Effects of Traditional Chinese Medicinal Plants on Antiinsulin Resistance Bioactivity of DXMS-Induced Insulin Resistant HepG2 Cells

Medicinal plants have a long history of use in China to treat diabetic symptoms.Ancient Chinese medical manuscripts and ethnobotanical surveys document plant remedies that continue to be actively used in China for the treatment of diabetic symptoms.Based on a systematic ancient Chinese medical manuscripts review in combination with ethnobotanical survey,16 medicinal plants for the traditional treatment of diabetic symptoms were identified for the evaluation of anti-insulin resistance bioactivity.The biological activity of 16 medicinal plants was tested on dexamethasone(DXMS)-induced insulin resistant HepG2 cells.The result shows that 11 of the 16 medicinal plants enhanced glucose uptake of DXMS-induced insulin resistant HepG2 cells,thereby demonstrating their ability to increase insulin sensitivity,other five medicinal plants including Astragalus membranaceus were found ineffective.The study shows that ancient Chinese medical manuscripts and ethnobotanical surveys on plants for the prevention and treatment of diabetic symptoms provide a promising knowledge base for drug discovery to mitigate the global diabetes epidemic.

Hypercohones A-C,acylphloroglucinol derivatives with homo-adamantane cores from Hypericum cohaerens

Three new homo-adamantanyl type natural products were derived from polyprenylated polycyclic acylphloroglucinol.Hypercohones A-C(1-3),along with five other known hypercohones(4-8),were isolated from the aerial parts of Hypericum cohaerens.The structures of 1-3 were elucidated on the basis of comprehensive spectroscopic analysis.The inhibitory activities of these isolates against five human cancer cell lines in vitro were tested.

neo-Clerodane diterpenoids from Salvia dugesii and their bioactive studies

Salvia dugesii is an invasive plant in Yunnan,China.To tentatively explore its utilization,a systematic phytochemical investigation was carried out on this plant,which led to the isolation of five new neo-clerodane diterpenoids,dugesins C-G(1-5),together with six known ones.Their structures were determined by comprehensive NMR and MS spectroscopic analysis.It was noteworthy that the eleven isolates,composed of five different carbocyclic systems derived from the neo-clerodane diterpenoid skeleton,were reported from the same plant for the first time.The anti-feedantial,cytotoxic,and antiviral activities of the isolates were evaluated.Dugesin F(4)was tested to be a non-toxic antiviral compound against influenza virus FM1.

Hypercohones D–G,New Polycyclic Polyprenylated Acylphloroglucinol Type Natural Products from Hypericum cohaerens

Four new polycyclic polyprenylated acylphloroglucinol type metabolites,hypercohones D–G(1–4),along with four known analogues(5–8),were isolated from the aerial parts of Hypericum cohaerens.The structures of these isolates were elucidated by extensive spectroscopic methods.The inhibitory activities of these isolates against five human cancer cell lines in vitro were also tested.

New Dimeric and seco-Abietane Diterpenoids from Salvia wardii

Two dimeric abietane diterpenoids,salviwardins A and B(1 and 2),and a seco-abietane diterpenoid salviwardin C(3),along with five known analogues(4–8),were isolated from the roots of Salvia wardii.The structures of these isolates were elucidated by extensive spectroscopic methods.The inhibitory activities of these isolates against five human cancer cell lines in vitro were also tested.

Erratum to:New Dimeric and seco-Abietane Diterpenoids from Salvia wardii

Erratum to:Nat.Prod.Bioprospect.(2015)5:77–82 DOI 10.1007/s13659-015-0054-6 In the HTML version of the original publication,the graphical abstract was inadvertently omitted.The graphical abstract is given in this erratum.

