Effects of Dimethylaminoethanol and Compound Amino Acid on D-Galactose Induced Skin Aging Model of Rat

A lasting dream of human beings is to reverse or postpone aging. In this study, dimethylaminoethanol (DMAE) and compound amino acid (AA) in Mesotherapy were investigated for their potential antiaging effects on D-galactose induced aging skin. At 18 days after D-gal induction, each rat was treated with intradermal microinjection of saline, AA, 0.1% DMAE, 0.2% DMAE, 0.1% DMAE + AA, or 0.2% DMAE + AA, respectively. At 42 days after treatment, the skin wound was harvested and assayed. Measurement of epidermal and dermal thickness in 0.1% DMAE + AA and 0.2% DMAE + AA groups appeared significantly thicker than aging control rats. No differences were found in tissue water content among groups. Hydroxyproline in 0.1% DMAE + AA, 0.2% DMAE + AA, and sham control groups was much higher than all other groups. Collagen type I, type III, and MMP-1 expression was highly upregulated in both 0.1% DMAE + AA and 0.2% DMAE + AA groups compared with aging control. In contrast, TIMP-1 expression levels of various aging groups were significantly reduced when compared to sham control. Coinjection of DMAE and AA into target tissue has marked antiaging effects on D-galactose induced skin aging model of rat

A novel method for measurement of the angle of repose of granular seeds in discrete element methods

Discrete element numerical simulations can help researchers find potential problems in the design phase, shortening the development cycle and reducing costs. In the field of agricultural engineering, more and more researchers are using discrete element methods (DEM) to assist in designing and optimising equipment parameters. Model parameters calibration is a prerequisite for discrete element numerical calculations, and the angle of repose (AoR) is commonly used to calibrate the parameters. However, the measurement of AoR in DEM was not seriously considered in industrial or academic fields. In practice, AoR is measured manually, using 2D digital image processing or using a 3D scan. However, reliable and consistent measurements of AoR in DEM are rarely mentioned. This study suggests an accurate and consistent way to measure AoR in DEM using a novel method to read particle coordinate information directly from the data file; then, the AoR is calculated by linearly fitting the centre coordinates of the outermost particles. Influences of input variables on AoR acquisition are discussed through several examples using customised templates with known angles. Then a comparative study of the accuracy of the measurement of AoR in DEM and the reliability of the parameter calibration results by the manual measurement, 2D digital image processing, and algorithm proposed in this paper was conducted. In case studies with four seed materials, this method prevented the subjective selection of AoR, improved the identification accuracy, and increased the precision and accuracy of DEM calibration. In addition, the time consumption for obtaining AoR using the novel method for measurement is much less than that of 2D

Strain-based tunable optical microresonator with an in-fiber rectangular air bubble

We demonstrate a strain-based fully tunable, near-lossless, whispering gallery mode (WGM) resonator made of an in-fiber rectangular air bubble, which is fabricated by splicing two segments of standard single-mode fibers. Such a resonator, with a 39 μm order radius and 1 μm order wall thickness, contributes to a high quality factor exceeding 106. The tuning in resonant wavelength is achieved by applying tensile strain to the resonator, and the voltage-tuning rate of the WGM resonance peaks is about 31.96 pm/V (strain-tuning rate ∼14.12 pm∕με), and the corresponding tuning accuracy is better than 0.03 pm. Since the tensile strain applied on the resonator can reach 1000 με, the achievable total tunable bandwidth of ∼14.12 nm is more than two times that of its azimuthal free spectral range

Biomarkers associated with functional improvement after stroke rehabilitation: a systematic review and meta-analysis of randomized controlled trials

