes

2021 ◽  
Author(s):  
Lulu An ◽  
Xu Zhao ◽  
Tonghui Zhao ◽  
Deli Wang
Keyword(s):  
Fuel Cell ◽  
Anion Exchange ◽  
Hydrogen Oxidation ◽  
Anion Exchange Membrane ◽  
Atomic Level ◽  
Alkaline Electrolytes ◽  
Exchange Membrane ◽  
Kinetics Of ◽  
Insight Into ◽  
Hydrogen Utilization

Anion exchange membrane fuel cell (AEMFC) is becoming highly attractive for hydrogen utilization owing to the advantages of employing economic catalysts in alkaline electrolytes. Nevertheless, the kinetics of anodic hydrogen...

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

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