Inflammatory indexes and anemia in chronic kidney disease: correlation and survival analysis of the National Health and Nutrition Examination Survey 2005-2018.

BACKGROUND: There is currently no research on the correlation between novel inflammatory indexes systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and the risk of anemia in chronic kidney disease (CKD) population, as well as survival analysis in CKD with anemia. METHODS: This investigation encompassed 4444 adult subjects out of the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018. The study utilized multi-variable logistic regression to assess the relationship between SII, NLR, PLR, and anemia risk occurrence in CKD population. Survival differences in CKD patients with anemia, based on varying levels of SII, NLR, and PLR were evaluated employing Kaplan-Meier and Cox proportional hazards models. RESULTS: The adjusted logistic regression model demonstrates that SII, NLR, and PLR are associated with the risk of anemia occurrence in CKD population. Kaplan-Meier's analysis reveals significant differences in survival rates among CKD patients with anemia stratified by NLR levels. The adjusted Cox proportional hazards model shows that the higher NLR group has a 30% elevated risk of all-cause mortality contrasted with lower group (hazard ratio, HR: 1.30, confidence interval (CI) [1.01, 1.66], p value <.04). Restricted cubic spline (RCS) demonstrates no nonlinear relationship between NLR and all-cause mortality. Lastly, sub-cohort analysis indicates that in populations with diabetes, hypertension, and hyperuricemia, NLR levels have a greater impact on all-cause mortality. CONCLUSIONS: Controlling inflammation may reduce the occurrence of anemia in CKD populations, with NLR serving to be a potential prognostic indicator for survival results within CKD patients suffering from co-morbid anemia.

Causal role of immune cells in aplastic anemia: Mendelian randomization (MR) study.

Prior research has identified associations between immune cells and aplastic anaemia (AA); however, the causal relationships between them have not been conclusively established. A two-sample Mendelian randomisation analysis was conducted to investigate the causal link between 731 immune cell signatures and AA risk using publicly available genetic data. Four types of immune signatures, including relative cell, absolute cell (AC), median fluorescence intensities and morphological parameters, were considered sensitivity analyses were also performed to verify the robustness of the results and assess potential issues such as heterogeneity and horizontal pleiotropy. Following multiple test adjustments using the False Discovery Rate (FDR) method, no statistically significant impact of any immunophenotype on AA was observed. However, twelve immunophenotypes exhibited a significant correlation with AA without FDR correction (p of IVW < 0.01), of which eight were harmful to AA: CD127- CD8br %T cell (Treg panel), CD25 on IgD + CD38dim (B cell panel), CD38 on naive-mature B cell (B cell panel), CD39 + resting Treg % CD4 Treg (Treg panel), CD39 + secreting Treg AC (Treg panel), CD8 on CD28 + CD45RA- CD8br (Treg panel), HLA DR + NK AC (TBNK panel), Naive DN (CD4-CD8-) AC (Maturation stages of T cell panel); and four were protective to AA: CD86 on CD62L + myeloid DC (cDC panel), DC AC (cDC panel), DN (CD4-CD8-) NKT %T cell (TBNK panel), and TD CD4 + AC (Maturation stages of T cell panel). The results of this study demonstrate a close link between immune cells and AA by genetic means, thereby improving the current understanding of the interaction between immune cells and AA risk and providing guidance for future clinical research.

Effect of different food matrices on the bioaccessibility of vitamin D3 in beverage systems: Comparison between juice and liquid milk.

Vitamin D plays a crucial role in bone, immunology, and neurophysiological functions but has inadequate bioavailability in the human body. In this paper, six different liquid beverages were used for vitamin D3 fortification, investigating the effect of different food matrices on the bioaccessibility of vitamin D. Not from concentrate (NFC) apple juice (9.34%) and NFC orange juice (8.12%) presented about 20% higher bioaccessibility of vitamin D3 than soybean and skim milk, and achieved a similar value of whole milk (8.04%). Meanwhile, the bioaccessibility of NFC apple and orange juice was markedly about 120% higher than that of apple clear juice. From the correlation analysis, the bioaccessibility of VD3 indicated significant correlations with small intestine retention (0.82) and viscosity (0.66). But small intestinal particle size showed a negative effect on bioaccessibility (-0.78). Therefore, food components, delivery matrices, and physicochemical properties of digesta were key factors to achieve higher bioaccessibility for guiding formulation design.

