Antiperspirant effects and mechanism investigation of Mulisan decoction in rats based on plasma metabolomics.

CONTEXT: Mulisan decoction (MLS) is a classic formula of traditional Chinese medicine for treating hyperhidrosis. The mechanism remains unclear. OBJECTIVE: To investigate the antiperspirant effect and underlying mechanisms of MLS. MATERIALS AND METHODS: Fifty rats were divided into control, model, and three doses of MLS intervention groups (n = 10). Rats except for control group were induced diseases features of the applicable scope of MLS via i.p. reserpine (0.5 mg/kg/d) for 10 days. From day 11, MLS groups were administrated orally MLS at 0.6, 3, and 15 g/kg once a day for 14 days, respectively. After the last administration, sweating was induced in all rats via s.c. pilocarpine (25 mg/kg), the right hind foot of rats was stained, and sweat point numbers were observed. Rat serum was collected to detect IL-2, IL-6, IFN-γ, and TNF-α. Rat plasma was collected for endogenous metabolite analysis via UPLC-QE-Focus-MS. RESULTS: Rats treated with MLS presented a significant decrease in sweat point numbers (13.5%), increase in body weight (13.2%), and promotion in the balance of Th1/Th2 cytokine ratio via increasing IL-2 (38.3%), IFN-γ (20.1%), and TNF-α (22.0%) and decreasing IL-6 (24.7%) compared with the model group (p < 0.05). Plasma metabolomics disclosed 15 potential biomarkers related to model rats, of which two could be significantly reversed by MLS (p < 0.05). The involved pathways were pantothenate and CoA biosynthesis, and porphyrin metabolism. CONCLUSIONS: MLS demonstrated a good antiperspirant effect and metabolism improvement. These findings inspire more clinical study validation on immune improvement and antiperspirant effect.

Unraveling the role of collateral circulation and serum ELAVL1 in carotid atherosclerosis and ischemic stroke: Insights from clinical observations.

BACKGROUND: The (embryonic lethal, abnormal vision, drosophila)-like protein 1 (ELAVL1) is a newly discovered protein associated with cerebral ischemic/reperfusion (I/R) injury. However, little is known of ELAVL1 in ischemic stroke patients. OBJECTIVES: To investigate the clinical significance of collateral circulation and serum ELAVL1 in patients with carotid atherosclerosis (CAS) and ischemic stroke. MATERIAL AND METHODS: The present prospective cohort investigation included 317 ischemic stroke patients and 300 CAS patients admitted between March 2020 and March 2022. Collateral circulation was measured using digital subtraction angiography (DSA) and graded using the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) grading system. Enzyme-linked immunosorbent assays (ELISAs) were used to measure serum ELAVL1, C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor alpha (TNF-α). The serum levels of total cholesterol (TC), triglyceride (TG), high-density leptin cholesterol (HDL-C), and low-density leptin cholesterol (LDL-C) were also measured. RESULTS: The serum levels of ELAVL1, CRP, IL-6, TNF-α, and LDL-C were all markedly higher, while HDL-C was significantly lower in ischemic stroke patients compared to the CAS patients. Serum ELAVL1 was markedly higher in ASITN/SIR grade 0-1 patients compared to grade 2-4 patients. Also, ELAVL1 correlated positively with serum CRP, IL-6, TNF-α, TC, and LDL-C, and negatively with HDL-C. Receiver operating characteristic (ROC) curves showed that ELAVL1 and collateral circulation have the potential to be used as biomarkers for the diagnosis of ischemic stroke. Meanwhile, CRP, IL-6, TNF-α, HDL-C, ASITN/SIR grading, and ELAVL1 were independent risk factors for ischemic stroke. CONCLUSIONS: We found that serum ELAVL1 was associated with clinical outcomes of ischemic stroke patients, while the combination of ELAVL1 and collateral circulation could be used as a potential biomarker for ischemic stroke diagnosis.

Co-Pigmentation Mechanism and Thermal Reaction Kinetics of Mulberry Anthocyanins with Different Phenolic Acids.