Risk factors and prediction of macrosomia in a cohort study

Objective: To investigate the risk factors of macrosomia and to predict the risk of macrosomia so as to reduce the incidence of macrosomia. Methods: A total of 2063 pregnant women who met the inclusion criteria were selected as the subjects from February 2016 to April 2017 in Jiading District Maternal and Child Health Hospital of Shanghai and Pudong New Area Maternal and Child Health Hospital. According to the birth weight of the neonates, the neonates were divided into the macrosomia group (neonatal weight > 4000 g, n=125) and the normal infant group (2500 g < neonatal weight < 4000 g, n=1938).The general data of age, number of pregnant women, BMI before pregnancy, gestational diabetes mellitus, glucose tolerance, weight gain during pregnancy, birth weight and gestational week were compared between the two groups. Multivariate logistic regression was used to analyze the risk factors of macrosomia. Results: ①There were significant differences in BMI, glucose tolerance, fasting blood sugar, weight gain in the second trimester, weight gain in the third trimester, birth weight and gestational week between the two groups (P<0.05). ②Single factor analysis showed that pre-pregnancy BMI, gestational week at first diagnosis, fasting blood glucose tolerance, weight gain in the second trimester, weight gain in the third trimester, gestational week and birth weight were the influencing factors of macrosomia (P<0.05). ③Multi-factor analysis showed that gestational weeks, gestational diabetes, fasting glucose tolerance and weight growth in the second trimester were the main factors affecting the production of macrosomia, among which gestational diabetes was the protective factor, while gestational weeks, fasting glucose tolerance and weight growth in the second trimester were the risk factors. Conclusion: The high risk factors for macrosomia are gestational weeks, glucose tolerance, fasting blood sugar and weight gain in the second trimester of pregnancy.We should strengthen regular obstetric examination, health care during pregnancy, reasonable diet and proper exercise, and strictly control the weight gain during the second trimester of pregnancy. At the same time, we should monitor blood sugar in time so as to reduce the incidence of macrosomia.

Effect of maternal pregestational diabetes mellitus on congenital heart diseases

Background The increasing population of diabetes mellitus in adolescent girls and women of childbearing age contributes to a large number of pregnancies with maternal pregestational diabetes mellitus.Congenital heart diseases are a common adverse outcome in mothers with pregestational diabetes mellitus.However,there is little systematic information between maternal pregestational diabetes mellitus and congenital heart diseases in the offspring.Data sources Literature selection was performed in PubMed.One hundred and seven papers were cited in our review,includ-ing 36 clinical studies,26 experimental studies,31 reviews,eight meta-analysis articles,and six of other types.Results Maternal pregestational diabetes mellitus poses a high risk of congenital heart diseases in the offspring and causes variety of phenotypes of congenital heart diseases.Factors such as persistent maternal hyperglycemia,oxidative stress,polymorphism of uncoupling protein 2,polymorphism of adiponectin gene,Notch 1 pathway,Nkx2.5 disorders,dysregula-tion of the hypoxia-inducible factor 1,and viral etiologies are associated with the occurrence of congenital heart diseases in the offspring of mothers with pregestational diabetes mellitus.Treatment options including blood sugar-reducing,anti-oxidative stress drug supplements and exercise can help to prevent maternal pregestational diabetes mellitus from inducing congenital heart diseases.Conclusions Our review contributes to a better understanding of the association between maternal pregestational diabetes mellitus and congenital heart diseases in the offspring and to a profound thought of the mechanism,preventive and therapeutic measurements of congenital heart diseases caused by maternal pregestational diabetes mellitus.

Feature-based initial population generation for the optimization of job shop problems

A suitable initial value of a good(close to the optimal value) scheduling algorithm may greatly speed up the convergence rate.However,the initial population of current scheduling algorithms is randomly determined.Similar scheduling instances in the production process are not reused rationally.For this reason,we propose a method to generate the initial population of job shop problems.The scheduling model includes static and dynamic knowledge to generate the initial population of the genetic algorithm.The knowledge reflects scheduling constraints and priority rules.A scheduling strategy is implemented by matching and combining the two categories of scheduling knowledge,while the experience of dispatchers is externalized to semantic features.Feature similarity based knowledge matching is utilized to acquire the constraints that are in turn used to optimize the scheduling process.Results show that the proposed approach is feasible and effective for the job shop optimization problem.