Hydrogel-Based Treatment of House Dust Mite-Induced Atopic Dermatitis through Triple Cleaning of Mites, Bacteria, and ROS-Related Inflammation.

Atopic dermatitis (AD) is a chronic and recurrent inflammatory disease caused by abnormalities in skin immunoregulation. House dust mite can directly damage the skin barrier and thus sensitize the skin, which is one of the main allergens inducing AD in humans and widely exists in daily life. Meanwhile, the accompanying bacterial infections and exposure to additional allergens exacerbate the condition by generating excessive reactive oxygen species (ROS). Herein, we have developed the CPDP hydrogel with injectable and self-healing ability to combat pathogenic microorganisms and inflammatory environments for AD therapy. In vitro experiments have affirmed the efficacy of the CPDP hydrogel in combating mites, killing bacteria, and scavenging ROS. In a mouse model closely mimicking HDM-induced AD, the CPDP hydrogel has shown superior therapeutic effects, including reducing epidermal thickness and mast cell count, increasing collagen deposition, as well as down-regulating pro-inflammatory factors.

Fenton-like Reaction Inspired "·OH Catalyzed" Osteogenic Process for the Treatment of Osteoporosis.

Inspired by the Fenton-like reaction, this work combines copper peroxide (CP) nanoparticles with black phosphorus (BP) nanosheets to form a hydroxyl radical (·OH)-centered "catalytic" osteogenic system. CP-produced ·OH interacts with BP to rapidly produce a large amount of phosphate ions, thus accelerating self-mineralization and promoting bone formation. In turn, BP not only exerts anti-inflammatory effects, thereby providing a favorable microenvironment for bone formation, but also offsets the potential toxicity of CP induced by reactive oxygen species (ROS). Together with copper ions (Cu2+), phosphate ions are also released as a byproduct of this process, which can contribute to the comprehensive promotion of osteogenesis.

Inner-Wall Coated Nanopipette Microextraction for Quantitative Analysis of Per- and Polyfluoroalkyl Substances in Single Cells Using Mass Spectrometry.

Per- and polyfluoroalkyl substances (PFASs) are a series of organic pollutants with potential cytotoxicity and biotoxicity. Accurate and sensitive detection of trace PFASs in single cells can provide insights into investigating their cytotoxicity, carcinogenicity, and mutagenicity. Here we report the development of an inner-wall coated nanopipette microextraction coupled with induced nanoelectrospray ionization mass spectrometry (InESI-MS) method and its application for rapid, sensitive, and accurate analysis of trace PFASs in single cells. A specially designed inner-wall coated nanopipette was prepared for sampling of the cytoplasm from a single cell, and the trace PFASs in the cytoplasm were selectively enriched into the coating via reversed-phase adsorption, ion bonding adsorption, and π-π interaction mechanisms. After the extraction, the cytoplasm was removed, and the enriched PFASs were then desorbed into some organic solvent, applying an alternating current (AC) voltage to the inner-wall coated nanopipette for InESI-MS analysis. The inner-wall coated nanopipette showed an exhaustive extraction to the trace PFASs in one single cell, and thus, the mass of each target analyte in the cytoplasm can be calculated via an internal standard calibration curve method, avoiding the measurement of ultrasmall volume cytoplasm for one single cell. By using the inner-wall coated nanopipette microextraction coupled with InESI-MS method, trace PFASs accumulated in the LO2 cells with pollutant exposure were successfully detected, and the accumulative behaviors and heterogeneities of PFASs in single cells were explored.

Nanospray Laser-Induced Plasma Ionization Mass Spectrometry for Rapid and Sensitive Analysis of Polycyclic Aromatic Hydrocarbons and Halogenated Derivatives.

Polycyclic aromatic hydrocarbons (PAHs) and halogenated derivatives are a series of environmental pollutants with potential toxicity and health risks on biosystems and the ecosystem. Rapid and sensitive analysis of trace PAHs and halogenated PAHs in complex environmental samples is a challenging topic for analytical science. Here we report the development of a nanospray laser-induced plasma ionization MS method for rapid and sensitive analysis of trace PAHs and halogenated PAHs under ambient and open-air conditions. A nanospray tip was applied for loading samples and placed pointing to the MS inlet, being a nanospray emitter with the application of a high voltage. A beam of laser was focused to induce energetic plasma between the nanospray emitter and the MS inlet for ionization of PAHs and halogenated PAHs for mass spectrometric analysis. Meanwhile, an inner-wall naphthyl-coated nanospray emitter was developed and applied as a solid-phase microextraction (SPME) probe for highly selective enrichment of trace PAHs and halogenated PAHs in complex environmental samples, and some organic solvent was applied to desorb the analytes for nanospray laser-induced plasma ionization MS analysis. Satisfactory linearity for each target PAH and halogenated PAH was obtained, with correlation coefficient values (r) no less than 0.9917. The method showed extremely high sensitivity for analysis of trace PAHs and halogenated PAHs in water, with limits of detection (LODs) and quantification (LOQs) of 0.0001-0.02 and 0.0003-0.08 µg/L, respectively. By using the inner-wall naphthyl-coated nanospray laser-induced plasma ionization MS method, sensitive detection of trace PAHs and halogenated PAHs in real sewage and wastewater samples was successfully achieved.