Applying the intermolecular co-pigmentation to improve the stability of mulberry anthocyanins is an important co-pigment method. Seven co-pigments, ferulic acid (FA), caffeic acid (CA), p-hydroxybenzoic acid (HBA), protocatechuic acid (PA), gallic acid (GA), vanillic acid (VA) and vanillin (VN) were selected to investigate mulberry anthocyanin co-pigmentation thermal reaction kinetics. The strongest co-pigment reactions were observed for FA at a molar ratio of 1:20, pH 3.5 and 20 °C, with the highest hyperchromic effects (52.94%), equilibrium constant (K) values (3.51) and negative values of Gibbs free energy (ΔG°) (-3.06 KJ/mol). Co-pigments that contained more free hydroxyl groups facilitated the co-pigmentation, and methyl contributed more to color enhancement, with respect to the hydrogen group. Ultra Performance Liquid Chromatography-Quadrupole-Time Of Flight-Mass/Mass Spectrometry (UPLC-Q-TOF-MS/MS) results indicated that FA and CA formed different anthocyanin derivatives with mulberry anthocyanin. The Fourier Transform Infrared Spectroscopy (FTIR) and molecular docking confirmed that hydrogen bonding, π-π stacking and hydrophobic interaction were formed between anthocyanins and three prevalent co-pigments (FA, CA and VA). CA and C3G could form four hydrogen bonds and two π-π stackings; this was the most stable system among three phenolic acid-C3G complexes. Due to the functional effect of phenolic acids, the addition of FA and CA not only enhanced the stability and color intensity of mulberry anthocyanins but also the functionality of the processing product.

Multidimensional soil salinity data mining and evaluation from different satellites.

Soil salinization, a common land degradation mode, restricts the ecological environment and is a global issue due to climate change. Accurately, quickly and effectively monitoring soil salinity is critical for governmental institutions that develop hazard prevention and mitigation strategies. Remote sensing (RS) technology provides a viable alternative to traditional field work due to its large area coverage, abundant spectral information and nearly constant observations. Key issues in RS-based soil salinity monitoring include the lack of both data-mining techniques for obtaining spectral band information and comprehensive considerations of synergies among different spectra. The main objective of this study was to provide in-depth explorations of data mining and integration algorithms from different satellites to multidimensionally evaluate soil salinity models. The Ebinur Lake Wetland Reserve (Xinjiang Province, China) was selected as a case study. First, ground-measured visible and near infrared (VIS-NIR) spectral data were combined with the RS band to simulate Landsat 8 (L8) and Sentinel 2 (S2) and 3 (S3) data. Second, one-dimensional RS bands and 15 soil salinity and vegetation indices were selected, and 15 spectral data transformations (reciprocal, differential, absorbance, etc.) were obtained. Two- and three-dimensional spectral indices were constructed, and the response relationships between different spectral indices and soil electrical conductivity (EC) were comprehensively explored. Finally, an integrated multidimensional algorithm was used to estimate soil salinity in high-performance models for the three satellites. The results showed that all data-mining-based model combinations performed well for all satellites (R2 > 0.80). However, with multidimensional model combinations, S3 presented the highest predictive capability (R2 = 0.89, RMSE = 2.57 mS·cm-1, RPD = 2.05), followed by S2 (R2 = 0.86, RMSE = 2.71 mS·cm-1, RPD = 1.90) and L8 (R2 = 0.85, RMSE = 2.84 mS·cm-1, RPD = 1.87). Therefore, data mining with integration algorithms in model combinations performs significantly better than previous models and could be considered a promising method for obtaining improved results from soil salinity susceptibility models in similar cases.

Hyperspectral monitor of soil chromium contaminant based on deep learning network model in the Eastern Junggar coalfield.

In China, over 10% of cultivated land is polluted by heavy metals, which can affect crop growth, food safety and human health. Therefore, how to effectively and quickly detect soil heavy metal pollution has become a critical issue. This study provides a novel data preprocessing method that can extract vital information from soil hyperspectra and uses different classification algorithms to detect levels of heavy metal contamination in soil. In this experiment, 160 soil samples from the Eastern Junggar Coalfield in Xinjiang were employed for verification, including 143 noncontaminated samples and 17 contaminated soil samples. Because the concentration of chromium in the soil exists in trace amounts, combined with the fact that spectral characteristics are easily influenced by other types of impurity in the soil, the evaluation of chromium concentrations in the soil through hyperspectral analysis is not satisfactory. To avoid this phenomenon, the pretreatment method of this experiment includes a combination of second derivative and data enhancement (DA) approaches. Then, support vector machine (SVM), k-nearest neighbour (KNN) and deep neural network (DNN) algorithms are used to create the discriminant models. The accuracies of the DA-SVM, DA-KNN and DA-DNN models were 95.61%, 95.62% and 96.25%, respectively. The results of this experiment demonstrate that soil hyperspectral technology combined with deep learning can be used to instantly monitor soil chromium pollution levels on a large scale. This research can be used for the management of polluted areas and agricultural insurance applications.