Cathepsin S (CTSS) in IgA nephropathy: an exploratory study on its role as a potential diagnostic biomarker and therapeutic target.

Introduction: IgA nephropathy (IgAN), a prevalent form of glomerulonephritis globally, exhibits complex pathogenesis. Cathepsins, cysteine proteases within lysosomes, are implicated in various physiological and pathological processes, including renal conditions. Prior observational studies have suggested a potential link between cathepsins and IgAN, yet the precise causal relationship remains unclear. Methods: We conducted a comprehensive bidirectional and multivariable Mendelian randomization (MR) study using publicly available genetic data to explore the causal association between cathepsins and IgAN systematically. Additionally, immunohistochemical (IHC) staining and enzyme-linked immunosorbent assay (ELISA) were employed to evaluate cathepsin expression levels in renal tissues and serum of IgAN patients. We investigated the underlying mechanisms via gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Molecular docking and virtual screening were also performed to identify potential drug candidates through drug repositioning. Results: Univariate MR analyses demonstrated a significant link between increased cathepsin S (CTSS) levels and a heightened risk of IgAN. This was evidenced by an odds ratio (OR) of 1.041 (95% CI=1.009-1.073, P=0.012) as estimated using the inverse variance weighting (IVW) method. In multivariable MR analysis, even after adjusting for other cathepsins, elevated CTSS levels continued to show a strong correlation with an increased risk of IgAN (IVW P=0.020, OR=1.037, 95% CI=1.006-1.069). However, reverse MR analyses did not establish a causal relationship between IgAN and various cathepsins. IHC and ELISA findings revealed significant overexpression of CTSS in both renal tissues and serum of IgAN patients compared to controls, and this high expression was unique to IgAN compared with several other primary kidney diseases such as membranous nephropathy, minimal change disease and focal segmental glomerulosclerosis. Investigations into immune cell infiltration, GSEA, and GSVA highlighted the role of CTSS expression in the immune dysregulation observed in IgAN. Molecular docking and virtual screening pinpointed Camostat mesylate, c-Kit-IN-1, and Mocetinostat as the top drug candidates for targeting CTSS. Conclusion: Elevated CTSS levels are associated with an increased risk of IgAN, and this enzyme is notably overexpressed in IgAN patients' serum and renal tissues. CTSS could potentially act as a diagnostic biomarker, providing new avenues for diagnosing and treating IgAN.

The pivotal role of glucose transporter 1 in diabetic kidney disease.

Diabetes mellitus (DM) is a significant public health problem. Diabetic kidney disease (DKD) is the most common complication of DM, and its incidence has been increasing with the increasing prevalence of DM. Given the association between DKD and mortality in patients with DM, DKD is a significant burden on public health resources. Despite its significance in DM progression, the pathogenesis of DKD remains unclear. Aberrant glucose uptake by cells is an important pathophysiological mechanism underlying DKD renal injury. Glucose is transported across the bilayer cell membrane by a glucose transporter (GLUT) located on the cell membrane. Multiple GLUT proteins have been identified in the kidney, and GLUT1 is one of the most abundantly expressed isoforms. GLUT1 is a crucial regulator of intracellular glucose metabolism and plays a key pathological role in the phenotypic changes in DKD mesangial cells. In an attempt to understand the pathogenesis of DKD better, we here present a review of studies on the role of GLUT1 in the development and progression of DKD.

Transcriptomic analysis reveals hub genes and pathways in response to acetic acid stress in Kluyveromyces marxianus during high-temperature ethanol fermentation.

