Use of Transcriptome Sequencing to Analyze the Effects of Different Doses of an Astragalus-Rhubarb-Saffron Mixture in Mice with Diabetic Kidney Disease.

Purpose: To investigate the therapeutic effect and underlying mechanism of a traditional Chinese medicine (TCM) mixture consisting of Astragalus, rhubarb, and saffron in a mouse model of diabetic kidney disease (DKD). Methods: Forty-eight db/db mice received no TCM (DKD model), low-dose TCM, medium-dose TCM, or high-dose TCM, and an additional 12 db/m mice received no TCM (normal control). Intragastric TCM or saline (controls) was administered daily for 24 weeks. Blood glucose, body weight, serum creatinine (SCr), blood urea nitrogen (BUN), blood lipids, and urinary microalbumin were measured every four weeks, and the urinary albumin excretion rate (UAER) was calculated. After 24 weeks, kidney tissues were collected for transcriptome sequencing, and the main functions of these genes were determined via functional enrichment analysis. Results: Compared with the DKD model group, the medium-dose and high-dose TCM groups had significantly decreased levels of SCr, BUN, total cholesterol, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and UAER (all p<0.05). We identified 42 genes that potentially functioned in this therapeutic response, and the greatest effect on gene expression was in the high-dose TCM group. We also performed functional enrichment analysis to explore the potential mechanisms of action of these different genes. Conclusion: A high-dose of the Astragalus-rhubarb-saffron TCM provided the best prevention of DKD. Analysis of the kidney transcriptome suggested that this TCM mixture may prevent DKD by altering immune responses and oxygen delivery by hemoglobin.

Co-differential genes between DKD and aging: implications for a diagnostic model of DKD.

Objective: Diabetic kidney disease (DKD) is a serious complication of diabetes mellitus (DM) that is closely related to aging. In this study, we found co-differential genes between DKD and aging and established a diagnostic model of DKD based on these genes. Methods: Differentially expressed genes (DEGs) in DKD were screened using GEO datasets. The intersection of the DEGs of DKD and aging-related genes revealed DKD and aging co-differential genes. Based on this, a genetic diagnostic model for DKD was constructed using LASSO regression. The characteristics of these genes were investigated using consensus clustering, WGCNA, functional enrichment, and immune cell infiltration. Finally, the expression of diagnostic model genes was analyzed using single-cell RNA sequencing (scRNA-seq) in DKD mice (model constructed by streptozotocin (STZ) injection and confirmed by tissue section staining). Results: First, there were 159 common differential genes between DKD and aging, 15 of which were significant. These co-differential genes were involved in stress, glucolipid metabolism, and immunological functions. Second, a genetic diagnostic model (including IGF1, CETP, PCK1, FOS, and HSPA1A) was developed based on these genes. Validation of these model genes in scRNA-seq data revealed statistically significant variations in FOS, HSPA1A, and PCK1 gene expression between the early DKD and control groups. Validation of these model genes in the kidneys of DKD mice revealed that Igf1, Fos, Pck1, and Hspa1a had lower expression in DKD mice, with Igf1 expression being statistically significant. Conclusion: Our findings suggest that DKD and aging co-differential genes are significant in DKD diagnosis, providing a theoretical basis for novel research directions on DKD.

[Characterization of Metal Elements in Atmospheric PM2.5 and Health Risk Assessment in Heze in Winter from 2017 to 2018].