Interval-valued Pythagorean fuzzy multi-criteria decision-making method based on the set pair analysis theory and Choquet integral.

This paper proposes a novel fuzzy multi-criteria decision-making method based on an improved score function of connection numbers and Choquet integral under interval-valued Pythagorean fuzzy environment. To do so, we first introduce a method to convert interval-valued Pythagorean fuzzy numbers into connection numbers based on the set pair analysis theory. Then an improved score function of connection numbers is proposed to make the ranking order of connection numbers more in line with reality in multi-criteria decision-making process. In addition, some properties of the proposed score function of connection numbers and some examples have been given to illustrate the advantages of conversion method proposed in the paper. Then, considering interactions among different criteria, we propose a fuzzy multi-criteria decision-making approach based on set pair analysis and Choquet integral under interval-valued Pythagorean fuzzy environment. Finally, a case of online learning satisfaction survey and a brief comparative analysis with other existing approaches are studied to show that the proposed method is simple,convenient and easy to implement. Comparing with previous studies, the method in this paper, from a new perspective, effectively deals with multi-criteria decision-making problems that the alternatives cannot be reasonably ranked in the decision-making process under interval-valued Pythagorean fuzzy environment.

The association between vitamin D levels in the second trimester of pregnancy and gestational diabetes mellitus.

BACKGROUND AND AIMS: 25-hydroxyvitamin D (25(OH)D) affects glucose metabolism by increasing insulin secretion and insulin receptor expression. However, whether 25(OH)D deficiency will increase the risk of gestational diabetes mellitus (GDM) has not been clearly reported. The purpose of this study is to assess the relationship between vitamin D levels in the second trimester of pregnancy and the risk of GDM. METHODS: According to the inclusion and exclusion criteria, 247 pregnant women came to the fourth hospital of Shijiazhuang (The affiliated obstetrics and gynecology hospital of Hebei Medical University) for obstetrics were investigated during the period of January 1, 2019 to December 31, 2020. The levels of 25(OH)D in the second trimester (16-20 weeks) and oral 75 g glucose tolerance test (OGTT) at 24-28 weeks of pregnancy were reviewed. The sociodemographic data were collected from questionnaire. Multivariate logistic regression was used to analyze the relationship between vitamin D levels and GDM. RESULTS: The incidence of GDM in the observation group (25(OH)D ≤ 26 ng/ml) was higher than that in the control group (25(OH)D > 26 ng/ml) (p = 0.039). Compared with control group, the observation group had significantly higher level of fasting plasma glucose (FPG) (4.7 [4.5-5.0] mmol/L vs. 4.6 [4.4-4.8] mmol/L, p = 0.012). In the whole study, the level of 25(OH)D was negatively correlated with FPG (r = - 0.164，p = 0.010). After adjusting for age, pre-pregnancy BMI, parity and adverse pregnancy history, compared with the observation group (25 (OH) D ≤ 26 ng/ml), the risk of developing GDM decreased by 50.9% in control group (25(OH)D > 26 ng/ml) (odds ratio [OR] = 0.491, 95% confidence interval [CI] = 0.243-0.989, p = 0.047). CONCLUSION: Adequate vitamin D levels during the second trimester of pregnancy may reduce the risk of GDM.

Online education satisfaction assessment based on cloud model and fuzzy TOPSIS.

During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Secondly, the cloud model is used to quantitatively describe the natural language and uncertainty in a large amount of assessment information. The cloud model generator is used for sub-indicators and achieves an effective and flexible conversion between linguistic information and quantitative values. The cloud model of indicators are presented by integrating the corresponding sub-indicators. The weights of indicators are determined by the entropy method based on the cloud model and possibility degree matrix, which eliminates the judgment of decision-makers and has great power for handling practical problems with unknown weight information. Finally, a fuzzy TOPSIS method based on the cloud model is proposed to rank the satisfaction of online education of different colleges. The proposed method is compared with other existing methods to shown its merits. The experimental result is consistent with the proportion of students who accept online education in the post-epidemic era. According to the second questionnaire, as the qualitative evaluation of the cloud model of indicators increases, the qualitative evaluation of satisfaction of different types of colleges will also increase. It indicates that the method proposed in this paper is practical.