The integrated bioinformatic analysis identifies immune microenvironment-related potential biomarkers for patients with gestational diabetes mellitus.

Background: Gestational diabetes mellitus (GDM), a transient disease, may lead to short- or long-term adverse influences on maternal and fetal health. Therefore, its potential functions, mechanisms and related molecular biomarkers must be comprehended for the control, diagnosis and treatment of GDM. Methods: The differentially expressed genes (DEGs) were identified using GSE49524 and GSE87295 associated with GDM from the Gene Expression Omnibus database, followed by function enrichment analysis, protein-protein interactions network construction, hub DEGs mining, diagnostic value evaluation and immune infiltration analysis. Finally, hub DEGs, the strongest related to immune infiltration, were screened as immune-related biomarkers. Results: A hundred and seven DEGs were identified between patients with GDM and healthy individuals. Six hub genes with high diagnostic values, including ALDH1A1, BMP4, EFNB2, MME, PLAUR and SLIT2, were identified. Among these, two immune-related genes (PLAUR and SLIT2) with the highest absolute correlation coefficient were considered immune-related biomarkers in GDM. Conclusion: Our study provides a comprehensive analysis of GDM, which would provide a foundation for the development of diagnosis and treatment of GDM.

Genetic Mapping of the Leaf Number above the Primary Ear and Its Relationship with Plant Height and Flowering Time in Maize.

The leaf number above the primary ear (LA) is a major contributing factor to plant architecture in maize. The yield of leafy maize, which has extra LA compared to normal maize, is higher than normal maize in some regions. One major concern is that increasing LA may be accompanied by increased plant height and/or flowering time. Using an F2:3 population comprising 192 families derived from a leafy maize line and a normal maize line, an association population comprising 437 inbred maize lines, and a pair of near-isogenic maize lines, we mapped the quantitative trait loci (QTL) associated with LA and assessed its genetic relationship with flowering time and plant height. Ten QTL with an additive and dominant effect, 18 pairs of interacting QTL in the F2:3 population and seventeen significant SNPs in the association population were detected for LA. Two major QTL, qLA3-4 and qLA7-1, were repeatedly detected and explained a large proportion of the phenotypic variation. The qLA3-4 was centered on lfy1, which is a dominant gene underlying extra leaves above the ear in leafy maize. Four LA QTL were found to overlap with flowering time and/or plant height, which suggested that these QTL might have a pleiotropic effect. The pleiotropy of the lfy1 locus on LA, flowering time and plant height were validated by near-isogenic line analysis. These results enhance our understanding of the genetic architecture affecting maize LA and the development of maize hybrids with increased LA.

Confirmation and Fine Mapping of a Major QTL for Aflatoxin Resistance in Maize Using a Combination of Linkage and Association Mapping.

Maize grain contamination with aflatoxin from Aspergillus flavus (A. flavus) is a serious health hazard to animals and humans. To map the quantitative trait loci (QTLs) associated with resistance to A. flavus, we employed a powerful approach that differs from previous methods in one important way: it combines the advantages of the genome-wide association analysis (GWAS) and traditional linkage mapping analysis. Linkage mapping was performed using 228 recombinant inbred lines (RILs), and a highly significant QTL that affected aflatoxin accumulation, qAA8, was mapped. This QTL spanned approximately 7 centi-Morgan (cM) on chromosome 8. The confidence interval was too large for positional cloning of the causal gene. To refine this QTL, GWAS was performed with 558,629 single nucleotide polymorphisms (SNPs) in an association population comprising 437 maize inbred lines. Twenty-five significantly associated SNPs were identified, most of which co-localised with qAA8 and explained 6.7% to 26.8% of the phenotypic variation observed. Based on the rapid linkage disequilibrium (LD) and the high density of SNPs in the association population, qAA8 was further localised to a smaller genomic region of approximately 1500 bp. A high-resolution map of the qAA8 region will be useful towards a marker-assisted selection (MAS) of A. flavus resistance and a characterisation of the causal gene.

Differential effects of ketoconazole, itraconazole and voriconazole on the pharmacokinetics of imatinib and its main metabolite GCP74588 in rat.

The aim of the study was to develop a high performance liquid chromatography method for simultaneous determination of imatinib and CGP74588 in rat serum and study the inhibition effects of ketoconazole, itraconazole and voriconazole on pharmacokinetics of imatinib and CGP74588 in rats. In our study, we found that ketoconazole caused a significant increase (63.4%) in the AUC of imainib and a 28.8% increase in Cmax, which was greater than that of itraconazole but lower than that of voriconazole. When co-administered with voriconazole, pharmacokinetic parameters of imatinib were not significantly altered except for a 36.8% increase in the Cmax of imtinib. The Cmax of CGP74588 was decreased by 55.8% and AUC(0-∞) 49.7%, while the Vz/F and CLz/F values were increased by 1.7-fold and 1.1-fold, respectively. Itraconazole did not significantly influence the pharmacokinetic parameters of imatinib and CGP74588. The difference may be related to the different variation of inhibition sites of the three azole antifungal agents on CYP3A4 and P-gp. In clinical, when imatinib was co-administrated with ketoconazole or voriconazole, dose adjustment of imatinib should be taken into account.

[Toxicokinetics of paraquat in rabbits].

OBJECTIVE: to develop a high performance liquid chromatography method (HPLC) for the determination of paraquat in rabbit plasma and study its toxicokinetics in rabbits. METHODS: twelve rabbits were randomly divided into 2 groups with giving oral and intravenous administration of paraquat at a single dose of 60 mg/kg and 6 mg/kg respectively. The plasma paraquat concentrations were determined by HPLC and calculated by DAS pharmacokinetics program. RESULTS: the linear range of paraquat in plasma was 0.05 ∼ 50.00 mg/L (r = 0.9998). The relative recoveries of the assay were 99.41% ∼ 102.32%. The absolute recoveries of the assay were 83.72% ∼ 90.48%. Both the intra-day and inter-day validations were less than 10%. For oral administration, the toxicokinetics parameters of paraquat were as follows: Cmax (14.46 ± 2.35) mg/L, Tmax (1.63 ± 0.31) h, AUC(0-t) (177.61 ± 14.62) mg × h/L, AUC(0-∞) (182.24 ± 14.54) mg × h/L, While for intravenous administration, the toxicokinetics parameters of paraquat: Cmax (35.13 ± 5.53) mg/L, Tmax 0.05 h, AUC(0-t) (121.74 ± 12.30) mg × h/L, AUC(0-∞) (125.12 ± 12.17) mg × h/L, The difference of these parameters between the two groups had statistical significance (P < 0.05). The oral bioavailability was (14.66 ± 1.55)%. CONCLUSION: the oral bioavailability of paraquat is relatively low. The biological half life of paraquat is relatively long and there is no significant difference between oral administration and intravenous on biological half life. This method is simple, sensitive and accurate. It can be used for the investigation of paraquat in rabbits.