Atmospheric PM2.5 samples were collected in Heze, Shandong Province, from a total of three sampling sites at Heze College, Huarun Pharmacy, and a wastewater treatment plant between October 15, 2017 and January 31, 2018, to determine the concentrations of 21 metal elements in PM2.5 using inductively coupled plasma mass spectrometry (ICP-MS). The degree of elemental enrichment was also discussed, the health risks and potential heavy metal ecological risks were assessed. The results showed that ρ (PM2.5) ranged from 26.7 to 284.1 µg·m-3 at the three sampling sites during the sampling period, and the concentration values did not differ significantly, all of which were at high pollution levels. The highest concentrations of K were found in the three sampling sites, accounting for 31.03%, 39.47%, and 38.43% of the total, respectively, mainly due to the high contribution of biomass burning in autumn and winter in Heze, a large agricultural city. The highest concentrations of Zn, 89.70, 84.21, and 67.68 ng·m-3, were found in the trace elements at the three sampling sites, respectively. The enrichment factor results showed that the enrichment factor values of Zn, Pb, Sn, Sb, Cd, and Se were higher than 100, among which the enrichment factors of Cd and Se were higher than 2 000 and 4 000, respectively, which were significantly influenced by anthropogenic activities and might have been related to industrial production, metal smelting, road sources, and coal combustion emissions. The health risk results showed that there was some potential non-carcinogenic risk (HQ>0.1 for children and adults) for As and a combined potential non-carcinogenic risk (HI>0.1) and some potential carcinogenic risk (CRT>1×10-6) for both children and adults at the three sampling sites. There was a more significant carcinogenic risk (CRT>1×10-4) for adults at the wastewater treatment plant, and the slightly higher carcinogenic risk for adults than that for children may have been related to the longer outdoor activity and higher PM2.5 exposure for adults. The elements with the highest potential ecological risk values were Cd, As, and Pb, with Cd exhibiting a very high potential ecological risk that should be taken seriously. All three sampling sites showed a very high combined potential ecological risk, with the intensity spatially expressed as Heze College>Huarun Pharmacy>wastewater treatment plant.

Chemodiversity of organic nitrogen emissions from light-duty gasoline vehicles is governed by engine displacements and driving speed.

Organic nitrogen emissions from light-duty gasoline vehicles (LDGVs) is believed to play a pivotal role in atmospheric particulate matter (PM) in urban environments. Here, the characterization of organic nitrogen emitted by LDGVs with varying engine displacements at different speed phases was analyzed using a Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) at molecular level. For the LDGV with small engine displacements, the nitrogen-containing organic (CHON) compounds exhibit higher abundance, molecular weight, oxygen content and aromaticity in the extra-high-speed phase. Conversely, for the LDGV with big engine displacements, more CHON compounds with elevated abundance, molecular weight, oxygen content and aromaticity were observed in the low-speed phase. Our study assumed that the formation of CHON compounds emitted from LDGVs is mainly the oxidation reaction during fuel combustion, so the potential precursor-product pairs related to oxidation process were used to study the degree of combustion reaction. The results show that the highest proportion of oxidation occurs during extra-high-speed phase for LDGV with small engine displacements, and during low-speed phase for LDGV with big engine displacements. These results offer a novel perspective for comprehending the mechanism behind vehicle emissions formation and contribute valuable insights for crafting effective air pollution regulations.

Directed Evolution of 4-Hydroxyphenylpyruvate Biosensors Based on a Dual Selection System.

Biosensors based on allosteric transcription factors have been widely used in synthetic biology. In this study, we utilized the Acinetobacter ADP1 transcription factor PobR to develop a biosensor activating the PpobA promoter when bound to its natural ligand, 4-hydroxybenzoic acid (4HB). To screen for PobR mutants responsive to 4-hydroxyphenylpyruvate(HPP), we developed a dual selection system in E. coli. The positive selection of this system was used to enrich PobR mutants that identified the required ligands. The following negative selection eliminated or weakened PobR mutants that still responded to 4HB. Directed evolution of the PobR library resulted in a variant where PobRW177R was 5.1 times more reactive to 4-hydroxyphenylpyruvate than PobRWT. Overall, we developed an efficient dual selection system for directed evolution of biosensors.