The thermotolerant yeast Kluyveromyces marxianus is known for its potential in high-temperature ethanol fermentation, yet it suffers from excess acetic acid production at elevated temperatures, which hinders ethanol production. To better understand how the yeast responds to acetic acid stress during high-temperature ethanol fermentation, this study investigated its transcriptomic changes under this condition. RNA sequencing (RNA-seq) was used to identify differentially expressed genes (DEGs) and enriched gene ontology (GO) terms and pathways under acetic acid stress. The results showed that 611 genes were differentially expressed, and GO and pathway enrichment analysis revealed that acetic acid stress promoted protein catabolism but repressed protein synthesis during high-temperature fermentation. Protein-protein interaction (PPI) networks were also constructed based on the interactions between proteins coded by the DEGs. Hub genes and key modules in the PPI networks were identified, providing insight into the mechanisms of this yeast's response to acetic acid stress. The findings suggest that the decrease in ethanol production is caused by the imbalance between protein catabolism and protein synthesis. Overall, this study provides valuable insights into the mechanisms of K. marxianus's response to acetic acid stress and highlights the importance of maintaining a proper balance between protein catabolism and protein synthesis for high-temperature ethanol fermentation.

Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis.

BACKGROUND: Lupus nephritis (LN) is one of the most severe complications of systemic lupus erythematosus (SLE). However, the current management of LN remains unsatisfactory due to sneaky symptoms during early stages and lack of reliable predictors of disease progression. METHODS: Bioinformatics and machine learning algorithms were initially used to explore the potential biomarkers for LN development. Identified biomarker expression was evaluated by immunohistochemistry (IHC) and multiplex immunofluorescence (IF) in 104 LN patients, 12 diabetic kidney disease (DKD) patients, 12 minimal change disease (MCD) patients, 12 IgA nephropathy (IgAN) patients and 14 normal controls (NC). The association of biomarker expression with clinicopathologic indices and prognosis was analyzed. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were utilized to explore potential mechanisms. RESULTS: Interferon-inducible protein 16 (IFI16) was identified as a potential biomarker for LN. IFI16 was highly expressed in the kidneys of LN patients compared to those with MCD, DKD, IgAN or NC. IFI16 co-localized with certain renal and inflammatory cells. Glomerular IFI16 expression was correlated with pathological activity indices of LN, while tubulointerstitial IFI16 expression was correlated with pathological chronicity indices. Renal IFI16 expression was positively associated with systemic lupus erythematosus disease activity index (SLEDAI) and serum creatinine while negatively related to baseline eGFR and serum complement C3. Additionally, higher IFI16 expression was closely related to poorer prognosis of LN patients. GSEA and GSVA suggested that IFI16 expression was involved in adaptive immune-related processes of LN. CONCLUSION: Renal IFI16 expression is a potential biomarker for disease activity and clinical prognosis in LN patients. Renal IFI16 levels may be used to shed light on predicting the renal response and develop precise therapy for LN.

Renal C4d is a potential biomarker of disease activity and severity in pediatric lupus nephritis patients.

Background: Systemic lupus erythematosus (SLE), a multisystemic autoimmune disease, is very aggressive in pediatric-onset patients as they are prone to develop lupus nephritis (LN). Although renal C4d positivity is correlated with the activity of renal disease and SLE in adult-onset LN patients, available information for pediatric-onset patients is limited. Methods: To evaluate the potential diagnostic significance of renal C4d staining in pediatric LN patients, we retrospectively detected C4d staining by immunohistochemistry on renal biopsy specimens from 58 pediatric LN patients. The clinical and laboratory data at the time of the kidney biopsy and the renal disease activity of histological injury were analyzed according to the C4d staining status. Results: Glomerular C4d (G-C4d)-positive staining was detected in all 58 cases of LN. Patients with a G-C4d score of 2 displayed more severe proteinuria than those with a G-C4d score of 1 (24-h urinary protein: 3.40 ± 3.55 g vs. 1.36 ± 1.24 g, P < 0.05). Peritubular capillary C4d (PTC-C4d) positivity was found in 34 of 58 LN patients (58.62%). The PTC-C4d-positive patient groups (patients with a PTC-C4d score of 1 or 2) had higher serum creatinine and blood urea nitrogen levels as well as renal pathological activity index (AI) and SLE disease activity index (SLEDAI) scores; however, they had lower serum complement C3 and C4 levels compared to PTC-C4d-negative patients (P < 0.05). In addition, there was positive tubular basement membrane C4d (TBM-C4d) staining in 11 of 58 LN patients (18.96%), and a higher proportion of TBM-C4d-positive patients than TBM-C4d-negative patients (63.63% vs. 21.27%) had hypertension. Conclusion: Our study revealed that G-C4d, PTC-C4d, and TMB-C4d were positively correlated with proteinuria, disease activity and severity, and hypertension, respectively, in pediatric LN patients. These data suggest that renal C4d is a potential biomarker for disease activity and severity in pediatric LN patients, providing insights into the development of novel identification and therapeutic approaches for pediatric-onset SLE with LN.