[Temporal and Spatial Distribution Characteristics of Aerosol Optical Properties in Urban Agglomerations on the North Slope of the Tianshan Mountains].

In order to explore the temporal and spatial distribution characteristics of atmospheric aerosol optical depth (AOD) in the urban agglomeration on the northern slope of the Tianshan Mountains, the temporal and spatial distribution characteristics and trends of changes in the AOD in the study area from 2000 to 2019 were analyzed by MODIS aerosol products(MCD19-A2). For 2016-2019, when the AOD was relatively stable, the parameters such as the AOD and Ångström wavelength index (α) were analyzed using multi-band sun photometer ground-based remote sensing technology. The results showed that ① the spatial distribution of AOD in the study area was consistent with the topography, and high values were mainly distributed in the low altitude area. The spatial distribution of AOD in the four seasons showed a strong seasonal change from spring (0.15±0.03) > autumn (0.14±0.03) > summer (0.14±0.02). ② In terms of time, the annual average AOD value of the study area was 0.12 from 2000 to 2019 with an annual growth rate of 1.03%, thereby showing an overall increasing trend. The annual variation in the monthly mean value of AOD was bimodal; the first and second peaks were in May and November. The main reason for the increase in AOD was the release and transmission of dust from natural sources and heating. ③ Under the influence of dust weather, the AOD changed sharply in spring, and the size and change range of aerosol particles were larger than those in summer. The high value of AOD in the study area was mainly affected by coarse mode particles. The moisture absorption growth of fine mode particles caused a fluctuation in the AOD, but it was not the cause of the high value of AOD.

Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI.

Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity. Yet, studies on detailed comparison in soil salinity tracking between Landsat-8 OLI and Sentinel-2 MSI remain limited. For this purpose, we collected a total of 64 topsoil samples in an arid desert region, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) to compare the monitoring accuracy between Landsat-8 OLI and Sentinel-2 MSI. In this study, the Cubist model was trained using RS-derived covariates (spectral bands, Tasseled Cap transformation-derived wetness (TCW), and satellite salinity indices) and laboratory measured electrical conductivity of 1:5 soil:water extract (EC). The results showed that the measured soil salinity had a significant correlation with surface soil moisture (Pearson's r = 0.75). The introduction of TCW generated satisfactory estimating performance. Compared with OLI dataset, the combination of MSI dataset and Cubist model yielded overall better model performance and accuracy measures (R2 = 0.912, RMSE = 6.462 dS m-1, NRMSE = 9.226%, RPD = 3.400 and RPIQ = 6.824, respectively). The differences between Landsat-8 OLI and Sentinel-2 MSI were distinguishable. In conclusion, MSI image with finer spatial resolution performed better than OLI. Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map. The increased temporal revisiting frequency and spectral resolution of MSI data are expected to be positive enhancements to the acquisition of high-quality soil salinity information of desert soils.

[Spatiotemporal Evolution and Driving Mechanism of Aerosol Optical Depth in the Ebinur Lake Basin].

Aerosol optical depth (AOD) describes the attenuation of light by aerosols and reflects the degree of regional air pollution to some extent. This study was based on the data from the long-term sequence MOD09A1 from 2000 to 2015 and the generation of a lookup table using the deep blue algorithm (DB) to perform AOD remote sensing estimation on the Landsat TM/ETM+/OLI data from the Ebinur Lake Basin to analyze the temporal and spatial variation characteristics of AOD in the Ebinur Lake Basin and to perform an AOD prediction and factor contribution ranking using the random forest model (RF) combined with environmental variables. The results showed that:① AOD of Ebinur Lake Basin has significant seasonal variation characteristics, and the AOD values were spring (0.414) > summer (0.390) > autumn (0.287), with the largest variation in spring. ② The AOD average of the Ebinur Lake Basin was 0.374, and the interannual variation as a whole showed an upward trend. However, the AOD increased rapidly during 2010-2015, with an interannual increase of 32.32%, which indicated increasing air pollution in the basin over the past 15 years, especially the past five years. ③ The spatial distribution of AOD in the Ebinur Lake Basin was stepped up from the north to the south of Lake Ebinur. In this area, the pollution in Jinghe County was the most prominent, and the AOD value reached 0.483. ④ The RF model had a good predictive effect on AOD, R2=0.866, RMSE=0.042, and evapotranspiration had the most significant effect on AOD in the Ebinur Lake basin.