Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes: Web-Based Survey Study

BackgroundTelemedicine has great potential for diabetes management. The COVID-19 pandemic has boosted the development of telemedicine. However, the factors influencing the behavioral intentions to use and use behaviors of telemedicine in patients with diabetes in China are not clear. ObjectiveWe aimed to understand the determinants of behavioral intention to use telemedicine based on an extended Unified Theory of Acceptance and Use of Technology model and to identify demographic factors associated with telemedicine use in patients with diabetes in China. MethodsPatients with diabetes who are aged ≥18 years were surveyed from February 1 to February 7, 2023. We distributed the survey link in 3 WeChat groups including a total of 988 patients with diabetes from the outpatient department or patients discharged from Changsha Central Hospital. Structural equation modeling was used to understand the determinants of behavioral intention. A multivariate logistic regression analysis was used to identify the demographic factors associated with telemedicine use. ResultsIn total, 514 questionnaires were collected. Of the respondents, 186 (36.2%) were diagnosed with COVID-19. The measurement model showed acceptable reliability, convergent validity, discriminant validity, and data fit indices. The model explained 63.8% of the variance in behavioral intention. Social influence, performance expectancy, and facilitating conditions positively influenced behavioral intention (β=.463, P.05). The overall use of telemedicine was 20.6% (104/514). After adjusting for the behavioral intention score, the multivariate regression analysis showed that age, education, and family income were associated with telemedicine use. Telemedicine use was higher in the 40 to 59 years and 18 to 39 years age groups than in the ≥60 years age group (odds ratio [OR] 4.35, 95% CI 1.84-10.29, P=.001; OR 9.20, 95% CI 3.40-24.88, P¥100,000 group: OR 4.63, 95% CI 1.41-15.27, P=.01). ConclusionsSocial influence, performance expectancy, and facilitating conditions positively affected the behavioral intention of patients with diabetes to use telemedicine. Young patients, highly educated patients, and patients with high family income use telemedicine more often. Promoting behavioral intention and paying special attention to the needs of older adult patients, patients with low income, and patients with low levels of education are needed to encourage telemedicine use

Controllable organic magnetoresistance in polyaniline coated poly(: P -phenylene-2,6-benzobisoxazole) short fibers

Herein, we first report a tunable organic magnetoresistance (OMAR) effect in polyaniline (PANI) coated acid treated poly(p-phenylene-2,6-benzobisoxazole) (t-PBO) short fibers. This unique OMAR is interpreted using the paramagnetic nature of PBO molecules combined with the localization length a0 calculated from the wave-function shrinkage model and forward interference model

Magnetoresistive conductive polymer-tungsten trioxide nanocomposites with ultrahigh sensitivity at low magnetic field

The effect of conductive polymer matrix including polyaniline (PANI) and polypyrrole (PPy) on the magnetoresistance (MR) behaviors in the variable range hopping (VRH) regime has been investigated in the disordered polymer nanocomposites containing tungsten trioxide (WO3) nanoparticles. These nanocomposites have demonstrated ultrahigh MR sensitivity at low magnetic field regime. The observed positive MR has been well explained by the wave-function shrinkage model. The conductive polymer matrix has shown different effects on the MR behaviors of the nanocomposites. The WO3/PANI nanocomposites have a lower localization length (a0) and density of states at the Fermi level (N(EF)), and higher average hopping distance (Rhop) and average hopping energy (W) compared with those of the WO3/PPy nanocomposites. © 2013 Elsevier Ltd. All rights reserved

Free energy calculations of glycosaminoglycan-protein interactions

Glycosaminoglycans (GAGs) are complex highly charged linear polysaccharides that have a variety of roles in biological processes. We report the first use of molecular dynamics (MD) free energy calculations using the MM/PBSA method to investigate the binding of GAGs to protein molecules, namely the platelet endothelial cell adhesion molecule 1 (PECAM-1) and annexin A2. Calculations of the free energy of the binding of heparin fragments of different sizes reveal the existence of a region of low GAG-binding affinity in domains 5-6 of PECAM-1 and a region of high affinity in domains 2-3, consistent with experimental data and ligand-protein docking studies. A conformational hinge movement between domains 2 and 3 was observed, which allows the binding of heparin fragments of increasing size (pentasaccharides to octasaccharides) with an increasingly higher binding affinity. Similar simulations of the binding of a heparin fragment to annexin A2 reveal the optimization of electrostatic and hydrogen bonding interactions with the protein and protein-bound calcium ions. In general, these free energy calculations reveal that the binding of heparin to protein surfaces is dominated by strong electrostatic interactions for longer fragments, with equally important contributions from van der Waals interactions and vibrational entropy changes, against a large unfavorable desolvation penalty due to the high charge density of these molecules

The Structure of Glycosaminoglycans and their Interactions with Proteins

Glycosaminoglycans (GAGs) are important complex carbohydrates that participate in many biological processes through the regulation of their various protein partners. Biochemical, structural biology and molecular modelling approaches have assisted in understanding the molecular basis of such interactions, creating an opportunity to capitalize on the large structural diversity of GAGs in the discovery of new drugs. The complexity of GAG–protein interactions is in part due to the conformational flexibility and underlying sulphation patterns of GAGs, the role of metal ions and the effect of pH on the affinity of binding. Current understanding of the structure of GAGs and their interactions with proteins is here reviewed: the basic structures and functions of GAGs and their proteoglycans, their clinical significance, the three-dimensional features of GAGs, their interactions with proteins and the molecular modelling of heparin binding sites and GAG–protein interactions. This review focuses on some key aspects of GAG structure–function relationships using classical examples that illustrate the specificity of GAG–protein interactions, such as growth factors, anti-thrombin, cytokines and cell adhesion molecules. New approaches to the development of GAG mimetics as possible new glycotherapeutics are also briefly covered