MicroRNA-182-5p relieves murine allergic rhinitis via TLR4/NF-κB pathway

Allergic rhinitis (AR) is one of the most common chronic diseases. This study examined whether microRNA (miR)-182-5p plays a role in AR by regulating toll-like receptor 4 (TLR4). First, data demonstrated that TLR4 was a target of miR-182-5p. Subsequently, AR mouse model was established to explore the role of miR-182-5p and TLR4 in AR in vivo. Initially, quantitative reverse transcription-PCR (qRT-PCR) analysis indicated that miR-182-5p was downregulated, while TLR4 expression was upregulated in AR mice. Then we found that miR-182-5p mimic reduced the frequency of sneezing and nose rubbing of the AR mice. In addition, miR-182-5p mimic significantly increased ovalbumin (OVA)-specific IgE and leukotriene C4 expression levels in nasal lavage fluid (NLF) and serum of AR mice. miR-182-5p mimic decreased the number of inflammatory cells in NLF of AR mice. It also reduced the levels of inflammatory factors in the serum of AR mice, such as interleukin (IL)-4, IL-5, IL-13, IL-17 and tumor necrosis factor (TNF)-α, while increasing the release of IFN-γ and IL-2. Finally, miR-182-5p mimic inhibited NF-κB signaling pathway activation in AR mice. However, all effects of miR-182-5p mimic on AR mice were reversed by TLR4-plasmid. In conclusion, miR-182-5p/TLR4 axis may represent a novel therapeutic target for AR

Multi-Objective Optimal Energy Management for the Integrated Electrical and Natural Gas Network with Combined Cooling, Heat and Power Plants

This paper proposes a multi-objective optimal energy management framework for the integrated electrical and natural gas network (IEGN) with combined cooling, heat, and power (CCHP) plants. Various energy conversion devices that are installed in the CCHP plant provide redundant generation options and energy pathways, which could be optimally chosen and shifted with given objectives, while meeting the multi-energy (ME) demands. However, this flexible energy dispatch manners may frequently change the energy distribution in the IEGN and challenge their mutual accommodation. In particular, the linepack reserve in the natural gas network, which supports the ramping capabilities of both the gas turbines and the flexible energy dispatch of the gas-dependent ME devices, is highly influenced. Without enough linepack reserve, not only will the flexible operation of the CCHP plants be hindered, but also the gas turbines will be prevented from balancing the supply and the demand in the electrical network, thus threatens the safety of the IEGN. Owing to this, the linepack reserve is modelled and jointly considered in the proposed energy management framework. The multi-objective optimization model that is proposed in this paper could simultaneously promote the economic benefits, safety, and efficiency of the IEGN, and Elitist Non-dominated Sorting Genetic algorithm II is used to solve it. At last, case studies demonstrate the effectiveness of the proposed method

Relative Humidity Sensor Based on Microfiber Loop Resonator

A novel relative humidity (RH) sensor based on a microfiber loop resonator (MLR) is proposed and experimentally demonstrated. As refractive index of the microfiber in the MLR is modified by environmental humidity, resonant wavelength of the MLR changes with RH level. By detecting this wavelength shift, RH measurement is realized with a linear response sensitivity of 1.8 pm/% RH. The obvious advantage of this technique over others is that no coating of humidity-sensitive material is required

Kinetic Modeling of Glycerol Hydrogenolysis: A Short Review

Glycerol hydrogenolysis represents one of the most promising technologies for future bio-refineries. In this context, kinetic modeling provides key quantitative assessment of the significance of various reactions for process development. However, as of present, there are only limited studies on detailed kinetic modeling of glycerol conversion to 1,2-propanediol, ethylene glycol and other alcoholic products. In this work, a comprehensive summary on kinetic modeling of glycerol hydrogenolysis has been conducted to reveal the possible mechanism involved in the activation of the C-H and C-O bond in glycerol molecules. In particular, power law and Langmuir–Hinshelwood model types have been critically discussed with mechanistic insights. The outcome of this review article will offer alternative views on the scale-up design of glycerol hydrogenolysis to glycols, as well as hydrogenolysis of various other bio-derived compounds to value-added chemicals

Effects of salusins on the protein expressions of p-JNK and p-p38 MAPK in HUVECs.