Identification and Functional Mechanism Verification of Novel MicroRNAs Associated with the Fibrosis Progression in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a serious threat to human health worldwide, and its incidence is increasing annually. A growing amount of information is emerging about the role of micoRNAs (miRNAs) in the regulation of renal fibrosis, which has aroused interest in the development of drugs that block pathogenic miRNAs or restore protective miRNAs levels. To clarify the role of miRNAs in CKD, we selected patients with significant renal fibrotic disease (diabetic nephropathy (DN) and focal segmental glomerulosclerosis (FSGS)) as the disease group, and patients with little or no renal fibrotic disease (minimal change disease (MCD) and renal carcinoma adjacent to normal kidney) as controls. Significantly differentially expressed miRNAs were obtained by human kidney tissue sequencing, subsequently verified in mice models of DN and FSGS, and subsequently inhibited or overexpressed in human renal tubular epithelial cells (HK-2) stimulated by high glucose (HG) and TGF-ß1 in vitro. Therefore, the mechanism of its action in renal fibrosis was further elaborated. Finally, the downstream target genes of the corresponding miRNAs were verified by bioinformatics analysis, qRT-PCR, western blot and double luciferase report analysis. Two novel miRNAs, hsa-miR-1470-3p (miR-1470) and hsa-miR-4483-3p (miR-4483), were detected by renal tissue sequencing in the disease group with significant renal fibrosis (DN and FSGS) and the control group with little or no renal fibrosis (MCD and normal renal tissue adjacent to renal carcinoma). Subsequent human renal tissue qRT-PCR verified that the expression of miR-1470 was significantly increased, while the expression of miR-4483 was markedly decreased in the disease group (p < 0.05). Moreover, in vivo DN and FSGS mice models, the expression levels of miR-1470 and miR-4483 were consistent with the results of human kidney tissue. In vitro, miR-4483 was suppressed, whereas miR-1470 was induced by treatment with TGF-ß1 or HG. Inhibition of miR-1470 or overexpression of miR-4483 promoted HG or TGF-ß1-induced fibrosis in HK-2 cells. Further study revealed that MMP-13 and TIMP1 were the target genes ofmiR-1470 and miR-4483, respectively. Our study identifies newly dysregulated miRNA profiles related to fibrosis kidneys. miR-1470 and miR-4483 are demonstrated to participate in kidney fibrosis by regulation of MMP-13, TIMP1 respectively. Our results may represent a promising research direction for renal disorders and help identify new biomarkers and therapeutic targets for CKD.

Selective Access to Functional Fluoroenones via Palladium-Catalyzed Selenofluoroalkylacylation of Terminal Alkynes.

The trifluoromethylacyl group (-COCF3) is an important motif and widely studied in catalysis, medicinal chemistry, and materials science. Herein, a novel palladium-catalyzed selenofluoroalkylacylation of terminal alkynes with commercially available fluoroalkyl anhydride and diorganyl diselenides to afford ß-seleno and aryl/alkyl disubstituted enones under mild conditions is disclosed. In addition, selenodifluoroacetylations and selenoperfluoroacetylations are also suitable for this reaction. Mechanistic studies reveal that this reaction proceeds via an oxidative radical-polar crossover process.

Using 16S rDNA and metagenomic sequencing technology to analyze the fecal microbiome of children with avoidant/restrictive food intake disorder.

