Mixed Self-Assembly of Polyethylene Glycol and Aptamer on Polydopamine Surface for Highly Sensitive and Low-Fouling Detection of Adenosine Triphosphate in Complex Media

Detection of disease biomarkers within complex biological media is a substantial outstanding challenge because of severe biofouling and nonspecific adsorptions. Herein, a reliable strategy for sensitive and low-fouling detection of a biomarker, adenosine triphosphate (ATP) in biological samples was developed through the formation of a mixed self-assembled sensing interface, which was constructed by simultaneously self-assembling polyethylene glycol (PEG) and ATP aptamer onto the self-polymerized polydopamine-modified electrode surface. The developed aptasensor exhibited high selectivity and sensitivity toward the detection of ATP, and the linear range was 0.1–1000 pM, with a detection limit down to 0.1 pM. Moreover, owing to the presence of PEG within the sensing interface, the aptasensor was capable of sensing ATP in complex biological media such as human plasma with significantly reduced nonspecific adsorption effect. Assaying ATP in real biological samples including breast cancer cell lysates further proved the feasibility of this biosensor for practical application

Three-Dimensional Graphene Networks as a New Substrate for Immobilization of Laccase and Dopamine and Its Application in Glucose/O₂ Biofuel Cell

We report here three-dimensional graphene networks (3D-GNs) as a novel substrate for the immobilization of laccase (Lac) and dopamine (DA) and its application in glucose/O₂ biofuel cell. 3D-GNs were synthesized with an Ni²⁺-exchange/KOH activation combination method using a 732-type sulfonic acid ion-exchange resin as the carbon precursor. The 3D-GNs exhibited an interconnected network structure and a high specific surface area. DA was noncovalently functionalized on the surface of 3D-GNs with 3,4,9,10-perylene tetracarboxylic acid (PTCA) as a bridge and used as a novel immobilized mediating system for Lac-based bioelectrocatalytic reduction of oxygen. The 3D-GNs-PTCA-DA nanocomposite modified glassy carbon electrode (GCE) showed stable and well-defined redox current peaks for the catechol/<i>o</i>-quinone redox couple. Due to the mediated electron transfer by the 3D-GNs-PTCA-DA nanocomposite, the Nafion/Lac/3D-GNs-PTCA-DA/GCE exhibited high catalytic activity for oxygen reduction. The 3D-GNs are proven to be a better substrate for Lac and its mediator immobilization than 2D graphene nanosheets (2D-GNs) due to the interconnected network structure and high specific surface area of 3D-GNs. A glucose/O₂ fuel cell using Nafion/Lac/3D-GNs-PTCA-DA/GCE as the cathode and Nafion/glucose oxidase/ferrocence/3D-GNs/GCE as the anode can output a maximum power density of 112 μW cm^–2 and a short-circuit current density of 0.96 mA cm^–2. This work may be helpful for exploiting the popular 3D-GNs as an efficient electrode material for many other biotechnology applications

Table_1_Effects of common lifestyle factors on obstructive sleep apnea: precautions in daily life based on causal inferences.pdf

BackgroundThis study aimed to evaluate the causal impact of common modifiable lifestyles on obstructive sleep apnea (OSA), which is beneficial for recommendations to prevent and manage OSA.MethodPublished genome-wide association study (GWAS) summary statistics were used to perform two-sample Mendelian randomization (MR). Variants associated with each exposure of smoking, drinking, and leisure sedentary behaviors at the genetic level were used as instrumental variables (IVs). Then, inverse-variance weighting (IVW) was considered the primary result for causality. Moreover, several complimented approaches were also included to verify the observed associations. MR-PRESSO and MR-Egger intercept were applied to test the horizontal pleiotropy. To assess heterogeneity, Cochran's Q test by IVW and MR-Egger were applied.ResultsRegular smoking history increased OSA risk in all applied approaches [OR (95% CI)IVW = 1.28 (1.12, 1.45), p = 1.853 × 10−4], while the causality of lifetime smoking index [OR (95% CI)IVW = 1.39 (1.00, 1.91), p = 0.048], alcohol intake frequency [outliers removed OR (95% CI)IVW = 1.26 (1.08, 1.45), p = 0.002], and coffee intake behavior [OR (95% CI)IVW = 1.66 (1.03, 2.68), p = 0.039] on OSA risk were not always consistent in other approaches. In addition, no robust causal associations were observed for the effect of sedentary leisure behaviors on OSA risk. In sensitivity analysis, we observed no sign of horizontal pleiotropy or heterogeneity.ConclusionEver regularly smoking has a robust causal role in increasing OSA risk, which should be discouraged as precautions from developing OSA.</p