Effects of Selenium on Chronic Kidney Disease: A Mendelian Randomization Study.

BACKGROUND: Previous observational studies have shown that there is a controversial association between selenium levels and chronic kidney disease (CKD). Our aim was to assess the causal relationship between selenium levels and CKD using Mendelian randomization (MR) analysis. METHODS: We used the two-sample Mendelian randomization (MR) method to analyze the causal role of selenium levels on CKD risk. The variants associated with selenium levels were extracted from a large genome-wide association study (GWAS) meta-analysis of circulating selenium levels (n = 5477) and toenail selenium levels (n = 4162) in the European population. Outcome data were from the largest GWAS meta-analysis of European-ancestry participants for kidney function to date. Inverse variance weighted (IVW) method was used as the main analysis and a series of sensitivity analyses were carried out to detect potential violations of MR assumptions. RESULTS: The MR analysis results indicate that the genetically predicted selenium levels were associated with decreased estimated glomerular filtration (eGFR) (effect = -0.0042, 95% confidence interval [CI]: -0.0053-0.0031, p = 2.186 × 10-13) and increased blood urea nitrogen (BUN) (effect = 0.0029, 95% confidence interval [CI]: 0.0006-0.0052, p = 0.0136) with no pleiotropy detected. CONCLUSIONS: The MR study indicated that an increased level of selenium is a causative factor for kidney function impairment.

Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.

Objective: Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration. Materials and methods: Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated. Results: A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents. Conclusion: DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.

Network pharmacology and molecular docking technology-based predictive study of the active ingredients and potential targets of rhubarb for the treatment of diabetic nephropathy.

Diabetic nephropathy (DN) is one of the most serious complications of diabetes and the main cause of end-stage renal failure. Rhubarb is a widely used traditional Chinese herb, and it has exhibited efficacy in reducing proteinuria, lowering blood sugar levels and improving kidney function in patients with DN. However, the exact pharmacological mechanism by rhubarb improves DN remain unclear due to the complexity of its ingredients. Hence, we systematically explored the underlying mechanisms of rhubarb in the treatment of DN. We adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment. Molecular docking technology was used to verify the binding ability between the main active compounds and central therapeutic targets, and screen out the core active ingredients in rhubarb for the treatment of DN. Finally, molecular dynamics simulation was performed for the optimal core protein-ligand obtained by molecular docking using GROMACS software. The network analysis identified 16 active compounds in rhubarb that were linked to 37 possible therapeutic targets related to DN. Through protein-protein interaction analysis, TP53, CASP8, CASP3, MYC, JUN and PTGS2 were identified as the key therapeutic targets. By validation of molecular docking, finding that the central therapeutic targets have good affinities with the main active compounds of rhubarb, and rhein, beta-sitosterol and aloe-emodin were identified as the core active ingredients in rhubarb for the treatment of DN. Results from molecular dynamics simulations showed that TP53 and aloe-emodin bound very stably with a binding free energy of - 26.98 kcal/mol between the two. The results of the gene enrichment analysis revealed that the PI3K-Akt signalling pathway, p53 signalling pathway, AGE-RAGE signalling pathway and MAPK signalling pathway might be the key pathways for the treatment of DN, and these pathways were involved in podocyte apoptosis, glomerular mesangial cell proliferation, inflammation and renal fibrosis. Based on the network pharmacology approach and molecular docking technology, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effects of rhubarb against DN. These findings provided an important scientific basis for further research of the mechanism of rhubarb in the treatment of DN.

The microbial diversity in industrial effluents makes high-throughput sequencing-based source tracking of the effluents possible.