<p>After treatment with salusin-α or salusin-β, the total protein was extracted from the cultured HUVECs. The expressions of p-JNK and p-p38 MAPK were determined using western blot analysis. Sal-α: salusin-α; Sal-β: salusin-β. The results are presented as the mean ± SD of <i>n</i> = 3 independent experiments. ^$<i>P</i><0.05, ^$$<i>P</i><0.01 vs. control group; *<i>P</i><0.05 vs. DMSO group; ^#<i>P</i><0.05 vs. 10 nM Sal group.</p

Effects of salusins on the expressions of VCAM-1 and MCP-1 mRNA in HUVECs.

<p>After treatment with salusin-α or salusin-β, total RNA was extracted from the cultured HUVECs. The amounts of VCAM-1 and MCP-1 mRNA were determined using real-time quantitative PCR analysis. Sal-α: salusin-α; Sal-β: salusin-β. The results are presented as the mean ± SD of <i>n</i> = 6 independent experiments. ^$$<i>P</i><0.01 vs. control group; *<i>P</i><0.05, **<i>P</i><0.01 vs. DMSO group; ^#<i>P</i><0.05,^##<i>P</i><0.01 vs. 10 nM Sal group.</p

Effects of salusins on the protein expressions of VCAM-1 and MCP-1 in HUVECs.

<p>After treatment with salusin-α or salusin-β, the total protein was extracted from the cultured HUVECs. The expressions of VCAM-1 and MCP-1 were determined using western blot analysis. Sal-α: salusin-α; Sal-β: salusin-β. The results are presented as the mean ± SD of <i>n</i> = 3 independent experiments. ^$$<i>P</i><0.01 vs. control group; ^&<i>P</i><0.05 vs. LPS group; **<i>P</i><0.01 vs. DMSO group; ^#<i>P</i><0.05,^##<i>P</i><0.01 vs. 10 nM Sal group.</p

Effects of salusins on the levels of IL-6 and TNF-α in the supernatant of HUVECs.

<p>After treatment with salusin-α or salusin-β, the supernatant of cultured HUVECs were collected. The levels of IL-6 and TNF-α were determined using an enzyme-linked immunosorbent assay (ELISA). Sal-α: salusin-α; Sal-β: salusin-β. The results are presented as the mean ± SD of <i>n</i> = 5 independent experiments. ^$$<i>P</i><0.01 vs. control group; *<i>P</i><0.05, **<i>P</i><0.01 vs. DMSO group; ^#<i>P</i><0.05,^##<i>P</i><0.01 vs. 10 nM Sal group.</p

DataSheet1_Synthesis of amorphous trimetallic PdCuNiP nanoparticles for enhanced OER.PDF

Metal phosphides with multi-element components and amorphous structure represent a novel kind of electrocatalysts for promising activity and durability towards the oxygen evolution reaction (OER). In this work, a two-step strategy, including alloying and phosphating processes, is reported to synthesize trimetallic amorphous PdCuNiP phosphide nanoparticles for efficient OER under alkaline conditions. The synergistic effect between Pd, Cu, Ni, and P elements, as well as the amorphous structure of the obtained PdCuNiP phosphide nanoparticles, would boost the intrinsic catalytic activity of Pd nanoparticles towards a wide range of reactions. These obtained trimetallic amorphous PdCuNiP phosphide nanoparticles exhibit long-term stability, nearly a 20-fold increase in mass activity toward OER compared with the initial Pd nanoparticles, and 223 mV lower in overpotential at 10 mA cm−2. This work not only provides a reliable synthetic strategy for multi-metallic phosphide nanoparticles, but also expands the potential applications of this promising class of multi-metallic amorphous phosphides.</p

Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer

Abstract Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide, underscoring an urgent need for the development of early detection strategies and precise postoperative interventions. However, the identification of non-invasive biomarkers for early diagnosis and patient risk stratification remains underexplored. Here, we conduct a targeted metabolomics analysis of 702 plasma samples from multi-center participants to elucidate the GC metabolic reprogramming. Our machine learning analysis reveals a 10-metabolite GC diagnostic model, which is validated in an external test set with a sensitivity of 0.905, outperforming conventional methods leveraging cancer protein markers (sensitivity < 0.40). Additionally, our machine learning-derived prognostic model demonstrates superior performance to traditional models utilizing clinical parameters and effectively stratifies patients into different risk groups to guide precision interventions. Collectively, our findings reveal the metabolic landscape of GC and identify two distinct biomarker panels that enable early detection and prognosis prediction respectively, thus facilitating precision medicine in GC