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Manipulation of flowering time to develop cultivars with desired maturity dates is fundamental in plant breeding. It is desirable to generate polyploid rapeseed (Brassica napus L.) germplasm with varying flowering time controlled by a few genes. In the present study, BnaSVP, a rapeseed homolog of the Arabidopsis SVP (Short Vegetative Phase) gene, was characterized and a set of mutants was developed using a CRISPR/Cas9-based gene-editing tool. A single construct targeting multiple sites was successfully applied to precisely mutate four copies of BnaSVP. The induced mutations in these copies were stably transmitted to subsequent generations. Homozygous mutants with loss-of-function alleles and free transgenic elements were generated across the four BnaSVP homologs. All mutant T1 lines tested in two environments (summer and winter growing seasons) showed early-flowering phenotypes. The decrease in flowering time was correlated with the number of mutated BnaSVP alleles. The quadruple mutants showed the shortest flowering time, with a mean decrease of 40.6%–50.7% in length relative to the wild type under the two growth conditions. Our study demonstrates the quantitative involvement of BnaSVP copies in the regulation of flowering time and provides valuable resources for rapeseed breeding.

BackgroundThe ongoing coronavirus disease 2019 (COVID-19) pandemic has forced the development of vaccines. Reports have suggested that vaccines play a role in inducing autoimmune diseases (AIDs). Scattered cases have reported that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines may promote thyroid disease, including Graves’ disease (GD). However, the effect of inactivated SARS-CoV-2 vaccine on GD remains unclear. The aim of the present study was to investigate the response of thyrotropin receptor antibody (TRAB) to inactivated SARS-COV-2 vaccines.MethodsWe conducted a retrospective study to observe the differences in thyroid function and TRAB trends between pre-vaccination (n=412) and post-vaccination (n=231) groups at an interval of 2 months. We then retrospectively observed the differences in serum thyroid function and TRAB levels at 3 months before (n=280), 1 month before (n=294), 1 month after (n=306), and 3 months after (n=250) vaccination. Subsequently, 173 GD patients who were not vaccinated with inactivated SARS-COV-2 vaccines were selected for a prospective study. Thyroid function and TRAB assessment were performed before 3 and 1 months and 1 and 3 months after the first dose of vaccination and were then compared by repeated measures ANOVA to explore their dynamic changes.ResultsA retrospective study preliminarily observed that the trend of TRAB post-vaccination was opposite of that pre-vaccination (p=0.000), serum TRAB levels decreased before vaccination and increased after vaccination. In this prospective study, repeated measures ANOVA indicated significant differences in serum FT3 (p=0.000), FT4 (p=0.000), TSH (p=0.000), and TRAB (p=0.000) levels at different time points before and after vaccination. Serum TRAB levels showed dynamic changes that decreased significantly at 1 month before vaccination (p=0.000), no significant differences at 1 month after vaccination (p=0.583), and reflected an upward trend at 3 months after vaccination (p=0.034). Serum FT3 and FT4 levels showed similar trends to serum TRAB levels before and after vaccination. Instead, the serum TSH levels showed a continuous upward trend over time.ConclusionBased on the results obtained in both retrospective and prospective studies, we concluded that serum TRAB levels decreased less after inactivated SARS-CoV-2 vaccination and showed an upward trend, which may be related to humoral immunity induced by vaccination.

Objectives The prevalence of gestational diabetes mellitus (GDM) has increased year-after-year globally, especially in low-income and developing countries. This study aims to identify the prevalence of GDM, the risk factors, and the effect on pregnancy outcome based on a retrospective case-control study. Methods Two hundred ninety-three parturients with GDM who delivered in a general hospital in Fujian province and met the inclusion criteria were selected as the case group from January to June 2018. Two hundred ninety-three parturients without GDM who delivered in the same period served as the control group. Risk factors for GDM were determined by univariate and binary logistic regression analysis. The prevalence of pregnancy outcomes was determined by a chi-square test. Results The prevalence of GDM was 15.69%. The percentages of 1, 2, and 3 abnormal OGTT values were 55.6%, 30.7%, and 13.7%, respectively. Gravidas with GDM have a higher risk of macrosomia, polyhydramnios, pre-eclampsia, placenta previa, and gestational hypertension than gravidas without GDM (p < 0.05). Analysis of the factors influencing the development of GDM was advanced age, married, parents with a history of diabetes, gestational hypertension, and number of abortions. Conclusions The prevalence of GDM was 15.69% in this geographic region, and > 50% of the patients had one abnormal OGTT value. The risk factors for GDM were advanced age, parents with diabetes, gestational hypertension, and the number of abortions. Pregnancy outcomes of the two groups of patients were different with respect to macrosomia, polyhydramnios, pre-eclampsia, placenta previa, and hypertensive disorders of pregnancy.