In order to explore the microbial diversity in industrial effluents, and on this basis, to verify the feasibility of tracking industrial effluents in sewer networks based on sequencing data, we collected 28 sewage samples from the industrial effluents relative to four factories in Shenzhen, China, and sequenced the 16S rRNA genes to profile the microbial compositions. We identified 5413 operational taxonomic units (OTUs) in total, and found that microbial compositions were highly diverse among samples from different locations in the sewer system, with only 107 OTUs shared by 90% of the samples. These shared OTUs were enriched in the phylum of Proteobacteria, the families of Comamonadaceae and Pseudomonadaceae, as well as the genus of Pseudomonas, with both degradation related and pathogenic bacteria. More importantly, we found differences in microbial composition among samples relevant to different factories, and identified microbial markers differentiating effluents from these factories, which can be used to track the sources of the effluents. This study improved our understanding of microbial diversity in industrial effluents, proved the feasibility of industrial effluent source tracking based on sequencing data, and provided an alternative technique solution for environmental surveillance and management.

Zinc-Ion Storage Mechanism of Polyaniline for Rechargeable Aqueous Zinc-Ion Batteries.

Aqueous multivalent ion batteries, especially aqueous zinc-ion batteries (ZIBs), have promising energy storage application due to their unique merits of safety, high ionic conductivity, and high gravimetric energy density. To improve their electrochemical performance, polyaniline (PANI) is often chosen to suppress cathode dissolution. Herein, this work focuses on the zinc ion storage behavior of a PANI cathode. The energy storage mechanism of PANI is associated with four types of protonated/non-protonated amine or imine. The PANI cathode achieves a high capacity of 74 mAh g-1 at 0.3 A g-1 and maintains 48.4% of its initial discharge capacity after 1000 cycles. It also demonstrates an ultrahigh diffusion coefficient of 6.25 × 10-9~7.82 × 10-8 cm-2 s-1 during discharging and 7.69 × 10-10~1.81 × 10-7 cm-2 s-1 during charging processes, which is one or two orders of magnitude higher than other reported studies. This work sheds a light on developing PANI-composited cathodes in rechargeable aqueous ZIBs energy storage devices.

A noninvasive artificial neural network model to predict IgA nephropathy risk in Chinese population.

Renal biopsy is the gold standard for Immunoglobulin A nephropathy (IgAN) but poses several problems. Thus, we aimed to establish a noninvasive model for predicting the risk probability of IgAN by analyzing routine and serological parameters. A total of 519 biopsy-diagnosed IgAN and 211 non-IgAN patients were recruited retrospectively. Artificial neural networks and logistic modeling were used. The receiver operating characteristic (ROC) curve and performance characteristics were determined to compare the diagnostic value between the two models. The training and validation sets did not differ significantly in terms of any variables. There were 19 significantly different parameters between the IgAN and non-IgAN groups. After multivariable logistic regression analysis, age, serum albumin, serum IgA, serum immunoglobulin G, estimated glomerular filtration rate, serum IgA/C3 ratio, and hematuria were found to be independently associated with the presence of IgAN. A backpropagation network model based on the above parameters was constructed and applied to the validation cohorts, revealing a sensitivity of 82.68% and a specificity of 84.78%. The area under the ROC curve for this model was higher than that for logistic regression model (0.881 vs. 0.839). The artificial neural network model based on routine markers can be a valuable noninvasive tool for predicting IgAN in screening practice.

Investigate the genetic mechanisms of diabetic kidney disease complicated with inflammatory bowel disease through data mining and bioinformatic analysis.

