Efficacy of Probiotics Supplementation On Chronic Kidney Disease: a Systematic Review and Meta-Analysis

Background/Aims: Dysbiosis of the intestinal microbiota may accelerate the progression of chronic kidney disease (CKD) by increasing the levels of urea toxins. In recent years, probiotics have been recognized to maintain the physiological balance of the intestinal microbiota. In this study, we aim to assess the therapeutic effects of probiotics on CKD patients with and without dialysis via meta-analysis. Methods: We conducted a meta-analysis of randomized controlled trials (RCTs) by searching the databases of Pubmed, EMBASE and Cochrane Library (No. CRD42018093080). Studies on probiotics for treatment of CKD adults lasting for at least 4 weeks were selected. The primary outcomes were the levels of urea toxins, and the second outcomes were the levels of interleukin (IL)-6, C-reactive protein (CRP) and hemoglobin (Hb). The risk of bias was assessed by Cochrane Collaboration’ tool, and the quality of evidence was appraised with the Grading of Recommendation Assessment. Means and standard deviations were analyzed by random effects analysis. Stratified analysis was done and sensitivity analysis was performed when appropriate. Results: Totally, eight studies with 261 patients at CKD stage 3 to 5 with and without dialysis were included. We found a decrease of p-cresyl sulfate (PCS) of 3 studies with 125 subjects (P = 0.01, SMD -0.57, 95% CI, -0.99 to -0.14, I2 = 25%) and an increase of IL-6 in 3 studies with 134 subjects (P = 0.03, 95% CI, SMD 0.37, 0.03 to 0.72, I2 = 0%) in the probiotics groups. Analysis of serum creatinine (P = 0.47), blood urine nitrogen (P = 0.73), CRP (P = 0.55) and Hb (P = 0.49) yielded insignificant difference. Conclusion: Limited number of studies and small sample size are limitations of our study. Probiotics supplementation may reduce the levels of PCS and elevate the levels of IL-6 whereby protecting the intestinal epithelial barrier of patients with CKD

LCK as a Potential Therapeutic Target for Acute Rejection after Kidney Transplantation: A Bioinformatics Clue

Objectives. We aim to identify the key biomarker of acute rejection (AR) after kidney transplantation via bioinformatics methods. Methods. The gene expression data GSE75693 of 30 samples with stable kidney transplantation recipients and 15 AR samples were downloaded and analyzed by the limma package to identify differentially expressed genes (DEGs). Then, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were done to explore the biological functions and potential important pathways of DEGs. Finally, protein-protein interactions (PPIs) and literature mining were applied to construct the cocitation network and to select the hub protein. Results. A total of 437 upregulated genes and 353 downregulated genes were selected according to P1.0. DEGs of AR are mainly located on membranes and impact the activation of receptors in immune responses. In the PPI network, Src kinase, lymphocyte kinase (LCK), CD3G, B2M, interferon-γ, CD3D, tumor necrosis factor, VAV1, and CD3E in the T cell receptor signaling pathway were selected as important factors, and LCK was identified as the hub protein. Conclusion. LCK, via acting on T-cell receptor, might be a potential therapeutic target for AR after kidney transplantation

Construction Formula of Biological Age Using the Principal Component Analysis

The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula

Construction Formula of Biological Age Using the Principal Component Analysis

The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula

Ester-Producing Mechanism of Ethanol O-acyltransferase EHT1 Gene in Pichia pastoris from Shanxi Aged Vinegar

The ethanol O-acyltransferase EHT1 is an important element of key signaling pathways and is widely expressed in yeast strains. In this study, we investigated the expression of EHT1 in the overexpression lines or knockout system of Pichia pastoris using qRT-PCR and western blotting. The amount of total protein was determined using the Bradford method; the esterase activity was determined using p-nitrophenyl acetate as a substrate, and the production of volatile fatty acids in wild-type, knockout, and over-expression systems was detected using SPME GC-MS. The esterase activity of EHT1-knockout P. pastoris was significantly lower than that in wild type (P<0.01), and the activities of esterase in three EHT1-overexpressing strains—OE-1, OE-2, and OE-3—were significantly higher than those in wild type (P<0.01). In the EHT1-knockout strain products, the contents of nine volatile fatty acids were significantly lower than those in wild type (P<0.01), and the relative percentages of three fatty acids, methyl nonanoate, methyl decanoate, and ethyl caprate, were significantly lower than those in the other six species in the wild-type and knockout groups (P<0.05). The nine volatile fatty acids in the fermentation products of the overexpressed EHT1 gene were significantly higher than those in the wild-type group (P<0.01). The relative percentages of the three fatty acid esters, methyl nonanoate, methyl caprate, and ethyl caprate, were significantly higher than those in the other six species (P<0.05). EHT1 plays an important regulatory role in esterase activity and the production of medium-chain volatile fatty acids

Targeting secretory leukocyte protease inhibitor (SLPI) inhibits colorectal cancer cell growth, migration and invasion via downregulation of AKT

The secretory leukocyte protease inhibitor (SLPI) is a serine protease inhibitor which plays important role in bacterial infection, inflammation, wound healing and epithelial proliferation. Dysregulation of SLPI has been reported in a variety of human cancers including glioblastoma, lung, breast, ovarian and colorectal carcinomas and is associated with tumor aggressiveness and metastatic potential. However, the pathogenic role of SLPI in colorectal cancer is still unclear. Here we showed that SLPI mRNA level was significantly upregulated in colorectal cancer tissues compared to adjacent normal controls. Targeting SLPI by siRNA inhibited proliferation, migration and invasion of colorectal cancer cells lines HT29 and HT116 in vitro. Mechanistically, blockage of cancer cell growth and metastasis after SLPI knockdown was associated with down-regulation of AKT signaling. In conclusion, SLPI regulated colorectal cell growth and metastasis via AKT signaling. SLPI may be a novel biomarker and therapeutic target for colorectal cancer. Targeting AKT signaling may be effective for colorectal cancer treatment

A cross‐sectional study of the interaction between night shift frequency and age on hypertension prevalence among female nurses

Abstract Night shift is a common work schedule. This study aimed to analyze the interaction between age and frequency of night shift on the hypertension prevalence. A census questionnaire was conducted in 512 medical institutions in 11 cities of Hebei Province. One lakh twenty‐one thousand nine hundred three female nurses were included in this study. Binary Logistic regression analysis was done by SPSS Version 26.0. The youngest age group without night shift was used as the reference group. The odds ratio was calculated by different combinations of interaction items. Interaction coefficients were calculated by an Excel table designed by Andersson. Compared with the 18–25 year old ones without night shift, there existed an additive interaction between the age of 36–45 and more than 5–10 night shifts per month on hypertension prevalence. Odds ratio, the relative excess risk of interaction, the attributable proportion of interaction, and the synergy index and their 95% confidence intervals were 2.923(2.292‐3.727), 0.631(0.309‐0.954), 0.216(0.109‐0.323), 1.488(1.158‐1.913). Additive interaction was also found between the age of 36–45 and more than 10 night shifts per month. OR, RERI, API, SI, and their 95% confidence intervals were 3.430(2.273‐5.175) 1.037(0.061‐2.013), 0.303(0.089‐0.516), and 1.746(1.093‐2.788). There also existed an additive interaction between the age of 46–65 and more than 5–10 night shifts per month on hypertension prevalence. OR, RERI, API, SI, and their 95% confidence intervals were 7.398(5.595‐9.781) 1.809(0.880‐2.739), 0.245(0.148‐0.341), and 1.394(1.199‐1.622).There existed interaction between specific age groups and night shift frequency on the prevalence of hypertension among female nurses