Proteomics originated from 2-dimensional electrophoresis more than thirty years ago. Technological advances have made proteomics one of the most flourishing areas of modern biotechnology research. Proteomics mainly characterizes protein functions, protein–protein interactions, and protein modification in cells, tissues or animals. The integration of proteomic data helps to screen bioactive compounds, biomarkers of diseases, or signaling pathways in cells or the whole body. Natural products are valuable resources that contain a variety of bioactive compounds. However, the mechanisms of action of many natural products and bioactive compounds are unclear. The identification of the target proteins of discovered biologically active natural products to understand their mechanisms is a critical hurdle for their development into clinical drugs. This review describes the application of proteomics to determine the mechanisms of natural products, including compounds, peptides and mixtures in the recent years. This article summarizes progress on antimicrobials, antineoplastics, antiparasitics, nervous system drugs, antivirals and cardiovascular disease drugs.Proteomics originated from 2-dimensional electrophoresis more than thirty years ago. Technological advances have made proteomics one of the most flourishing areas of modern biotechnology research. Proteomics mainly characterizes protein functions, protein–protein interactions, and protein modification in cells, tissues or animals. The integration of proteomic data helps to screen bioactive compounds, biomarkers of diseases, or signaling pathways in cells or the whole body. Natural products are valuable resources that contain a variety of bioactive compounds. However, the mechanisms of action of many natural products and bioactive compounds are unclear. The identification of the target proteins of discovered biologically active natural products to understand their mechanisms is a critical hurdle for their development into clinical drugs. This review describes the application of proteomics to determine the mechanisms of natural products, including compounds, peptides and mixtures in the recent years. This article summarizes progress on antimicrobials, antineoplastics, antiparasitics, nervous system drugs, antivirals and cardiovascular disease drugs., {"references":["1.\tChang J, Kwon HJ. Discovery of novel drug targets and their functions using phenotypic screening of natural products. J Ind Microbiol Biotechnol. 2016 Mar;43(2-3):221-31. doi: 10.1007/s10295-015-1681-y.","2.\tDouglas Kinghorn, Young-Won Chin, and Steven M. Swanson .Discovery of Natural Product Anticancer Agents from Biodiverse Organisms. Curr Opin Drug Discov Devel. 2009 Mar; 12(2): 189–196.","3.\tIrkin R, Esmer OK. 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ObjectiveMicroalbuminuria (MAU) occurs due to universal endothelial damage, which is strongly associated with kidney disease, stroke, myocardial infarction, and coronary artery disease. Screening patients at high risk for MAU may aid in the early identification of individuals with an increased risk of cardiovascular events and mortality. Hence, the present study aimed to establish a risk model for MAU by applying machine learning algorithms.MethodsThis cross-sectional study included 3,294 participants ranging in age from 16 to 93 years. R software was used to analyze missing values and to perform multiple imputation. The observed population was divided into a training set and a validation set according to a ratio of 7:3. The first risk model was constructed using the prepared data, following which variables with P P ≥ 0.05 was considered to indicate no difference in the fit of the models. Variables with P ResultsSystolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), triglyceride (TG) levels, sex, age, and smoking were identified as predictors of MAU prevalence. Verification using a chi-square test, confusion matrix, and calibration curve indicated that the risk of MAU could be predicted based on the risk score.ConclusionBased on the ability of our machine learning algorithm to establish an effective risk score, we propose that comprehensive assessments of SBP, DBP, FBG, TG, gender, age, and smoking should be included in the screening process for MAU.

Additional file 1: Figure S1. Cochrane risk of bias. Figure S2. The forest plot.

To investigate the effects of a structured moderate-intensity resistance exercise program on blood glucose levels and other health-related indicators in patients with GDM.A total of 99 patients with GDM in a tertiary class A general hospital were randomly divided into an experimental group and a control group. GDM patients in the control group received routine prenatal care, online education, and a personalized diabetes diet intervention. The experimental group was treated in the same way as the control group with the addition of a moderate intensity resistance exercise program.The blood glucose levels in both groups were lower after the intervention compared with before intervention (P 0.05). After intervention, the average fasting blood glucose, the 2 h postprandial blood glucose, the utilization rate of insulin, the amount of insulin, gestational weight gain and blood pressure (P 0.05) in the experimental group were lower than the control group. In addition, there was no statistical significance in the incidence of adverse pregnancy outcomes between the two groups after intervention (P 0.05).Moderate intensity resistance exercise was helpful for improvement of blood glucose control and insulin use, gestational weight gain and blood pressure in patients with GDM. In the future, long-term follow-up of both maternal and newborn infants should be performed to assess the long-term effects of exercise intervention on maternal and child health. The impact on the risk of obesity and diabetes may need to be further clarified. The trial was approved by the registration of the Chinese Clinical Trial Registry, and registration number: ChiCTR1900027929.