ObjectiveThis study aims to identify blood and cerebrospinal fluid biomarkers that are correlated to the functional improvement of stroke patients after rehabilitation therapy, and provide ideas for the treatment and evaluation of stroke patients.MethodsThe PubMed, Web of Science, and Embase databases were searched for articles published in the English language, from inception to December 8, 2022.ResultsA total of 9,810 independent records generated 50 high-quality randomized controlled trials on 119 biomarkers. Among these records, 37 articles were included for the meta-analysis (with a total of 2,567 stroke patients), and 101 peripheral blood and cerebrospinal fluid biomarkers were included for the qualitative analysis. The quantitative analysis results revealed a moderate quality evidence that stroke rehabilitation significantly increased the level of brain-derived neurotrophic factor (BDNF) in serum. Furthermore, the low-quality evidence revealed that stroke rehabilitation significantly increased the concentration of serum noradrenaline (NE), peripheral blood superoxide dismutase (SOD), peripheral blood albumin (ALB), peripheral blood hemoglobin (HB), and peripheral blood catalase (CAT), but significantly decreased the concentration of serum endothelin (ET) and glutamate. In addition, the changes in concentration of these biomarkers were associated with significant improvements in post-stroke function. The serum BNDF suggests that this can be used as a biomarker for non-invasive brain stimulation (NIBS) therapy, and to predict the improvement of stroke patients.ConclusionThe concentration of serum BNDF, NE, ET and glutamate, and peripheral blood SOD, ALB, HB and CAT may suggest the function improvement of stroke patients

Unveiling the additive-assisted oriented growth of perovskite crystallite for high performance light-emitting diodes.

Solution-processed metal halide perovskites have been recognized as one of the most promising semiconductors, with applications in light-emitting diodes (LEDs), solar cells and lasers. Various additives have been widely used in perovskite precursor solutions, aiming to improve the formed perovskite film quality through passivating defects and controlling the crystallinity. The additive's role of defect passivation has been intensively investigated, while a deep understanding of how additives influence the crystallization process of perovskites is lacking. Here, we reveal a general additive-assisted crystal formation pathway for FAPbI3 perovskite with vertical orientation, by tracking the chemical interaction in the precursor solution and crystallographic evolution during the film formation process. The resulting understanding motivates us to use a new additive with multi-functional groups, 2-(2-(2-Aminoethoxy)ethoxy)acetic acid, which can facilitate the orientated growth of perovskite and passivate defects, leading to perovskite layer with high crystallinity and low defect density and thereby record-high performance NIR perovskite LEDs (~800 nm emission peak, a peak external quantum efficiency of 22.2% with enhanced stability)

Tellurium Nanotubes and Chemical Analogues from Preparation to Applications: A Minor Review

Tellurium (Te), the most metallic semiconductor, has been widely explored in recent decades owing to its fantastic properties such as a tunable bandgap, high carrier mobility, high thermal conductivity, and in-plane anisotropy. Many references have witnessed the rapid development of synthesizing diverse Te geometries with controllable shapes, sizes, and structures in different strategies. In all types of Te nanostructures, Te with one-dimensional (1D) hollow internal structures, especially nanotubes (NTs), have attracted extensive attention and been utilized in various fields of applications. Motivated by the structure-determined nature of Te NTs, we prepared a minor review about the emerging synthesis and nanostructure control of Te NTs, and the recent progress of research into Te NTs was summarized. Finally, we highlighted the challenges and further development for future applications of Te NTs

Maneuvering Target Tracking Using Simultaneous Optimization and Feedback Learning Algorithm Based on Elman Neural Network

Tracking maneuvering targets is a challenging problem for sensors because of the unpredictability of the target’s motion. Unlike classical statistical modeling of target maneuvers, a simultaneous optimization and feedback learning algorithm for maneuvering target tracking based on the Elman neural network (ENN) is proposed in this paper. In the feedback strategy, a scale factor is learnt to adaptively tune the dynamic model’s error covariance matrix, and in the optimization strategy, a corrected component of the state vector is learnt to refine the final state estimation. These two strategies are integrated in an ENN-based unscented Kalman filter (UKF) model called ELM-UKF. This filter can be trained online by the filter residual, innovation and gain matrix of the UKF to simultaneously achieve maneuver feedback and an optimized estimation. Monte Carlo experiments on synthesized radar data showed that our algorithm had better performance on filtering precision compared with most maneuvering target tracking algorithms