Nonequilibrium configurations of swelling polymer brush layers induced by spreading drops of weakly volatile oil.

Polymer brush layers are responsive materials that swell in contact with good solvents and their vapors. We deposit drops of an almost completely wetting volatile oil onto an oleophilic polymer brush layer and follow the response of the system upon simultaneous exposure to both liquid and vapor. Interferometric imaging shows that a halo of partly swollen polymer brush layer forms ahead of the moving contact line. The swelling dynamics of this halo is controlled by a subtle balance of direct imbibition from the drop into the brush layer and vapor phase transport and can lead to very long-lived transient swelling profiles as well as nonequilibrium configurations involving thickness gradients in a stationary state. A gradient dynamics model based on a free energy functional with three coupled fields is developed and numerically solved. It describes experimental observations and reveals how local evaporation and condensation conspire to stabilize the inhomogeneous nonequilibrium stationary swelling profiles. A quantitative comparison of experiments and calculations provides access to the solvent diffusion coefficient within the brush layer. Overall, the results highlight the-presumably generally applicable-crucial role of vapor phase transport in dynamic wetting phenomena involving volatile liquids on swelling functional surfaces.

Smartphone-based colorimetric detection systems for glucose monitoring in the diagnosis and management of diabetes.

Diabetes is a group of metabolic conditions resulting in high blood sugar levels over prolonged periods that affects hundreds of millions of patients worldwide. Measuring glucose concentration enables patient-specific insulin therapy, and is essential to reduce the severity of the disease, potential complications, and related mortalities. Recent advances and developments in smartphone-based colorimetric glucose detection systems are discussed in this review. The importance of glucose monitoring, data collection, transfer, and analysis, via non-invasive/invasive methods is highlighted. The review also presents various approaches using 3D-printed materials, screen-printed electrodes, polymer templates, designs allowing multiple glucose analysis, bioanalytes and/or nanostructures for glucose detection. The positive effects of advances in improving the performance of smartphone-based platforms are introduced along with future directions and trends in the application of emerging technologies in smartphone-based diagnostics.

Poly(2-Hydroxyethyl Methacrylate) Hydrogel-Based Microneedles for Metformin Release.

The release of metformin, a drug used in the treatment of cancer and diabetes, from poly(2-hydroxyethyl methacrylate), pHEMA, hydrogel-based microneedle patches is demonstrated in vitro. Tuning the composition of the pHEMA hydrogels enables preparation of robust microneedle patches with mechanical properties such that they would penetrate skin (insertion force of a single microneedle to be ≈40 N). Swelling experiments conducted at 20, 35, and 60 °C show temperature-dependent degrees of swelling and diffusion kinetics. Drug release from the pHEMA hydrogel-based microneedles is fitted to various models (e.g., zero order, first order, second order). Such pHEMA microneedles have potential application for transdermal delivery of metformin for the treatment of aging, cancer, diabetes, etc.

Non-enzymatic colorimetric glucose detection based on Au/Ag nanoparticles using smartphone and machine learning.

Conventional enzyme-based glucose quantification approaches are not feasible due to their high cost, specific working temperatures, short shelf life, and poor stability. Therefore, a portable platform, which offers rapid response, cost-efficiency, and high sensitivity, is indispensable for the healthcare of diabetes. In this study, we proposed a portable platform incorporating gold (Au) and silver (Ag) nanoparticles (NPs) with a smartphone application based on machine learning for non-enzymatic glucose quantification. The color change obtained from the reaction of small and large Au/Ag NPs with glucose was captured using a smartphone camera to create a dataset for the training of machine-learning classifiers. Our custom-designed user-friendly smartphone application called "GlucoQuantifier" uses a cloud system to communicate with a remote server running a machine-learning classifier. Among the tested classifiers, linear discriminant analysis exhibits the best classification performance (93.63%) with small Au/Ag NPs and it demonstrates that incorporating Au/Ag NPs with machine learning under a smartphone application can be used for non-enzymatic glucose quantification.

Ordering of Air-Oxidized Decanethiols on Au(111).

Self-assembled monolayers (SAMs) of alkanethiols on gold are a commonly used platform for nanotechnology owing to their ease of preparation and high surface coverage. Unfortunately, the gold-sulfur bond is oxidized at ambient conditions which alters the stability and structure of the monolayer. We show using scanning tunneling microscopy and X-ray photoelectron spectroscopy that decanethiolate molecules oxidize into decanesulfonates that organize into a hitherto unknown striped phase. Air-exposed SAMs oxidize, as can be determined by a shift of the S 2p peak and the appearance of O 1s photoelectrons as part of the decanethiol monolayer transforms into a lamellae-like decanesulfonate structure when exposed to air. The herringbone structure of the Au(111) surface is preserved, indicating that the interaction between the molecules and the surface is rather weak as these findings are substantiated by density functional theory calculations.

Adenosine deaminase, nitric oxide, superoxide dismutase, and xanthine oxidase in patients with major depression: impact of antidepressant treatment.

BACKGROUND: There has been much evidence in recent years that free oxygen radicals and nitric oxide (NO) may play an important role in the pathophysiology of neuropsychiatric disorders. In this study, we aimed to investigate whether NO, xanthine oxidase (XO), superoxide dismutase (SOD), and adenosine deaminase (ADA) levels are associated with major depression (MD) and to evaluate the impact of antidepressant treatments on NO, SOD, ADA and XO levels in MD. METHODS: Thirty-six patients who were diagnosed as MD according to DSM-IV criteria and 20 healthy controls were included. The serum levels of NO, XO, SOD, and ADA were measured by spectrophotometric methods both in patients and controls. Patients were treated with antidepressant drugs for 8 weeks. All patients were assessed by Hamilton Depression Rating Scale (HDRS) both before and after antidepressant treatment. RESULTS: ADA and XO levels of the patients were significantly higher than the controls. SOD level of the patients was significantly lower than the controls. Although NO levels of the patients were higher than the controls, the difference was not statistically significant. There was no correlation between HDRS and the parameters studied (SOD, ADA, XO, and NO) of the patients. After 8 weeks of antidepressant treatment, ADA and SOD activities were increased, whereas NO and, XO levels decreased significantly. CONCLUSIONS: ADA, XO, and SOD activity may have a pathophysiological role in MD and may predict prognosis of MD. Activity of these enzymes may be used to monitor effects of the antidepressant treatment.