Long-Term Trends in Visibility and at Chengdu, China

<div><p>Long-term (1973 to 2010) trends in visibility at Chengdu, China were investigated using meteorological data from the U.S. National Climatic Data Center. The visual range exhibited a declining trend before 1982, a slight increase between 1983 and 1995, a sharp decrease between 1996 and 2005, and some improvements after 2006. The trends in visibility were generally consistent with the economic development and implementation of pollution controls in China. Intensive PM_2.5 measurements were conducted from 2009 to 2010 to determine the causes of visibility degradation. An analysis based on a modification of the IMPROVE approach indicated that PM_2.5 ammonium bisulfate contributed 27.7% to the light extinction coefficient (<i>b_ext</i>); this was followed by organic mass (21.7%), moisture (20.6%), and ammonium nitrate (16.3%). Contributions from elemental carbon (9.4%) and soil dust (4.3%) were relatively minor. Anthropogenic aerosol components (sulfate, nitrate, and elemental carbon) and moisture at the surface also were important determinants of the aerosol optical depth (AOD) at 550 nm, and the spatial distributions of both <i>b_ext</i> and AOD were strongly affected by regional topography. A Positive Matrix Factorization receptor model suggested that coal combustion was the largest contributor to PM_2.5 mass (42.3%) and the dry-air light-scattering coefficient (47.7%); this was followed by vehicular emissions (23.4% and 20.5%, respectively), industrial emissions (14.9% and 18.8%), biomass burning (12.8% and 11.9%), and fugitive dust (6.6% and 1.1%). Our observations provide a scientific basis for improving visibility in this area.</p></div

Annual variations of ridit values during 1973–2010 in Chengdu.

<p>Ridit values >0.5 mean that the visual range for the year was better than the reference distribution established from the 1973–2010 data; the opposite is true for value <0.5. Solid lines are linear fits of the ridit trends.</p

Coefficients of the regression model for visual range (VR) and air pollution index (API) during Period-1 (1973–1982), Period-2 (1983–1995), Period-3 (1996–2005), and Period-4 (2006–2010) in Chengdu.

a<p>Standard deviation.</p>b<p>Sample number of the monthly average values of each VR and API.</p>c<p>Correlation coefficient.</p>d<p>Annual rate of change: R = 12<i>β</i> (km yr⁻¹ for VR for API yr⁻¹).</p>e<p>Period-3 for the API was from 2000–2005.</p

Changes in light extinction (<i>b_ext</i>) budgets for PM_2.5 components and moisture for the Best 2.5% and the Worst 2.5% visual range observations.

a<p>Group composed of the 2.5% of the days least impaired visual ranges,</p>b<p>Group of the 2.5% most impaired days.</p

Daily variations of the contributions of PM_2.5 chemical components and aerosol moisture to the light extinction coefficient (<i>b_ext</i>) for the intensive sampling period based on the revised IMPROVE equation.

<p>The aerosol moisture contributions were calculated from <i>b_ext</i> under ambient condition subtracts <i>b_ext</i> under dry condition.</p

Average chemical component concentrations and meteorological parameters for the best and worst visual ranges (VRs).

a<p>Units: PM_2.5 and chemical species, µg m⁻³; Relative humidity (RH), %; Wind speed (WS), m s⁻¹; Mixed layer depth (MLD), m.</p>b<p>daily average VR values for the 2.5% least impaired days.</p>c<p>daily average VR values for the 2.5% most impaired days.</p>d<p>S.D.: Standard deviation.</p>e<p>OM: Organic mass = 1.8×OC.</p>f<p>EC: Elemental carbon.</p

Average source contribution (in percent) for each PMF source factor to PM_2.5 mass concentration and dry particle light scattering coefficient (<i>b_sp,dry</i>).

<p>Average source contribution (in percent) for each PMF source factor to PM_2.5 mass concentration and dry particle light scattering coefficient (<i>b_sp,dry</i>).</p