Background: Patients with diabetic kidney disease (DKD) often have gastrointestinal dysfunction such as inflammatory bowel disease (IBD). This study aims to investigate the genetic mechanism leading to IBD in DKD patients through data mining and bioinformatics analysis. Methods: The disease-related genes of DKD and IBD were searched from the five databases of OMIM, GeneCards, PharmGkb, TTD, and DrugBank, and the intersection part of the two diseases were taken to obtain the risk genes of DKD complicated with IBD. A protein-protein interaction (PPI) network analysis was performed on risk genes, and three topological parameters of degree, betweenness, and closeness of nodes in the network were used to identify key risk genes. Finally, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed on the risk genes to explore the related mechanism of DKD merging IBD. Results: This study identified 495 risk genes for DKD complicated with IBD. After constructing a protein-protein interaction network and screening for three times, six key risk genes were obtained, including matrix metalloproteinase 2 (MMP2), hepatocyte growth factor (HGF), fibroblast growth factor 2 (FGF2), interleukin (IL)-18, IL-13, and C-C motif chemokine ligand 5 (CCL5). Based on GO enrichment analysis, we found that DKD genes complicated with IBD were associated with 3,646 biological processes such as inflammatory response regulation, 121 cellular components such as cytoplasmic vesicles, and 276 molecular functions such as G-protein-coupled receptor binding. Based on KEGG enrichment analysis, we found that the risk genes of DKD combined with IBD were associated with 181 pathways, such as the PI3K-Akt signaling pathway, advanced glycation end product-receptor for AGE (AGE-RAGE) signaling pathway and hypoxia-inducible factor (HIF)-1 signaling pathway. Conclusion: There is a genetic mechanism for the complication of IBD in patients with CKD. Oxidative stress, chronic inflammatory response, and immune dysfunction were possible mechanisms for DKD complicated with IBD.

Association of abnormal-glucose tolerance during pregnancy with exposure to PM2.5 components and sources.

Maternal exposure to PM2.5 has been associated with abnormal glucose tolerance during pregnancy, but little is known about which constituents and sources are most relevant to glycemic effects. We conducted a retrospective cohort study of 1148 pregnant women to investigate associations of PM2.5 chemical components with gestational diabetes mellitus (GDM) and impaired glucose tolerance (IGT) and to identify the most harmful sources in Heshan, China from January 2015 to July 2016. We measured PM2.5 using filter-based method and analyzed them for 28 constituents, including carbonaceous species, water-soluble ions and metal elements. Contributions of PM2.5 sources were assessed by positive matrix factorization (PMF). Logistic regression model was used to estimate composition-specific and source-specific effects on GDM/IGT. Random forest algorithm was applied to evaluate the relative importance of components to GDM and IGT. PM2.5 total mass and several chemical constituents were associated with GDM and IGT across the early to mid-gestation periods, as were the PM2.5 sources fossil fuel/oil combustion, road dust, metal smelting, construction dust, electronic waster, vehicular emissions and industrial emissions. The trimester-specific associations differed among pollutants and sources. The third and highest quartile of elemental carbon, ammonium (NH4+), iron (Fe) and manganese (Mn) across gestation were consistently associated with higher odds of GDM/IGT. Maternal exposures to zinc (Zn), titanium (Ti) and vehicular emissions during the first trimester, and vanadium (V), nickel (Ni), road dust and fossil fuel/oil combustion during the second trimester were more important for GDM/IGT. This study provides important new evidence that maternal exposure to PM2.5 components and sources is significantly related to elevated risk for abnormal glucose tolerance during pregnancy.

Insights into the source-specific health risk of ambient particle-bound metals in the Pearl River Delta region, China.

Quantification of source-specific health risks of PM2.5 plays an essential role in health-oriented air pollution control. However, there is limited evidence supporting the source-based risk apportionment of particle-bound metals. In this study, source-specific cancer and non-cancer risk characterization of 12 particle-bound metals was performed in the Pearl River Delta (PRD) region, China. A combination of health risk assessment model and receptor-based source apportionment modeling with positive matrix factorization (PMF) was applied for characterizing the spatial-temporal patterns for inhalation health risks of particle-bound metals in three main city clusters, inland area and coastal area in the region from December 2014 through July 2016. Results showed that the carcinogenic risk of particle-bound metals for adults (4.13 × 10-5) was higher than that for children (9.53 × 10-6) in the PRD region. The highest and significant non-carcinogenic risk was found in the northwest city cluster. Industrial emission (63.3%) were the dominant contributors to the cancer risk, while the main contributors to the non-cancer risk were the vehicle emission source (33.2%) in the dry season and industrial emission (30.8%) in the wet season. Our results provide important evidence for spatial source-specific health risks with temporal characteristics of particle-bound metals in most densely populated areas in the southern China, and suggest that reduction of industrial and vehicle emissions could facilitate more cost-effective PM2.5 control measures to improve human health.

Predictive Study of the Active Ingredients and Potential Targets of Codonopsis pilosula for the Treatment of Osteosarcoma via Network Pharmacology.