To investigate the gut microbiota distribution and its functions in children with avoidant/restrictive food intake disorder (ARFID). A total of 135 children were enrolled in the study, including 102 children with ARFID and 33 healthy children. Fecal samples were analyzed to explore differences in gut microbiota composition and diversity and functional differences between the ARFID and healthy control (HC) groups via 16S rDNA and metagenomic sequencing. The gut microbiota composition and diversity in children with ARFID were different from those in heathy children, but there is no difference in the composition and diversity of gut microbiota between children at the age of 3-6 and 7-12 with ARFID. At the phylum level, the most abundant microbes in the two groups identified by 16S rDNA and metagenomic sequencing were the same. At the genus level, the abundance of Bacteroides was higher in the ARFID group (P > 0.05); however, different from the result of 16SrDNA sequencing, metagenomic sequencing showed that the abundance of Bacteroides in the ARFID group was significantly higher than that in the HC group (P = 0.041). At the species level, Escherichia coli, Streptococcus thermophilus and Lachnospira eligens were the most abundant taxa in the ARFID group, and Prevotella copri, Bifidobacterium pseudocatenulatum, and Ruminococcus gnavus were the top three microbial taxa in the HC group; there were no statistically significant differences between the abundance of these microbial taxa in the two groups. LefSe analysis indicated a greater abundance of the order Enterobacterales and its corresponding family Enterobacteriaceae, the family Bacteroidaceae and corresponding genus Bacteroides, the species Bacteroides vulgatus in ARFID group, while the abundance of the phylum Actinobacteriota and its corresponding class Actinobacteria , the order Bifidobacteriales and corresponding family Bifidobacteriaceae, the genus Bifidobacterium were enriched in the HC group. There were no statistically significant differences in the Chao1, Shannon and Simpson indices between the Y1 and Y2 groups (P = 0.1, P = 0.06, P = 0.06). At the phylum level, Bacillota, Bacteroidota, Proteobacteria and Actinobacteriota were the most abundant taxa in both groups, but there were no statistically significant differences among the abundance of these bacteria (P = 0.958, P = 0.456, P = 0.473, P = 0.065). At the genus level, Faecalibacterium was more abundant in the Y2 group than in the Y1 group, and the difference was statistically significant (P = 0.037). The KEGG annotation results showed no significant difference in gut microbiota function between children with ARFID and healthy children; however, GT26 was significantly enriched in children with ARFID based on the CAZy database. The most abundant antibiotic resistance genes in the ARFID group were the vanT, tetQ, adeF, ermF genes, and the abundance of macrolide resistance genes in the ARFID group was significantly higher than that in the HC group (P = 0.041). Compared with healthy children, children with ARFID have a different distribution of the gut microbiota and functional genes. This indicates that the gut microbiome might play an important role in the pathogenesis of ARFID.Clinical trial registration: ChiCTR2300074759.

A review on the relationship between Arachidonic acid 15-Lipoxygenase (ALOX15) and diabetes mellitus.

Arachidonic acid 15-lipoxygenase (ALOX15), as one of the lipoxygenase family, is mainly responsible for catalyzing the oxidation of various fatty acids to produce a variety of lipid components, contributing to the pathophysiological processes of various immune and inflammatory diseases. Studies have shown that ALOX15 and its related products are widely distributed in human tissues and related to multiple diseases such as liver, cardiovascular, cerebrovascular diseases, diabetes mellitus and other diseases. Diabetes mellitus (DM), the disease studied in this article, is a metabolic disease characterized by a chronic increase in blood glucose levels, which is significantly related to inflammation, oxidative stress, ferroptosis and other mechanisms, and it has a high incidence in the population, accompanied by a variety of complications. Figuring out how ALOX15 is involved in DM is critical to understanding its role in diseases. Therefore, ALOX15 inhibitors or combination therapy containing inhibitors may deliver a novel research direction for the treatment of DM and its complications. This article aims to review the biological effect and the possible function of ALOX15 in the pathogenesis of DM.

UAV hyperspectral combined with LiDAR to estimate chlorophyll content at the stand and individual tree scales.