Osteosarcoma (OS) is the most common type of primary bone tumor in children and adults. Dangshen (Codonopsis pilosula) is a traditional Chinese medicine commonly used in the treatment of OS worldwide. However, the molecular mechanisms of Dangshen in OS remain unclear. Hence, in this study, we aimed to systematically explore the underlying mechanisms of Dangshen in the treatment of OS. Our study adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, gene ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and other network tools. The network analysis identified 15 active compounds in Dangshen that were linked to 48 possible therapeutic targets related to OS. The results of the gene enrichment analysis show that Dangshen produces a therapeutic effect in OS likely by regulating multiple pathways associated with DNA damage, cell proliferation, apoptosis, invasion, and migration. Based on the network pharmacology approach, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effect of Dangshen in OS. These findings may aid in the development of novel targeted therapies for OS in the future.

Association of ambient particle pollution with gestational diabetes mellitus and fasting blood glucose levels in pregnant women from two Chinese birth cohorts.

BACKGROUND: Fasting blood glucose may capture the adverse effects of air pollution on pregnant women better than the incidence of gestational diabetes mellitus (GDM), but evidence on the association between air pollution and maternal glucose concentrations is limited. OBJECTIVE: To investigate the associations between air pollutants, GDM and fasting blood glucose during pregnancy. METHODS: We recruited 2326 pregnant women from two birth cohorts located in Guangzhou and Heshan, the Pearl River Delta region (PRD), China. PM10, PM2.5 and black carbon (BC) exposure concentrations in the first and second trimesters of pregnancy were collected at fixed-site monitoring stations for each cohort. Multiple logistic regressions were employed to estimate the associations between particle pollution and GDM. Mixed-effects models were used to evaluate the associations of air pollutants with blood glucose levels. Restricted cubic spline functions were fitted to visualize the concentration-response relationships. Distributed lag non-linear models were used to estimate week-specific lag effects of particle pollution exposure on GDM and blood glucose. Unconstrained distributed lag models with lags of 0-3 weeks were used to examine potential cumulative effects. RESULTS: We observed positive and significant associations of PM10, PM2.5 and BC exposure with fasting glucose, particularly in the second trimester. PM10, PM2.5 and BC were strongly correlated and displayed similar cumulative (lag 0-3 weeks) associations with fasting blood glucose. Exposure to particle pollution was not associated with 1-h or 2-h blood glucose. Models estimating the association between air pollutants and GDM were consistent with statistical insignificance. CONCLUSIONS: Based on the results of the present study, exposure to air pollution during pregnancy exerts cumulative, adverse effects on fasting glucose levels. This study provides preliminary support for the use of blood glucose levels to explore the potential health impact of air pollution on pregnant women.

The Potential Significance of ABO Genotyping for Donor Selection in Kidney Transplantation.

Background: The ABO blood group system is clinically important in kidney transplantation, but ABO genotyping fails to attract sufficient attention in some countries and regions. We identified one case of early graft dysfunction due to an ABO genotype mismatch. Here, we performed ABO genotyping in blood samples, analyzed grouping discrepancies, and investigated the weak A subgroup frequency in kidney transplantation candidates. Methods: Blood samples from 302 uremic patients with grouping discrepancies and 356 uremic patients with type A blood were analyzed using standard serologic serotyping techniques. The ABO genotypes and alleles were analyzed by polymerase chain reaction sequence-specific primer (PCR-SSP) and sequence-based typing (PCR-SBT). Results: All 302 uremic patients with grouping discrepancies carried weak ABO subgroup alleles and 77.48% carried irregular ABO antibodies. The discrepancy rate between serotyping and genotyping was 42.38%, and the mismatching rate of donor selection according to serotype reached 88.74%. And 2.53% of 356 uremic patients with type A blood were determined to be in the weak A subgroup, which was a higher percentage than that observed in the healthy Chinese population (0.53%) by serological screening, but much lower than that observed in Caucasians (20%). Conclusion: We revealed the high risk of blood type misjudgment and genetically ABO-mismatched transplantation if serological test was performed only in blood-group typing. Improved precision of ABO genotyping is crucial for successful kidney transplantation and reasonable organ allocation.