Chlorophyll is an important indicator of vegetation health status, accurate estimation of which is important for evaluating forest carbon sink. In this study, we estimated the chlorophyll content of coniferous forests, broad-leaved forests and mixed forest stands at stand and individual tree level by unmanned air vehicle (UAV) hyperspectral data combined with light detection and ranging (LiDAR) point clouds, which improved the non-destructive estimation accuracy of forest chlorophyll. We further comprehensively analyzed the spatial distribution of chlorophyll content at different scales. A total of 36 spectral characteristic variables related to chlorophyll content were screened by correlation analysis based on the fusion of UAV hyperspectral data and LiDAR point clouds combining with the empirical data from ground plots. We constructed multiple models for chlorophyll estimation by using statistical model, including multiple stepwise regression, BP neural network, BP neural network optimized by firefly algorithm, random forest and hybrid data-driven PROSPECT mechanism model. The optimal model was selected to estimate the chlorophyll content. The horizontal and vertical distribution of chlorophyll content at the stand level and individual tree level were analyzed. The results showed that the random forest model was superior to the models constructed by multiple stepwise regression, BP neural network and BP neural network optimized by firefly algorithm for chlorophyll estimation with R2 and RMSE of 0.59-0.64 and 3.79-5.83 µg·cm-2, respectively. The accuracy of the mechanism model was the highest, with R2 and RMSE of 0.97 and 3.40 µg·cm-2. The chlorophyll contents differed across stand types, with that in broad-leaved forest (25.25-31.60 µg·cm-2) being higher than mixed forest (13.52-23.93 µg·cm-2) and coniferous forest (6.40-13.71 µg·cm-2). There were significant differences in chlorophyll contents the in vertical direction among different stands. For individual tree of different species, the chlorophyll content inside the canopy was lower than that outside the canopy in the horizontal direction. In the vertical direction, there was no difference in chlorophyll content among different layers of Pinus sylvestris var. mongolica canopy. However, significant differences were observed among the upper, middle, and lower layers of Juglans mandshurica canopy. Using the fusion of hyperspectral image and LiDAR point cloud data, the mechanism model driven by hybrid data could effectively improve the accuracy and stability of chlorophyll content estimation at different scales.

Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice.

Background: Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD. Materials and Methods: Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software "Limma", differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (-logFC- > 1, p-value < 0.05) were visualized by heatmaps and volcano plots respectively. Next, the co-expression genes expressed in the three groups of DEGs were obtained by constructing a Venn diagram. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were further analyzed the related functions and enrichment pathways of these co-expression genes. Then, qRT-PCR was used to verify the expression levels of co-expression genes in the kidney of DKD and control mice. Finally, protein-protein interaction network (PPI), GO, KEGG analysis and Pearson correlation test were performed on the experimentally validated genes, in order to clarify the possible mechanism of them in DKD. Results: Our RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism-cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated). Conclusion: Our study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD.

Reliability of information about the use of antiepileptic drugs during pregnancy from three major web search engines in China.

OBJECTIVES: The objective of this study was to assess the reliability of online information, as provided by three major search engines in China, about the usage of antiepileptic drugs (AEDs) during pregnancy. METHOD: Over eight weeks, six physicians conducted a literature search on six computers and six smartphones at a frequency of once per week. During each web search on each computer and smartphone, three major search engines in China were used, namely, Baidu, Sogou and 360. The search terms used were a combination of words, including one AED name (valproate/oxcarbazepine/levetiracetam/lamotrigine) and one Chinese word ("huaiyun" or "renshen", which means pregnancy in Chinese). The top ten websites retrieved from each search were recorded. After the content of each website was evaluated, the sites were categorized into 9 types. Meanwhile, commercial advertisements on each web page were also registered. RESULTS: A total of 16,411 search results were assessed. After excluding the redundant web pages, 4840 search results were included in the data analysis. Only 12.05% of the search results were reliable, 47.75% were partly reliable, and 40.21% were unreliable. A total of 4139 (85.52%) webpages contained commercial advertisements. The results from a multivariate analysis suggested that websites with no advertisements and professional websites have an independent positive impact on reliability. CONCLUSION: Overall, little information on AED usage during pregnancy provided by major search engines in China was reliable. PRACTICE IMPLICATIONS: Accurate and professional online information for female patients with epilepsy should be provided through major efforts by the government, search engine companies, professional websites and epilepsy physicians.